【问题标题】:Collate language codes into one combined locale code using CASE WHEN, and count the number of times the combined locale code occurs on a date使用 CASE WHEN 将语言代码整理成一个组合语言环境代码,并计算组合语言环境代码在某个日期出现的次数
【发布时间】:2019-05-30 17:27:52
【问题描述】:

了解 CASE WHEN,当我在 Analytics 中看到多个区域设置代码时,我遇到了一个用例。这是一个更简单的问题,比我之前发布的问题更容易回答和阅读。

示例如下: en-us(英语美国) en-au(澳大利亚英语) en-br(英语巴西) es-es(西班牙西班牙) es-419(西班牙拉坦) pt-br(巴西葡萄牙语) pt-pt(葡萄牙)

如何在 BigQuery 中汇总这些值,以便计算仅找到区域设置的前两个字符的次数,而不是计算不同的值?

这个问题的第二部分是:如何构建我的表格,以便我能够按日期绘制这些计数?

目前,输出为: 日期:语言代码:CombinedLocale

示例数据表链接:https://docs.google.com/spreadsheets/d/1XZp1nhNZySWI39kKhb3ydYYIImmrfAMcGJDS6ASThqg/edit?usp=sharing

我试过了:

SELECT date, COUNT(language_code),
CASE 
    WHEN language_code like '%af%' THEN 'AF'
    WHEN language_code like '%en%' THEN 'EN'
    WHEN language_code like '%ar%' THEN 'AR'
    WHEN language_code like '%ba%' THEN 'BA'
ELSE "Others"
END AS CombinedLocale
FROM date_locales

还有:

Select date, COUNT(language_code)
FROM date_locales
WHERE CASE 
WHEN language_code like '%af%' THEN 'AF'
WHEN language_code like '%en%' THEN 'EN'
WHEN language_code like '%ar%' THEN 'AR'
WHEN language_code like '%ba%' THEN 'BA'
ELSE "Others"
END

这是我的工作代码:

SELECT date, language_code,
CASE 
    WHEN language_code like '%af%' THEN 'AF'
    WHEN language_code like '%en%' THEN 'EN'
    WHEN language_code like '%ar%' THEN 'AR'
    WHEN language_code like '%ba%' THEN 'BA'
ELSE "Others"
END AS CombinedLocale
FROM date_locales

我希望结果会随着时间的推移显示 CombinedLocale 表的计数,如下所示:

1 月 AF 3 1 月 5 日 2 月 5 日 2 月 EN 6 三月 EN 2 三月 EN 3

但我收到一条错误消息: SELECT 列表表达式引用既不分组也不聚合的列日期(行:1,列:8)

我认为我需要先将日期汇总到月份?我的印象是 BigQuery 与 DataStudio 的集成会自动聚合日期列。

【问题讨论】:

    标签: sql count google-bigquery case case-when


    【解决方案1】:

    您只是在寻找聚合查询吗?

    SELECT date,
           (CASE WHEN language_code like '%af%' THEN 'AF'
                 WHEN language_code like '%en%' THEN 'EN'
                 WHEN language_code like '%ar%' THEN 'AR'
                 WHEN language_code like '%ba%' THEN 'BA'
                 ELSE 'Others'
            END) AS CombinedLocale,
           COUNT(*)
    FROM date_locales
    GROUP BY date, CombinedLocale;
    

    【讨论】:

    • 啊,看来我只是不知道在哪里放置 COUNT(*) 感谢您的帮助!
    【解决方案2】:

    以下是 BigQuery 标准 SQL 并回答您问题中的两个项目

    #standardSQL
    SELECT 
      FORMAT_DATE('%b %Y', PARSE_DATE('%m/%d/%Y', dt)) month_year, 
      REGEXP_EXTRACT(code, r'(.*?)-') code, 
      COUNT(1) cnt
    FROM `project.dataset.date_locales`
    GROUP BY month_year, code   
    

    您可以使用一些虚拟数据进行测试,如下例所示

    #standardSQL
    WITH `project.dataset.date_locales` AS (
      SELECT '3/14/2019' dt, 'af-ZA' code UNION ALL
      SELECT '3/14/2019', 'am-ET' UNION ALL
      SELECT '5/7/2019', 'ar-AE' UNION ALL
      SELECT '5/19/2019', 'ar-BH' UNION ALL
      SELECT '3/5/2019', 'ar-DZ' UNION ALL
      SELECT '1/1/2019', 'ar-EG' UNION ALL
      SELECT '3/31/2019', 'ar-IQ' UNION ALL
      SELECT '4/20/2019', 'ar-JO' UNION ALL
      SELECT '3/17/2019', 'ar-KW' UNION ALL
      SELECT '1/8/2019', 'ar-LB' UNION ALL
      SELECT '3/26/2019', 'ar-LY' UNION ALL
      SELECT '5/7/2019', 'ar-MA' UNION ALL
      SELECT '3/12/2019', 'arn-CL' UNION ALL
      SELECT '5/19/2019', 'ar-OM' UNION ALL
      SELECT '4/19/2019', 'ar-QA' UNION ALL
      SELECT '4/20/2019', 'ar-SA' UNION ALL
      SELECT '5/22/2019', 'ar-SY' UNION ALL
      SELECT '5/23/2019', 'ar-TN' UNION ALL
      SELECT '3/10/2019', 'ar-YE' UNION ALL
      SELECT '4/6/2019', 'as-IN' UNION ALL
      SELECT '2/5/2019', 'az-Cyrl' UNION ALL
      SELECT '3/1/2019', 'az-Latn' UNION ALL
      SELECT '3/25/2019', 'ba-RU' UNION ALL
      SELECT '1/1/2019', 'be-BY' UNION ALL
      SELECT '2/1/2019', 'bg-BG' UNION ALL
      SELECT '5/3/2019', 'bn-BD' UNION ALL
      SELECT '5/2/2019', 'bn-IN' UNION ALL
      SELECT '3/19/2019', 'bo-CN' UNION ALL
      SELECT '1/19/2019', 'br-FR' 
    )
    SELECT 
      FORMAT_DATE('%b %Y', PARSE_DATE('%m/%d/%Y', dt)) month_year, 
      REGEXP_EXTRACT(code, r'(.*?)-') code, 
      COUNT(1) cnt
    FROM `project.dataset.date_locales`
    GROUP BY month_year, code   
    

    结果为

    Row month_year  code    cnt  
    1   Jan 2019    ar      2    
    2   Mar 2019    ar      5    
    3   Mar 2019    af      1    
    4   Feb 2019    az      1    
    5   Mar 2019    am      1    
    6   Apr 2019    as      1    
    7   May 2019    ar      6    
    8   Mar 2019    ba      1    
    9   May 2019    bn      2    
    10  Feb 2019    bg      1    
    11  Mar 2019    arn     1    
    12  Mar 2019    bo      1    
    13  Mar 2019    az      1    
    14  Jan 2019    br      1    
    15  Apr 2019    ar      3    
    16  Jan 2019    be      1    
    

    【讨论】:

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