aggregate

问题 I have the following scenario in Postgres (I'm using 9.4.1 ). I have a table of this format: create table test( id serial, val numeric not null, created timestamp not null default(current_timestamp), fk integer not null ); What I then have is a threshold numeric field in another table which should be used to label each row of test . For every value which is >= threshold I want to have that record marked as true but if it is true it should reset subsequent counts to 0 at that point, e.g. Data

问题 I am working on a complex logic where I need to redistribute a quantity from one dataset to another dataset. This questions is a continuation of this question In the example below I am introducing several new dimensions. After aggregating and distributing all the quantities I am expecting the same total quantity however I have some differences. See the example below package playground import org.apache.log4j.{Level, Logger} import org.apache.spark.sql.SparkSession import org.apache.spark.sql

问题 I think I am trying to do something that cannot be done. I am trying to create a pivot table, simultaneously doing two pivots by aggregating off two different columns. I have created a much simplified example to make the point more understandable. CREATE TABLE two_aggregate_pivot ( ID INT, category CHAR(1), value INT ) INSERT INTO dbo.two_aggregate_pivot ( ID, category, value ) VALUES (1, 'A', 100), (1, 'B', 97), (1, 'D', NULL), (2, 'A', 86), (2, 'C', 83), (2, 'D', 81) I can pivot to get the

问题 I think I am trying to do something that cannot be done. I am trying to create a pivot table, simultaneously doing two pivots by aggregating off two different columns. I have created a much simplified example to make the point more understandable. CREATE TABLE two_aggregate_pivot ( ID INT, category CHAR(1), value INT ) INSERT INTO dbo.two_aggregate_pivot ( ID, category, value ) VALUES (1, 'A', 100), (1, 'B', 97), (1, 'D', NULL), (2, 'A', 86), (2, 'C', 83), (2, 'D', 81) I can pivot to get the

问题 I think I am trying to do something that cannot be done. I am trying to create a pivot table, simultaneously doing two pivots by aggregating off two different columns. I have created a much simplified example to make the point more understandable. CREATE TABLE two_aggregate_pivot ( ID INT, category CHAR(1), value INT ) INSERT INTO dbo.two_aggregate_pivot ( ID, category, value ) VALUES (1, 'A', 100), (1, 'B', 97), (1, 'D', NULL), (2, 'A', 86), (2, 'C', 83), (2, 'D', 81) I can pivot to get the

问题 I have a dataset where some participants have multiple rows and I need to aggregate the data in a way that every participant has only one row. The dataset contains different variable types (e.g., factors, date, age etc.) I have made a code that works and looks like this: example4 <- SMARTdata_50j_diagc_2016 %>% group_by( Patient_Id ) %>% summarise( Groep = first( Groep ), Ziekenhuis_Nr = first( Ziekenhuis_Nr ), Ziekenhuistype = first( Ziekenhuistype ), aantalDBC = n(), aantalVervolg = sum( as

问题 I currently have a query using coalesce that worked in SQL server,however, it is not working in Amazon Redshift. Is there a way I can more appropriately write this to use in Redshift: coalesce(sum(Score)/nullif(sum(ScorePrem),0),0) as percent 回答1: Consider running the aggregate query as a subquery or CTE, then handle transformation or secondary calculations in an outer main query. WITH agg AS ( SELECT calendar_month_id ,day_of_month ,month_name ,DaysRemaining ,RPTBRANCH ,0 AS TotalGrp ,SUM

问题 I have the following tables: SELECT * FROM trajectories LIMIT 10; user_id | session_id | timestamp | lat | lon | alt ---------+-------------------+------------------------+-----------+------------+------ 11 | 10020071017220238 | 2007-10-18 02:51:38+01 | 37.780927 | 113.677553 | 2160 11 | 10020071017220238 | 2007-10-18 02:51:39+01 | 37.78093 | 113.677627 | 2160 11 | 10020071017220238 | 2007-10-18 02:51:40+01 | 37.780932 | 113.677698 | 2160 11 | 10020071017220238 | 2007-10-18 02:51:41+01 | 37

问题 I have the following data.frame: x <- data.frame(A = c("Y", "Y", "Z", NA), B = c(NA, TRUE, FALSE, TRUE), C = c(TRUE, TRUE, NA, FALSE)) And I need to compute the following table with xtabs : A B C Y 1 2 Z 0 0 <NA> 1 0 I was told to use na.action = NULL, which indeed returns the table I need: xtabs(formula = cbind(B, C) ~ A, data = x, addNA = TRUE, na.action = NULL) A B C Y 1 2 Z 0 0 <NA> 1 0 However, na.action = na.pass returns a different table: xtabs(formula = cbind(B, C) ~ A, data = x,

问题 I have the following data.frame: x <- data.frame(A = c("Y", "Y", "Z", NA), B = c(NA, TRUE, FALSE, TRUE), C = c(TRUE, TRUE, NA, FALSE)) And I need to compute the following table with xtabs : A B C Y 1 2 Z 0 0 <NA> 1 0 I was told to use na.action = NULL, which indeed returns the table I need: xtabs(formula = cbind(B, C) ~ A, data = x, addNA = TRUE, na.action = NULL) A B C Y 1 2 Z 0 0 <NA> 1 0 However, na.action = na.pass returns a different table: xtabs(formula = cbind(B, C) ~ A, data = x,