检索 binned vega (lite) 直方图的基础值?
Retrieve underlying values of a binned vega (lite) histogram?
如何检索和显示用于计算 vega 直方图中分箱柱最高点的值?
在这个 d3 observable notebook 中,我说明了想要实现的目标...单击柱时,我使用柱的基础值更新 selected_text
变量,这些值被计算为柱的最高价.
处理 fold
转换,但在我的例子中,我正在寻找 flatten transform
的逆转换。
这是我正在寻找的数据转换的图示。来自:
[
{"name": "alpha", "data": 123, "bin0": 1, "bin1": 2},
{"name": "alpha", "data": 789, "bin0": 2, "bin1": 3},
{"name": "beta", "data": 456, "bin0": 2, "bin1": 3},
{"name": "beta", "data": 789, "bin0": 3, "bin1": 4},
{"name": "beta", "data": 0AB, "bin0": 3, "bin1": 4}
]
到
[
"bin0": 1, "bin1": 2, values: [{"name": "alpha", "data": 123}]
"bin0": 2, "bin1": 3, values: [{"name": "alpha", "data": 789},
{"name": "beta", "data": 456}]
"bin0": 3, "bin1": 4, values: [{"name": "beta", "data": 789},
{"name": "beta", "data": 0AB}]
]
我认为如果 concat
表达式可用于 aggregation transforms
的 groupby operator
则可以实现,但事实并非如此。
"unflatten" 转换可以使用 values
聚合的聚合转换大致实现:
{
"data": {
"values": [
{"name": "alpha", "data": 123, "bin0": 1, "bin1": 2},
{"name": "alpha", "data": 789, "bin0": 2, "bin1": 3},
{"name": "beta", "data": 456, "bin0": 2, "bin1": 3},
{"name": "beta", "data": 789, "bin0": 3, "bin1": 4},
{"name": "beta", "data": 789, "bin0": 3, "bin1": 4}
]
},
"transform": [
{
"aggregate": [{"op": "values", "as": "values"}],
"groupby": ["bin0", "bin1"]
}
],
"mark": "point"
}
在Vega Editor的数据查看器中可以看到转换后的数据为:
[
{"bin0": 1, "bin1": 2,
"values": [{"name": "alpha", "data": 123, "bin0": 1, "bin1": 2}]},
{"bin0": 2, "bin1": 3,
"values": [{"name": "alpha", "data": 789, "bin0": 2, "bin1": 3},
{"name": "beta", "data": 456, "bin0": 2, "bin1": 3}]},
{"bin0": 3, "bin1": 4,
"values": [{"name": "beta", "data": 789, "bin0": 3, "bin1": 4},
{"name": "beta", "data": 0AB, "bin0": 3, "bin1": 4}]}
]
鉴于 Jake 的回答,这里是 complete example 在 Vega 中我的目标。
{
"$schema": "https://vega.github.io/schema/vega/v5.json",
"description": "A histogram of film ratings, modified to include null values.",
"width": 400,
"height": 200,
"padding": 5,
"autosize": {"type": "fit", "resize": true},
"signals": [
{"name": "binCount", "update": "(bins.stop - bins.start) / bins.step"},
{"name": "barStep", "update": "(width ) / (1 + binCount)"},
{
"name": "tooltip",
"value": {},
"on": [
{"events": "rect:mouseover", "update": "slice(datum.dlist,0,2)"},
{"events": "rect:mouseout", "update": "{}"}
]
}
],
"data": [
{
"name": "table",
"values": [
{"name": "alpha", "data": "123"},
{"name": "alpha", "data": "789"},
{"name": "beta", "data": "456"},
{"name": "beta", "data": "789"},
{"name": "gamma", "data": "789"},
{"name": "beta", "data": "300"}
],
"transform": [
{"type": "extent", "field": "data", "signal": "extent"},
{
"type": "bin",
"signal": "bins",
"field": "data",
"extent": {"signal": "extent"},
"maxbins": 4
}
]
},
{
"name": "aggregat",
"source": "table",
"transform": [
{
"type": "aggregate",
"groupby": ["bin0", "bin1"],
"ops": ["values"],
"fields": ["undefined"],
"as": ["values"]
}
]
},
{
"name": "counts",
"source": "table",
"transform": [{"type": "aggregate", "groupby": ["bin0", "bin1"]},
{
"type": "lookup",
"from": "aggregat",
"key": "bin0",
"fields": ["bin0"],
"values": ["values"],
"as": ["dlist"]
}]
}
],
"scales": [
{
"name": "yscale",
"type": "linear",
"range": "height",
"round": true,
"nice": true,
"domain": {"fields": [{"data": "counts", "field": "count"}]}
},
{
"name": "xscale",
"type": "linear",
"range": [{"signal": "barStep "}, {"signal": "width"}],
"round": true,
"domain": {"signal": "[bins.start, bins.stop]"},
"bins": {"signal": "bins"}
}
],
"axes": [
{"orient": "bottom", "scale": "xscale", "tickMinStep": 0.5},
{"orient": "left", "scale": "yscale", "tickCount": 5, "offset": 5}
],
"marks": [
{
"type": "rect",
"from": {"data": "counts"},
"encode": {
"update": {
"tooltip" : { "signal": "tooltip"},
"x": {"scale": "xscale", "field": "bin0", "offset": 1},
"x2": {"scale": "xscale", "field": "bin1"},
"y": {"scale": "yscale", "field": "count"},
"y2": {"scale": "yscale", "value": 0},
"fill": {"value": "steelblue"}
},
"hover": {"fill": {"value": "firebrick"}}
}
}
]
}
如何检索和显示用于计算 vega 直方图中分箱柱最高点的值?
在这个 d3 observable notebook 中,我说明了想要实现的目标...单击柱时,我使用柱的基础值更新 selected_text
变量,这些值被计算为柱的最高价.
fold
转换,但在我的例子中,我正在寻找 flatten transform
的逆转换。
这是我正在寻找的数据转换的图示。来自:
[
{"name": "alpha", "data": 123, "bin0": 1, "bin1": 2},
{"name": "alpha", "data": 789, "bin0": 2, "bin1": 3},
{"name": "beta", "data": 456, "bin0": 2, "bin1": 3},
{"name": "beta", "data": 789, "bin0": 3, "bin1": 4},
{"name": "beta", "data": 0AB, "bin0": 3, "bin1": 4}
]
到
[
"bin0": 1, "bin1": 2, values: [{"name": "alpha", "data": 123}]
"bin0": 2, "bin1": 3, values: [{"name": "alpha", "data": 789},
{"name": "beta", "data": 456}]
"bin0": 3, "bin1": 4, values: [{"name": "beta", "data": 789},
{"name": "beta", "data": 0AB}]
]
我认为如果 concat
表达式可用于 aggregation transforms
的 groupby operator
则可以实现,但事实并非如此。
"unflatten" 转换可以使用 values
聚合的聚合转换大致实现:
{
"data": {
"values": [
{"name": "alpha", "data": 123, "bin0": 1, "bin1": 2},
{"name": "alpha", "data": 789, "bin0": 2, "bin1": 3},
{"name": "beta", "data": 456, "bin0": 2, "bin1": 3},
{"name": "beta", "data": 789, "bin0": 3, "bin1": 4},
{"name": "beta", "data": 789, "bin0": 3, "bin1": 4}
]
},
"transform": [
{
"aggregate": [{"op": "values", "as": "values"}],
"groupby": ["bin0", "bin1"]
}
],
"mark": "point"
}
在Vega Editor的数据查看器中可以看到转换后的数据为:
[
{"bin0": 1, "bin1": 2,
"values": [{"name": "alpha", "data": 123, "bin0": 1, "bin1": 2}]},
{"bin0": 2, "bin1": 3,
"values": [{"name": "alpha", "data": 789, "bin0": 2, "bin1": 3},
{"name": "beta", "data": 456, "bin0": 2, "bin1": 3}]},
{"bin0": 3, "bin1": 4,
"values": [{"name": "beta", "data": 789, "bin0": 3, "bin1": 4},
{"name": "beta", "data": 0AB, "bin0": 3, "bin1": 4}]}
]
鉴于 Jake 的回答,这里是 complete example 在 Vega 中我的目标。
{
"$schema": "https://vega.github.io/schema/vega/v5.json",
"description": "A histogram of film ratings, modified to include null values.",
"width": 400,
"height": 200,
"padding": 5,
"autosize": {"type": "fit", "resize": true},
"signals": [
{"name": "binCount", "update": "(bins.stop - bins.start) / bins.step"},
{"name": "barStep", "update": "(width ) / (1 + binCount)"},
{
"name": "tooltip",
"value": {},
"on": [
{"events": "rect:mouseover", "update": "slice(datum.dlist,0,2)"},
{"events": "rect:mouseout", "update": "{}"}
]
}
],
"data": [
{
"name": "table",
"values": [
{"name": "alpha", "data": "123"},
{"name": "alpha", "data": "789"},
{"name": "beta", "data": "456"},
{"name": "beta", "data": "789"},
{"name": "gamma", "data": "789"},
{"name": "beta", "data": "300"}
],
"transform": [
{"type": "extent", "field": "data", "signal": "extent"},
{
"type": "bin",
"signal": "bins",
"field": "data",
"extent": {"signal": "extent"},
"maxbins": 4
}
]
},
{
"name": "aggregat",
"source": "table",
"transform": [
{
"type": "aggregate",
"groupby": ["bin0", "bin1"],
"ops": ["values"],
"fields": ["undefined"],
"as": ["values"]
}
]
},
{
"name": "counts",
"source": "table",
"transform": [{"type": "aggregate", "groupby": ["bin0", "bin1"]},
{
"type": "lookup",
"from": "aggregat",
"key": "bin0",
"fields": ["bin0"],
"values": ["values"],
"as": ["dlist"]
}]
}
],
"scales": [
{
"name": "yscale",
"type": "linear",
"range": "height",
"round": true,
"nice": true,
"domain": {"fields": [{"data": "counts", "field": "count"}]}
},
{
"name": "xscale",
"type": "linear",
"range": [{"signal": "barStep "}, {"signal": "width"}],
"round": true,
"domain": {"signal": "[bins.start, bins.stop]"},
"bins": {"signal": "bins"}
}
],
"axes": [
{"orient": "bottom", "scale": "xscale", "tickMinStep": 0.5},
{"orient": "left", "scale": "yscale", "tickCount": 5, "offset": 5}
],
"marks": [
{
"type": "rect",
"from": {"data": "counts"},
"encode": {
"update": {
"tooltip" : { "signal": "tooltip"},
"x": {"scale": "xscale", "field": "bin0", "offset": 1},
"x2": {"scale": "xscale", "field": "bin1"},
"y": {"scale": "yscale", "field": "count"},
"y2": {"scale": "yscale", "value": 0},
"fill": {"value": "steelblue"}
},
"hover": {"fill": {"value": "firebrick"}}
}
}
]
}