如何使用此排序图表在 vega-lite 中仅显示十个结果

Question

我有一个按需要排序的图表，但似乎无法获得排名。我只想显示现有排序的前十名...... 例如，乔治将成为最后一个特工。

未排名

  {
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "data": {
    "name": "data",
    "values": [
      {
        "agent": "Connor",
        "avg_score": "9.0000000000000000",
        "call_count": 1,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.90000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.44",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Lily",
        "avg_score": "8.0000000000000000",
        "call_count": 1,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.80000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.34",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Jordan",
        "avg_score": "7.5000000000000000",
        "call_count": 2,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.75000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.29",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Jane",
        "avg_score": "7.5000000000000000",
        "call_count": 2,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.75000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.29",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Eagle",
        "avg_score": "7.0000000000000000",
        "call_count": 1,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.70000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.24",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Joe",
        "avg_score": "5.0000000000000000",
        "call_count": 2,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.50000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.04",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Evan",
        "avg_score": "5.0000000000000000",
        "call_count": 2,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.50000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.04",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "George",
        "avg_score": "5.0000000000000000",
        "call_count": 2,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "1.00000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "0.46",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Margaret",
        "avg_score": "5.0000000000000000",
        "call_count": 1,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "1.00000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "0.46",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Margaret",
        "avg_score": "5.0000000000000000",
        "call_count": 1,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.50000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.04",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Laura",
        "avg_score": "4.0000000000000000",
        "call_count": 1,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.80000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "0.26",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Evan",
        "avg_score": "3.5000000000000000",
        "call_count": 2,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.70000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "0.16",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Hunter",
        "avg_score": "3.0000000000000000",
        "call_count": 1,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.30000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "-0.17",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Lily",
        "avg_score": "3.0000000000000000",
        "call_count": 1,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.60000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "0.06",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Jane",
        "avg_score": "3.0000000000000000",
        "call_count": 2,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.60000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "0.06",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Joe",
        "avg_score": "2.5000000000000000",
        "call_count": 2,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.50000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "-0.04",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "George",
        "avg_score": "2.5000000000000000",
        "call_count": 2,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.25000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "-0.22",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Jeff",
        "avg_score": "2.5000000000000000",
        "call_count": 2,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.50000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "-0.04",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Hunter",
        "avg_score": "2.0000000000000000",
        "call_count": 1,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.40000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "-0.14",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Jordan",
        "avg_score": "1.5000000000000000",
        "call_count": 2,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.30000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "-0.24",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Leslie",
        "avg_score": "1.5000000000000000",
        "call_count": 2,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.15000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "-0.32",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Leslie",
        "avg_score": "1.5000000000000000",
        "call_count": 2,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.30000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "-0.24",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Laura",
        "avg_score": "1.00000000000000000000",
        "call_count": 1,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.10000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "-0.37",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Eagle",
        "avg_score": "1.00000000000000000000",
        "call_count": 1,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.20000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "-0.34",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Jeff",
        "avg_score": "1.00000000000000000000",
        "call_count": 2,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.10000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "-0.37",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Connor",
        "avg_score": "0.00000000000000000000",
        "call_count": 1,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.00000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "-0.54",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      }
    ]
  },
  "description": "",
  "encoding": {
    "y": {
      "axis": {
        "labels": true,
        "tickBand": "center",
        "tickOpacity": 0,
        "title": ""
      },
      "field": "agent",
      "sort": {"field": "AgentPercentage"},
      "type": "nominal"
    }
  },
  "height": {"step": 16},
  "layer": [
    {
      "encoding": {
        "tooltip": [
          {"field": "AgentCalls", "title": "calls", "type": "quantitative"},
          {
            "field": "AvgAgtScore",
            "title": "avg score",
            "type": "quantitative"
          },
          {"field": "score_count", "title": "scores", "type": "quantitative"}
        ],
        "x": {
          "aggregate": "average",
          "axis": null,
          "field": "percentage",
          "scale": {"domain": [0, 1]}
        }
      },
      "mark": {"color": "#ddd", "tooltip": true, "type": "bar"},
      "transform": [
        {
          "groupby": ["agent"],
          "joinaggregate": [
            {"as": "AvgAgtScore", "field": "avg_score", "op": "average"}
          ]
        },
        {
          "groupby": ["agent"],
          "joinaggregate": [
            {"as": "AgentCalls", "field": "call_count", "op": "average"}
          ]
        }
      ]
    },
    {
      "encoding": {"text": {"field": "AgentPercentage", "format": ".2%"}},
      "mark": {
        "align": "left",
        "tooltip": "the agent percentage",
        "type": "text",
        "x": 5
      },
      "transform": [
        {
          "groupby": ["agent"],
          "joinaggregate": [
            {"as": "AgentPercentage", "field": "percentage", "op": "average"}
          ]
        }
      ]
    },
    {
      "encoding": {
        "color": {
          "condition": {
            "test": "datum['ScorePercentageCalc'] < 0",
            "value": "red"
          },
          "value": "green"
        },
        "text": {
          "field": "ScorePercentageCalc",
          "format": ".2%",
          "type": "quantitative"
        },
        "tooltip": [
          {"field": "score_desc", "title": "description"},
          {
            "field": "ScorePercentage",
            "format": ".2%",
            "title": "avg score",
            "type": "quantitative"
          }
        ]
      },
      "mark": {
        "align": "left",
        "baseline": "middle",
        "dx": 24,
        "fontWeight": "bold",
        "tooltip": "test",
        "type": "text"
      },
      "transform": [
        {
          "groupby": ["agent"],
          "joinaggregate": [
            {
              "as": "ScorePercentageCalc",
              "field": "score_calc",
              "op": "average"
            }
          ]
        },
        {
          "groupby": ["agent"],
          "joinaggregate": [
            {
              "as": "ScorePercentage",
              "field": "score_percentage",
              "op": "average"
            }
          ]
        }
      ]
    }
  ],
  "title": "Bottom Agent Scores",
  "width": 200
}

接受的答案工作得很好，下面是一个 Elixir 实现，它复制了该答案中的 json。我不知道你可以用这种方式组合变换[在 Elixir 中]。

   def create_bottom_agent_score_chart(score_stats) do
    data = Enum.map(score_stats, fn m -> Map.put(m, :score_desc, "diff from avg") end)

    Vl.new(width: 200, height: [step: 16], title: "Bottom Agent Scores", description: "")
    |> Vl.data_from_values(data, name: "data")
    |> Vl.transform(
      aggregate: [
        [field: "percentage", op: :average, as: "AgentPercentage"],
        [field: "call_count", op: :average, as: "AgentCalls"],
        [field: "avg_score", op: :average, as: "AvgAgtScore"],
        [field: "score_calc", op: :average, as: "ScorePercentageCalc"],
        [field: "score_percentage", op: :average, as: "ScorePercentage"],
        [field: "score_desc", op: :max, as: "score_desc"],
        [field: "score_count", op: :sum, as: "Scores"]
      ],
      groupby: ["agent"]
    )
    |> Vl.transform(
      window: [
        [
          op: "rank",
          as: "rank"
        ]
      ],
      sort: [[field: "AgentPercentage", order: "ascending"]]
    )
    |> Vl.transform(filter: "datum.rank < 11")
    |> Vl.encode_field(:y, "agent",
      type: :nominal,
      axis: [labels: true, tick_band: "center", tick_opacity: 0, title: ""],
      # sort: [op: "max", field: "percentage", order: "ascending"],
      sort: [field: "AgentPercentage", order: "ascending"]
    )
    |> Vl.layers([
      Vl.new()
      |> Vl.mark(:bar, tooltip: true, color: "#ddd")
      |> Vl.encode(:tooltip, [
        [field: "AgentCalls", type: :quantitative, title: "calls"],
        [field: "AvgAgtScore", type: :quantitative, title: "avg score"],
        [field: "Scores", type: :quantitative, title: "scores"]
      ])
      |> Vl.encode_field(:x, "AgentPercentage",
        # this agregate here is wierd, since the field is already averaged.
        # but it won't work without it...
        aggregate: :average,
        scale: [domain: [0, 1]],
        axis: nil
      ),
      Vl.new()
      |> Vl.mark(:text, align: "left", x: 5, tooltip: "the agent percentage")
      |> Vl.encode_field(:text, "AgentPercentage", format: ".1%"),
      Vl.new()
      |> Vl.mark(:text,
        align: "left",
        baseline: "middle",
        dx: 24,
        font_weight: :bold,
        tooltip: "test"
      )
      |> Vl.encode(:tooltip, [
        [field: "score_desc", title: "description"],
        [field: "ScorePercentage", type: :quantitative, title: "avg score", format: ".1%"]
      ])
      |> Vl.encode_field(:text, "ScorePercentageCalc",
        type: :quantitative,
        format: ".1%"
      )
      |> Vl.encode(:color,
        value: "green",
        condition: [test: "datum['ScorePercentageCalc'] < 0", value: "red"]
      )
    ])
    |> Vl.to_spec()
    |> Jason.encode!()
  end

Answer 1

我建议一次性完成所有转换。您还复制了值，这就是为什么您的文本更加粗体（值一个放在另一个上）。

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "data": {
    "name": "data",
    "values": [
      {
        "agent": "Connor",
        "avg_score": "9.0000000000000000",
        "call_count": 1,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.90000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.44",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Lily",
        "avg_score": "8.0000000000000000",
        "call_count": 1,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.80000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.34",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Jordan",
        "avg_score": "7.5000000000000000",
        "call_count": 2,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.75000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.29",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Jane",
        "avg_score": "7.5000000000000000",
        "call_count": 2,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.75000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.29",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Eagle",
        "avg_score": "7.0000000000000000",
        "call_count": 1,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.70000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.24",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Joe",
        "avg_score": "5.0000000000000000",
        "call_count": 2,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.50000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.04",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Evan",
        "avg_score": "5.0000000000000000",
        "call_count": 2,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.50000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.04",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "George",
        "avg_score": "5.0000000000000000",
        "call_count": 2,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "1.00000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "0.46",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Margaret",
        "avg_score": "5.0000000000000000",
        "call_count": 1,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "1.00000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "0.46",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Margaret",
        "avg_score": "5.0000000000000000",
        "call_count": 1,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.50000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "0.04",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Laura",
        "avg_score": "4.0000000000000000",
        "call_count": 1,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.80000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "0.26",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Evan",
        "avg_score": "3.5000000000000000",
        "call_count": 2,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.70000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "0.16",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
        "agent": "Hunter",
        "avg_score": "3.0000000000000000",
        "call_count": 1,
        "label": "Negative Words",
        "max_score": 10,
        "percentage": "0.30000000000000000000",
        "score_average": "4.6500000000000000",
        "score_calc": "-0.17",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.46500000000000000000"
      },
      {
        "agent": "Lily",
        "avg_score": "3.0000000000000000",
        "call_count": 1,
        "label": "Answer Time",
        "max_score": 5,
        "percentage": "0.60000000000000000000",
        "score_average": "2.7000000000000000",
        "score_calc": "0.06",
        "score_count": "2",
        "score_desc": "diff from avg",
        "score_percentage": "0.54000000000000000000"
      },
      {
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