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社区首页 >问答首页 >需要在Google中创建条件叠加条形图

问需要在Google中创建条件叠加条形图
EN

Stack Overflow用户
提问于 2022-07-01 06:25:40
回答 1查看 336关注 0票数 1

我有SEO数据，需要创建一个堆叠条形图与它。我有登陆页面(URL)，我需要将它们分为4类。

  1. 按点击将着陆页分类为前1-3页；4-10、11-20、21-100相同.
  2. 然后，我需要创建这个视图的堆叠条形图。样本图表附后：

​

需要在X轴上的月份，X轴上的总视图数。

我已经生成了随机数据这里来创建样本图表。希望这就足够了。

data-visualization
visualization
google-data-studio
data-studio-custom-visuals
EN

回答 1

Stack Overflow用户

发布于 2022-07-02 09:21:33

对于分组项的分类，必须进行一些预处理。无法做到这一点。

可能的方法可以在BigQuery，Sheets (枢轴)中完成。Data中的一种方法是通过PERCENTILE与一个自我混合的方法相结合，获得排名(例如，点击次数的前10% )，而不是顶级访问站点。

由于您添加了标签“自定义可视化”，这里有一个方法来做它的织女星插件。

首先将社区可视化"Vega/Vega“添加到您的报告中。

将date和Landing page作为维度添加，而指标是单击的总和。

​

​

在style选项卡中添加以下Vega代码：

代码语言：javascript
运行
复制
{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",

  "data": 
    {
      "values": [
{"$dimension0": "Jan 18, 2022", "$dimension1": "abs", "$metric0": 3},
{"$dimension0": "Jan 19, 2022", "$dimension1": "jsofj", "$metric0": 1},
{"$dimension0": "Jan 20, 2022", "$dimension1": "hfkh", "$metric0": 5},
{"$dimension0": "Jan 21, 2022", "$dimension1": "iefi", "$metric0": 7},
{"$dimension0": "Jan 21, 2022", "$dimension1": "eil", "$metric0": 2},
{"$dimension0": "Jan 18, 2022", "$dimension1": "vnwso", "$metric0": 5},
{"$dimension0": "Jan 24, 2022", "$dimension1": "ojjvl", "$metric0": 6},
{"$dimension0": "Jan 31, 2022", "$dimension1": "vgoe", "$metric0": 8},
{"$dimension0": "Jan 26, 2022", "$dimension1": "abs", "$metric0": 1},
{"$dimension0": "Jan 27, 2022", "$dimension1": "jsofj", "$metric0": 6},
{"$dimension0": "Jan 18, 2022", "$dimension1": "hfkh", "$metric0": 9},
{"$dimension0": "Jan 19, 2022", "$dimension1": "iefi", "$metric0": 4},
{"$dimension0": "Jan 21, 2022", "$dimension1": "eil", "$metric0": 3},
{"$dimension0": "Jan 31, 2022", "$dimension1": "vnwso", "$metric0": 2},
{"$dimension0": "Feb 1, 2022", "$dimension1": "ojjvl", "$metric0": 5},
{"$dimension0": "Feb 2, 2022", "$dimension1": "olwoe", "$metric0": 4},
{"$dimension0": "Feb 3, 2022", "$dimension1": "vgoe", "$metric0": 7},
{"$dimension0": "Feb 4, 2022", "$dimension1": "abs", "$metric0": 9},
{"$dimension0": "Feb 5, 2022", "$dimension1": "jsofj", "$metric0": 3},
{"$dimension0": "Jan 21, 2022", "$dimension1": "hfkh", "$metric0": 1},
{"$dimension0": "Jan 21, 2022", "$dimension1": "iefi", "$metric0": 2},
{"$dimension0": "Feb 8, 2022", "$dimension1": "eil", "$metric0": 5},
{"$dimension0": "Jan 20, 2022", "$dimension1": "vnwso", "$metric0": 8},
{"$dimension0": "Feb 10, 2022", "$dimension1": "ojjvl", "$metric0": 15},
{"$dimension0": "Jan 18, 2022", "$dimension1": "olwoe", "$metric0": 11},
{"$dimension0": "Feb 12, 2022", "$dimension1": "vgoe", "$metric0": 16},
{"$dimension0": "Jan 31, 2022", "$dimension1": "abs", "$metric0": 12},
{"$dimension0": "Jan 19, 2022", "$dimension1": "jsofj", "$metric0": 13},
{"$dimension0": "Feb 15, 2022", "$dimension1": "hfkh", "$metric0": 14},
{"$dimension0": "Feb 16, 2022", "$dimension1": "iefi", "$metric0": 10},
{"$dimension0": "Feb 17, 2022", "$dimension1": "eil", "$metric0": 5},
{"$dimension0": "Jan 31, 2022", "$dimension1": "jsofj", "$metric0": 8},
{"$dimension0": "Jan 18, 2022", "$dimension1": "hfkh", "$metric0": 7},
{"$dimension0": "Jan 21, 2022", "$dimension1": "iefi", "$metric0": 6},
{"$dimension0": "Jan 21, 2022", "$dimension1": "eil", "$metric0": 3},
{"$dimension0": "Jan 19, 2022", "$dimension1": "shf", "$metric0": 4},
{"$dimension0": "Jan 21, 2022", "$dimension1": "jildo", "$metric0": 3},
{"$dimension0": "Jan 31, 2022", "$dimension1": "kahif", "$metric0": 2},
{"$dimension0": "Feb 1, 2022", "$dimension1": "ikhqfl", "$metric0": 5},
{"$dimension0": "Feb 2, 2022", "$dimension1": "hqifkql", "$metric0": 4},
{"$dimension0": "Jan 19, 2022", "$dimension1": "guwk", "$metric0": 4},
{"$dimension0": "Jan 21, 2022", "$dimension1": "qfhuw", "$metric0": 3},
{"$dimension0": "Jan 31, 2022", "$dimension1": "ihqo3", "$metric0": 2},
{"$dimension0": "Feb 1, 2022", "$dimension1": "wfoj", "$metric0": 5}
      ]
    },
       "transform": [
 {
"joinaggregate": [{"op":"sum","field":"$metric0","as":"counts"}],"groupby": ["$dimension1"]
},
{
"sort":[{"field":"counts","order": "descending"},{"field":"$dimension1","order": "descending"}],
"window":[{"op": "row_number","as": "rownum"},{
      "op": "lead","field":"$dimension1","as": "last"}]
},
{
"calculate": " datum.last == datum.$dimension1 ? 0 : 1 ", "as": "adding"
},
{
"sort":[{"field":"rownum","order": "ascending"}],
"window":[{"op":"sum","field":"adding","as": "TOPs"}]
},

{
"calculate": " datum.TOPs<3 ? 'Top 1-3' : datum.TOPs<10 ? 'Top 4-10' : datum.TOPs<20 ? 'Top 11-20'  : 'Top 21-100'  ", "as": "TOPid"
} 

]

,
"mark": {"type": "bar"},
"encoding": {
"x": {
"field": "$dimension0",
"type": "ordinal",
"title": "$dimension0.name"},
"y": {
"field": "$metric0",
"type": "quantitative",
"axis": {"title": "$metric0.name"},
"aggregate": "sum"
},
"color":{
"field":  "TOPid",
"scale": {
"domain": ["Top 1-3", "Top 4-10", "Top 11-20", "Top 20-100"],
"range": ["blue", "lightblue", "#00a", "yellow"]
 }
} ,
"order": { "field": "TOPs"}
}
}

它还可以在：https://vega.github.io/editor/#/edited下查看和开发。

如果您需要按日期计算的顶级组，而不是全局的，请使用此版本。

代码语言：javascript
运行
复制
{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",

  "data": 
    {
      "values": [
{"$dimension0": "Jan 18, 2022", "$dimension1": "abs", "$metric0": 3},
{"$dimension0": "Jan 19, 2022", "$dimension1": "jsofj", "$metric0": 1},
{"$dimension0": "Jan 20, 2022", "$dimension1": "hfkh", "$metric0": 5},
{"$dimension0": "Jan 21, 2022", "$dimension1": "iefi", "$metric0": 7},
{"$dimension0": "Jan 21, 2022", "$dimension1": "eil", "$metric0": 2},
{"$dimension0": "Jan 18, 2022", "$dimension1": "vnwso", "$metric0": 5},
{"$dimension0": "Jan 24, 2022", "$dimension1": "ojjvl", "$metric0": 6},
{"$dimension0": "Jan 31, 2022", "$dimension1": "vgoe", "$metric0": 8},
{"$dimension0": "Jan 26, 2022", "$dimension1": "abs", "$metric0": 1},
{"$dimension0": "Jan 27, 2022", "$dimension1": "jsofj", "$metric0": 6},
{"$dimension0": "Jan 18, 2022", "$dimension1": "hfkh", "$metric0": 9},
{"$dimension0": "Jan 19, 2022", "$dimension1": "iefi", "$metric0": 4},
{"$dimension0": "Jan 21, 2022", "$dimension1": "eil", "$metric0": 3},
{"$dimension0": "Jan 31, 2022", "$dimension1": "vnwso", "$metric0": 2},
{"$dimension0": "Feb 1, 2022", "$dimension1": "ojjvl", "$metric0": 5},
{"$dimension0": "Feb 2, 2022", "$dimension1": "olwoe", "$metric0": 4},
{"$dimension0": "Feb 3, 2022", "$dimension1": "vgoe", "$metric0": 7},
{"$dimension0": "Feb 4, 2022", "$dimension1": "abs", "$metric0": 9},
{"$dimension0": "Feb 5, 2022", "$dimension1": "jsofj", "$metric0": 3},
{"$dimension0": "Jan 21, 2022", "$dimension1": "hfkh", "$metric0": 1},
{"$dimension0": "Jan 21, 2022", "$dimension1": "iefi", "$metric0": 2},
{"$dimension0": "Feb 8, 2022", "$dimension1": "eil", "$metric0": 5},
{"$dimension0": "Jan 20, 2022", "$dimension1": "vnwso", "$metric0": 8},
{"$dimension0": "Feb 10, 2022", "$dimension1": "ojjvl", "$metric0": 15},
{"$dimension0": "Jan 18, 2022", "$dimension1": "olwoe", "$metric0": 11},
{"$dimension0": "Feb 12, 2022", "$dimension1": "vgoe", "$metric0": 16},
{"$dimension0": "Jan 31, 2022", "$dimension1": "abs", "$metric0": 12},
{"$dimension0": "Jan 19, 2022", "$dimension1": "jsofj", "$metric0": 13},
{"$dimension0": "Feb 15, 2022", "$dimension1": "hfkh", "$metric0": 14},
{"$dimension0": "Feb 16, 2022", "$dimension1": "iefi", "$metric0": 10},
{"$dimension0": "Feb 17, 2022", "$dimension1": "eil", "$metric0": 5},
{"$dimension0": "Jan 31, 2022", "$dimension1": "jsofj", "$metric0": 8},
{"$dimension0": "Jan 18, 2022", "$dimension1": "hfkh", "$metric0": 7},
{"$dimension0": "Jan 21, 2022", "$dimension1": "iefi", "$metric0": 6},
{"$dimension0": "Jan 21, 2022", "$dimension1": "eil", "$metric0": 3},
{"$dimension0": "Jan 19, 2022", "$dimension1": "shf", "$metric0": 4},
{"$dimension0": "Jan 21, 2022", "$dimension1": "jildo", "$metric0": 3},
{"$dimension0": "Jan 31, 2022", "$dimension1": "kahif", "$metric0": 2},
{"$dimension0": "Feb 1, 2022", "$dimension1": "ikhqfl", "$metric0": 5},
{"$dimension0": "Feb 2, 2022", "$dimension1": "hqifkql", "$metric0": 4},
{"$dimension0": "Jan 19, 2022", "$dimension1": "guwk", "$metric0": 4},
{"$dimension0": "Jan 21, 2022", "$dimension1": "qfhuw", "$metric0": 3},
{"$dimension0": "Jan 31, 2022", "$dimension1": "ihqo3", "$metric0": 2},
{"$dimension0": "Feb 1, 2022", "$dimension1": "wfoj", "$metric0": 5}
      ]
    },
       "transform": [
 {
"joinaggregate": [{"op":"sum","field":"$metric0","as":"counts"}],"groupby": ["$dimension1","$dimension0"]
},
{
"sort":[{"field":"counts","order": "descending"},{"field":"$dimension1","order": "descending"}],
"window":[{"op": "row_number","as": "rownum"},{
      "op": "lead","field":"$dimension1","as": "last"}],
"groupby": ["$dimension0"]
},
{
"calculate": " datum.last == datum.$dimension1 || datum.rownum==1 ? 0 : 1 ", "as": "adding"
},
{
"sort":[{"field":"rownum","order": "ascending"}],
"window":[{"op":"sum","field":"adding","as": "TOPs"}],
"groupby": ["$dimension0"]
},

{
"calculate": " datum.TOPs<3 ? 'Top 1-3' : datum.TOPs<10 ? 'Top 4-10' : datum.TOPs<20 ? 'Top 11-20'  : 'Top 21-100'  ", "as": "TOPid"
} 

]

,
"mark": {"type": "bar"},
"encoding": {
"x": {
"field": "$dimension0",
"type": "ordinal",
"title": "$dimension0.name"},
"y": {
"field": "$metric0",
"type": "quantitative",
"axis": {"title": "$metric0.name"},
"aggregate": "sum"
},
"color":{
"field":  "TOPid",
"scale": {
"domain": ["Top 1-3", "Top 4-10", "Top 11-20", "Top 20-100"],
"range": ["blue", "lightblue", "#00a", "yellow"]
 }
} ,
"order": { "field": "TOPs"}
}
}

这是Vega Lite代码：

  • “数据”是指有一些样本数据。
  • “转换”完成所有所需的转换
  • “联合聚合”：通过“登陆页”获取所有单击的总和。
  • 接下来，我们需要为这些值生成一个自定义排序TOPs。
  • “排序”/“窗口”：将表从点击次数最多的地方排序到最少的点击次数。然后，“窗口”将“登陆页”更改为前一行。
  • “计算”设置为1，如果“着陆页”与前一行更改
  • “排序”/“窗口”：总结这些值。这是自定义的排名TOPs。
  • “计算”：将自定义排名TOPs划分为顶级组(前1-3、前4-20等等，并将其命名为TOPid )。
  • “标记”：Barplot
  • “编码”：设置桶形图的字段
  • "x"，"y"：x和y轴的定义
  • “颜色”：在这里，TOPid定义了叠加的地块。测试时请使用TOPs
  • “比例”：设置叠加情节的颜色。为了测试，请移除这个。
  • " order "：设置顺序，使用TOPs，因为TOPid是字符串名，因此没有所需的顺序。
票数 1
EN
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：

https://stackoverflow.com/questions/72825156

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