aggregate-functions

问题 I am trying to learn how to deal with time series data. I created some fake daily data, tried to aggregate it by week and then plot it: set.seed(123) library(xts) library(ggplot2) date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") date_decision_made <- format(as.Date(date_decision_made), "%Y/%m/%d") property_damages_in_dollars <- rnorm(731,100,10) final_data <- data.frame(date_decision_made, property_damages_in_dollars) y.mon<-aggregate(property_damages_in_dollars

问题 I would like to pivot results from a table into a new structure. So that it can map all the children to the parent product. Current Result Parent_Prod_Num|Child_Prod_Num|Child_Prod_Code|Child_Prod_Name 1|11|a123|a 1|12|b123|ab 1|13|c123|abc Expected Result Parent_Prod_Num|Child_Prod_Num_1| Child_Prod_Code_1|Child_Prod_Name_1| Child_Prod_Num_2| Child_Prod_Code_2|Child_Prod_Name_2| Child_Prod_Num_3| Child_Prod_Code_3|Child_Prod_Name_3 1|11|a123|a|12|b123|ab|13|c123|abc 回答1: For a fixed maximum

问题 I would like to pivot results from a table into a new structure. So that it can map all the children to the parent product. Current Result Parent_Prod_Num|Child_Prod_Num|Child_Prod_Code|Child_Prod_Name 1|11|a123|a 1|12|b123|ab 1|13|c123|abc Expected Result Parent_Prod_Num|Child_Prod_Num_1| Child_Prod_Code_1|Child_Prod_Name_1| Child_Prod_Num_2| Child_Prod_Code_2|Child_Prod_Name_2| Child_Prod_Num_3| Child_Prod_Code_3|Child_Prod_Name_3 1|11|a123|a|12|b123|ab|13|c123|abc 回答1: For a fixed maximum

问题 item_id rate status --------- ----------- ------ 1 12 credit 2 10 credit 3 10 credit 4 20 cash 5 55 credit I have the above table, A user inputs and amount of 25. Now I want to update the status of the rows having credit as status from credit to cash until the sum of rate is 25, so in the above table the top 1 rows having a sum of 22 should get a status of cash. Since the user input is 25, I still have a balance of 3 (25-22), this balance should be deducted from the third row making the third

问题 item_id rate status --------- ----------- ------ 1 12 credit 2 10 credit 3 10 credit 4 20 cash 5 55 credit I have the above table, A user inputs and amount of 25. Now I want to update the status of the rows having credit as status from credit to cash until the sum of rate is 25, so in the above table the top 1 rows having a sum of 22 should get a status of cash. Since the user input is 25, I still have a balance of 3 (25-22), this balance should be deducted from the third row making the third

问题 I'm kind of new to MySQL and I need help. I have a table Invoices and a table Payments . I am having trouble generating a report that will show all invoices that were paid in In Full or a Partial Payment was received by 12/31/2019. One Invoice can be paid by one or more payments (For example a partial payment, such as 25% down, and the remainder payment on completion of work). How can I create SQL query that will select ALL records from Invoices and then for each Invoice select matching

问题 I'm kind of new to MySQL and I need help. I have a table Invoices and a table Payments . I am having trouble generating a report that will show all invoices that were paid in In Full or a Partial Payment was received by 12/31/2019. One Invoice can be paid by one or more payments (For example a partial payment, such as 25% down, and the remainder payment on completion of work). How can I create SQL query that will select ALL records from Invoices and then for each Invoice select matching

问题 Spark API RelationalGroupedDataset has a function agg : @scala.annotation.varargs def agg(expr: Column, exprs: Column*): DataFrame = { toDF((expr +: exprs).map { case typed: TypedColumn[_, _] => typed.withInputType(df.exprEnc, df.logicalPlan.output).expr case c => c.expr }) } Why does it take two separate arguments? Why can't it take just exprs: Column* ? Has someone an implicit function that takes one argument? 回答1: This is to make sure that you specify at least one argument. Pure varargs

问题 Spark API RelationalGroupedDataset has a function agg : @scala.annotation.varargs def agg(expr: Column, exprs: Column*): DataFrame = { toDF((expr +: exprs).map { case typed: TypedColumn[_, _] => typed.withInputType(df.exprEnc, df.logicalPlan.output).expr case c => c.expr }) } Why does it take two separate arguments? Why can't it take just exprs: Column* ? Has someone an implicit function that takes one argument? 回答1: This is to make sure that you specify at least one argument. Pure varargs

问题 I have the following table data: value 1 5 10.5 12 36 I want to map these values into: range avg 0-21 (1 + 5 + 10.5 + 12) / 4 21.001-34 0 34.001-64 36 64 + 0 Basically map each value to ranges and calculate the avg of all values in each range. I tried to do: select case when avggeneral between 0 and 21 then ' 0-21' when avggeneral between 21.00001 and 34 then ' 21-34' when avggeneral between 34.00001 and 64 then ' 34-64' else '64+' end as 'range', AVG(*) as 'avg' from table but this doesn't