在 Group By 调用后划分列

Question

来自 R/Dplyr 背景，我在 R 中编写了以下代码：

staff_time2 <- staff_time %>% 
  mutate(Dashboard_Group = "CC", Occupied_Time = HandleTime + AvailableTime) %>% 
  select(Date, Dashboard_Group, HandleTime, Occupied_Time, WorkingTime, LoginTime) %>% 
  mutate(Date = stringi::stri_sub(Date,1, -9), Date = mdy(Date)) %>% 
  filter(Date >= "2020-08-16" & Date <= "2020-08-22") %>% 
  group_by(Dashboard_Group) %>% 
  mutate(BusyTime = sum(HandleTime), OccupiedTime = sum(Occupied_Time), 
         WorkTime = sum(WorkingTime), Login = sum(LoginTime)) %>% 
  ungroup() %>% 
  mutate(Occupancy = BusyTime/OccupiedTime, Utilization = WorkTime/Login ) 

现在我试图在 Google Big Query 上的 SQL 中做同样的事情。 我已经通过两个单独的查询完成了它：

查询 1：

SELECT  
'Contact Center' AS Dashboard_Group, 
sum((HandleTime + AvailableTime)) AS Occupied_Time,
sum(WorkingTime) AS Working_Time, 
sum(LoginTime) AS Login,
sum(HandleTime) AS BusyTime,
FROM `DATABASE.tblStaffTime`
WHERE Date BETWEEN "2020-08-16" AND "2020-08-22"
GROUP BY Dashboard_Group;

查询 2：

SELECT 
(BusyTime/Occupied_Time) AS Occupancy,
(Working_Time/Login) AS Utilization,
FROM `DATABASE.occupancy_and_utilization_1`;

查询 2 只是从查询 1 中获取结果并划分其中的两列。 以下是查询 1 的结果：

我只想将BusyTime按Occupied_Time和Working_Time按Login划分。 如何将这些合并为一个查询？

Answer 1

我只想将BusyTime按Occupied_Time和Working_Time按Login划分。

您可以重复sum() s（或使用子查询或 CTE）。 考虑：

SELECT  
    'Contact Center' AS Dashboard_Group, 
    sum(HandleTime + AvailableTime) AS Occupied_Time,
    sum(WorkingTime) AS Working_Time, 
    sum(LoginTime) AS Login,
    sum(HandleTime) AS BusyTime,
    sum(HandleTime) / sum(HandleTime + AvailableTime) as Occupancy,
    sum(WorkingTime) / sum(LoginTime) as Utilization
FROM `DATABASE.tblStaffTime`
WHERE Date BETWEEN '2020-08-16' AND '2020-08-22'
GROUP BY Dashboard_Group;