This example describes how to use the rolling computational functions:
ROLLINGSUM- computes a rolling sum from a window of rows before and after the current row. See ROLLINGSUM Function.
ROLLINGAVERAGE- computes a rolling average from a window of rows before and after the current row. See ROLLINGAVERAGE Function.
ROWNUMBER- computes the row number for each row, as determined by the ordering column. See ROWNUMBER Function.
The following dataset contains sales data over the final quarter of the year.
First, you want to maintain the row information as a separate column. Since data is ordered already by the
Date column, you can use the following:
window value:ROWNUMBER() order:Date
rowIdfor week of quarter.
Now, you want to extract month and week information from the
Date values. Deriving the month value:
derive type:single value:MONTH(Date) as:'Month'
derive type:single value:(1 + FLOOR(((month-1)/3))) as:'QTR'
window value:ROWNUMBER() order:Date group:QTR
WOQ(week of quarter).
Deriving the week-of-month value:
window value:ROWNUMBER() group:Month order:Date
WOM(week of month).
Now, you perform your rolling computations. Compute the running total of sales using the following:
window value: ROLLINGSUM(Sales, -1, 0) order: Date group:QTR
-1parameter is used in the above computation to gather the rolling sum of all rows of data from the current one to the first one. Note that the use of the
QTRcolumn for grouping, which moves the value for the
01/01/2017into its own computational bucket. This may or may not be preferred.
Rename this column
QTD (quarter to-date). Now, generate a similar column to compute the rolling average of weekly sales for the quarter:
window value: ROUND(ROLLINGAVERAGE(Sales, -1, 0)) order: Date group:QTR
ROLLINGAVERAGEfunction can compute fractional values, it is wrapped in the
ROUNDfunction for neatness. Rename this column
When the unnecessary columns are dropped and some reordering is applied, your dataset should look like the following: