在 Group By 調用后划分列

Question

來自 R/Dplyr 背景，我在 R 中編寫了以下代碼：

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 ) 

現在我試圖在 Google Big Query 上的 SQL 中做同樣的事情。 我已經通過兩個單獨的查詢完成了它：

查詢 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;

查詢 2：

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

查詢 2 只是從查詢 1 中獲取結果並划分其中的兩列。 以下是查詢 1 的結果：

我只想將BusyTime按Occupied_Time和Working_Time按Login划分。 如何將這些合並為一個查詢？

Answer 1

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

您可以重復sum() s（或使用子查詢或 CTE）。 考慮：

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;