根据另一个图表中动态更新的轴顺序对图表轴进行排序

如何解决根据另一个图表中动态更新的轴顺序对图表轴进行排序

在 this VegaLite spec 中，最底部条形图的 y 轴顺序更新，因为该图的数据根据​​散点图中的选择进行过滤。在我将同一条形图与另一个图表分层的最顶部条形图中，我如何为蓝色和橙色条形实现相同的排序行为？

我曾尝试在共享和独立之间切换轴并切换层的顺序，但这并没有做到。从概念上讲，我可以想象使用计算转换来定义一个基于选择并用作排序顺序键的新字段，但我不知道如何编写这个 vega 表达式字符串。

这是 altair 代码，如果有人愿意以这种方式解决它：

import altair as alt
import pandas as pd


data={
 'Term': ['algorithm','learning','algorithm','learning'],'Freq_x': [1330,1153,504.42,296.69,177.59,140.35],'Total': [1330,1353,1353.7,1330.47,1353.7],'Category': ['Default','Default','Topic1','Topic2','Topic2'],'logprob': [30.0,27.0,-5.116,-5.1418,-5.4112,-5.5271],'loglift': [30.0,0.0975,0.0891,-0.1803,-0.3135],'saliency_ind': [0,3,76,77,181,186],'x': [None,None,-0.0080,-0.0053,-0.0053],'y': [None,-0.0056,0.0003,0.0003],'topics': [None,1.0,2.0,2.0],'cluster': [None,1.0],'Freq_y': [None,20.39,14.18,14.18]}

df=pd.DataFrame(data)

pts = alt.selection(type="single",fields=['Category'],empty='none')

points = alt.Chart(df).mark_circle().encode(
    x='mean(x)',y='mean(y)',size='Freq_y',detail='Category',color=alt.condition(pts,alt.value('#F28E2B'),alt.value('#4E79A7'))
).add_selection(pts)

bars = alt.Chart(df).mark_bar().encode(
    x='Freq_x',y=alt.Y('Term'),)

bars2 = alt.Chart(df).mark_bar(color='#F28E2B').encode(
    x='Freq_x',y=alt.Y('Term',sort='-x'),).transform_filter(
    pts
)

(points | (bars + bars2) & bars2)

解决方法

您规范的问题在于，在 layers 中，您执行了 filter 转换，为每个层创建了一个单独的 data。排序在层的每一层都有效，但由于两个层 data 是分开的，所以每一层都被独立排序。

因此，我没有使用过滤器转换，而是尝试使用 calculate 转换手动过滤并创建了一个 filtered_freq_x 字段，该字段稍后用于第二层并在窗口上使用它执行排序。因此，我的数据在两个层中变得相同，只添加和使用了几个字段。

如果这对您不起作用，请告诉我。以下是规范配置和 editor：

{
  "config": {"view": {"continuousWidth": 200,"continuousHeight": 300}},"hconcat": [
    {
      "mark": "circle","encoding": {
        "color": {
          "condition": {"value": "#F28E2B","selection": "selector046"},"value": "#4E79A7"
        },"detail": {"type": "nominal","field": "Category"},"size": {"type": "quantitative","field": "Freq_y"},"x": {"type": "quantitative","aggregate": "mean","field": "x"},"y": {"type": "quantitative","field": "y"}
      },"selection": {
        "selector046": {
          "type": "single","fields": ["Category"],"empty": "none"
        }
      }
    },{
      "vconcat": [
        {
          "transform": [
            {
              "joinaggregate": [
                {"field": "Freq_x","op": "max","as": "max_Fx"}
              ]
            },{
              "calculate": "selector046['Category'] ? selector046['Category'] : []","as": "filterCategory"
            },{
              "calculate": "indexof(datum.filterCategory,datum['Category']) > -1 ? datum['Freq_x'] : null","as": "filtered_Freq_x"
            },{
              "sort": [{"field": "filtered_Freq_x","order": "descending"}],"window": [{"op": "rank","as": "Sorted"}]
            }
          ],"height": 50,"layer": [
            {
              "mark": "bar","encoding": {
                "x": {"type": "quantitative","field": "Freq_x"},"y": {
                  "type": "nominal","field": "Term","sort": {"field": "Sorted"}
                }
              }
            },{
              "mark": {"type": "bar","color": "#F28E2B"},"field": "filtered_Freq_x"},"sort": {"field": "Sorted"}
                }
              }
            }
          ]
        },{
          "mark": {"type": "bar","encoding": {
            "x": {"type": "quantitative","y": {"type": "nominal","sort": "-x"}
          },"transform": [{"filter": {"selection": "selector046"}}]
        }
      ]
    }
  ],"data": {"name": "data-d807cd22b94d04d6f1543201cfe5f45e"},"$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json","datasets": {
    "data-d807cd22b94d04d6f1543201cfe5f45e": [
      {
        "Term": "algorithm","Freq_x": 1330,"Total": 1330,"Category": "Default","logprob": 30,"loglift": 30,"saliency_ind": 0,"x": null,"y": null,"topics": null,"cluster": null,"Freq_y": null
      },{
        "Term": "learning","Freq_x": 1153,"Total": 1353,"logprob": 27,"loglift": 27,"saliency_ind": 3,"Freq_x": 504.42,"Total": 1353.7,"Category": "Topic1","logprob": -5.116,"loglift": 0.0975,"saliency_ind": 76,"x": -0.008,"y": -0.0056,"topics": 1,"cluster": 1,"Freq_y": 20.39
      },{
        "Term": "algorithm","Freq_x": 296.69,"Total": 1330.47,"logprob": -5.1418,"loglift": 0.0891,"saliency_ind": 77,"Freq_x": 177.59,"Category": "Topic2","logprob": -5.4112,"loglift": -0.1803,"saliency_ind": 181,"x": -0.0053,"y": 0.0003,"topics": 2,"Freq_y": 14.18
      },"Freq_x": 140.35,"logprob": -5.5271,"loglift": -0.3135,"saliency_ind": 186,"Freq_y": 14.18
      }
    ]
  }
}