使用 Power Bi 中的 Deneb 在散点图的点周围添加矩形

当绘制像这样的散点图 + 围绕应该分组在一起的点的矩形时，Deneb（Vega-Lite）规范会是什么样的？

以下代码创建散点图，但我不确定如何创建矩形。

{
  "data": {
    "values": [
      {"Average": 6.01,"Groups": "Group1","Index": 6,"Date": "2023-09-16"},
      {"Average": 13.21,"Groups": "Group1","Index": 8,"Date": "2023-11-04"},
      {"Average": 3.63,"Groups": "Group1","Index": 8,"Date": "2023-12-23"},
      {"Average": 5.91,"Groups": "Group1","Index": 7,"Date": "2024-02-10"},
      {"Average": 6.19,"Groups": "Group1","Index": 10,"Date": "2024-03-30"},
      {"Average": 3.97,"Groups": "Group1","Index": 10,"Date": "2024-05-18"},
      {"Average": -1.52,"Groups": "Group1","Index": 10,"Date": "2024-07-06"},
      {"Average": 1.28,"Groups": "Group1","Index": 10,"Date": "2024-08-24"},
      {"Average": 3.39,"Groups": "Group1","Index": 10,"Date": "2024-10-15"},
      {"Average": 1.21,"Groups": "Group1","Index": 23,"Date": "2024-12-03"},
      {"Average": -0.13,"Groups": "Group1","Index": 15,"Date": "2025-01-21"},
      {"Average": 4.49,"Groups": "Group1","Index": 16,"Date": "2025-03-11"},
      {"Average": 34.97,"Groups": "Group2","Index": 6,"Date": "2023-09-16"},
      {"Average": 25.14,"Groups": "Group2","Index": 8,"Date": "2023-11-04"},
      {"Average": 27.59,"Groups": "Group2","Index": 8,"Date": "2023-12-23"},
      {"Average": 27.2,"Groups": "Group2","Index": 7,"Date": "2024-02-10"},
      {"Average": 23.91,"Groups": "Group2","Index": 10,"Date": "2024-03-30"},
      {"Average": 26.29,"Groups": "Group2","Index": 10,"Date": "2024-05-18"},
      {"Average": 26.43,"Groups": "Group2","Index": 10,"Date": "2024-07-06"},
      {"Average": 25.21,"Groups": "Group2","Index": 10,"Date": "2024-08-24"},
      {"Average": 25.51,"Groups": "Group2","Index": 10,"Date": "2024-10-15"},
      {"Average": 38.46,"Groups": "Group2","Index": 23,"Date": "2024-12-03"},
      {"Average": 46.44,"Groups": "Group2","Index": 15,"Date": "2025-01-21"},
      {"Average": 56.63,"Groups": "Group2","Index": 16,"Date": "2025-03-11"},
      {"Average": 17.39,"Groups": "Group3","Index": 6,"Date": "2023-09-16"},
      {"Average": 9.15,"Groups": "Group3","Index": 8,"Date": "2023-11-04"},
      {"Average": 7.46,"Groups": "Group3","Index": 8,"Date": "2023-12-23"},
      {"Average": 6.62,"Groups": "Group3","Index": 7,"Date": "2024-02-10"},
      {"Average": 4.15,"Groups": "Group3","Index": 10,"Date": "2024-03-30"},
      {"Average": 5.52,"Groups": "Group3","Index": 10,"Date": "2024-05-18"},
      {"Average": 6.08,"Groups": "Group3","Index": 10,"Date": "2024-07-06"},
      {"Average": 5.54,"Groups": "Group3","Index": 10,"Date": "2024-08-24"},
      {"Average": 5.77,"Groups": "Group3","Index": 10,"Date": "2024-10-15"},
      {"Average": 5.23,"Groups": "Group3","Index": 23,"Date": "2024-12-03"},
      {"Average": 4.83,"Groups": "Group3","Index": 15,"Date": "2025-01-21"},
      {"Average": 9.56,"Groups": "Group3","Index": 16,"Date": "2025-03-11"
      }
    ]
  },
  "layer": [
    {
      "mark": {
        "type": "point"
      },
      "encoding": {
        "y": {
          "field": "Average",
          "type": "quantitative",
          "title": null
        },
        "x": {
          "field": "Date",
          "type": "ordinal",
          "title": null
        },
        "color": {
          "field": "Groups",
          "type": "nominal"
        },
        "shape": {
          "field": "Groups"
        }
      }
    },
    {
      "mark": {
        "type": "rule",
        "stroke": "black",
        "strokeWidth": 1
      },
      "encoding": {
        "x": {
          "field": "MinDate",
          "type": "ordinal"
        }
      },
      "transform": [
        {
          "aggregate": [
            {
              "op": "min",
              "field": "Date",
              "as": "MinDate"
            }
          ],
          "groupby": ["Index"]
        },
        {
          "calculate": "datum.MinDate - 1",
          "as": "MinDate"
        }
      ]
    },
    {
      "mark": {
        "type": "rule",
        "stroke": "black",
        "strokeWidth": 1
      },
      "encoding": {
        "x": {
          "field": "MaxDate",
          "type": "ordinal"
        }
      },
      "transform": [
        {
          "aggregate": [
            {
              "op": "max",
              "field": "Date",
              "as": "MaxDate"
            }
          ],
          "groupby": ["Index"]
        },
        {
          "calculate": "datum.MaxDate + 1",
          "as": "MaxDate"
        }
      ]
    }
  ]
}

此代码返回以下 plo，并且 x 轴中的日期看起来不太好并且它有一个 NaN。

是否可以从垂直线中移除 x 轴并仅保留点中的 x 轴？

geom_mark_rect在 R 中创建所需的输出。使用 R 包中的函数自动创建矩形ggforce。它基于Index列。

我可以使用 R visual 在 Power BI 中创建这个图，但速度很慢。

require("ggplot2")
library(ggplot2)

require("ggforce")
library(ggforce)

gt_shape <- 1:length(unique(dataset$Groups))
gt_ymax  <- ceiling(max(dataset$Average) + (0.15 * max(dataset$Average)))
gt_ymin  <- floor(min(dataset$Average) - (0.15 * min(dataset$Average)))

ggplot(dataset, aes(x = Date, y = Average, group = as.character(Index))) +
    geom_point(aes(shape = Groups, color = Groups, fill = Groups), stroke = 1, na.rm = TRUE) +
    geom_mark_rect(show.legend = FALSE) +
    theme_minimal() +
    ylim(gt_ymin, gt_ymax) +
    labs(
        y = "",
        x ="Date"
    ) +
    scale_shape_manual(values = gt_shape) +
    scale_colour_viridis_d(option = "turbo") +
    scale_fill_viridis_d(option = "turbo", guide = "none") +
    theme(
        legend.position = "bottom",
        text = element_text(colour = "black"),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)
    )

谢谢。