Running Totals

Running Totals or Cumulative Sums are a powerful way to see not just a trend of data, but also the cumulative results.

For example, if you wanted to monitor your monthly sales, but also make sure you're on track to achieve your annual goal, a running total will sort you out:

Step-by-Step

Running Totals rely on the use of Window Functions, which you can read more about here.  

To find a running total we'll use the SUM window function:

SUM( <expr1> ) OVER ( [ PARTITION BY <expr2> ] [ ORDER BY <expr3> [ ASC | DESC ] [ <window_frame> ] ] )

Where:

Or, in layman's terms:

SUM(column) OVER (PARTITION BY col_to_group_by, ORDER BY column_to_order_by)

In the following example we'll take the running total of hours of Netflix I watched in a given week:

SELECT 
  DATE_TRUNC('WEEK',START_TIME) WEEK,
  DATE_PART('WEEKDAY',START_TIME) DAY, 
  SUM(DURATION)/(60*60) DAILY_HOURS_WATCHED 
FROM 
  PUBLIC.NETFLIX 
WHERE 
  WEEK = '2018-11-26'
GROUP BY WEEK,DAY
ORDER BY WEEK,DAY

Now if I want to find the running total of hours watched that week, I can do the following:

SUM(DAILY_HOURS_WATCHED) OVER (ORDER BY DAY ASC) RUNNING_TOTAL

SELECT DAY, DAILY_HOURS_WATCHED, SUM(DAILY_HOURS_WATCHED) OVER (ORDER BY DAY ASC) RUNNING_TOTAL FROM ONE_WEEK

We can see our new RUNNING_TOTAL column increase each day.

This example had no partition, but if we wanted to compare my viewing habits across two different weeks, we would need to PARTITION BY week:

SELECT 
  DATE_TRUNC('WEEK',START_TIME) WEEK,
  DATE_PART('WEEKDAY',START_TIME) DAY, 
  SUM(DURATION)/(60*60) DAILY_HOURS_WATCHED 
FROM 
  PUBLIC.NETFLIX 
WHERE 
  WEEK IN ('2018-11-19','2018-11-26')
GROUP BY WEEK,DAY
ORDER BY WEEK,DAY

And now to find the running total across the two weeks:

SUM(DAILY_HOURS_WATCHED) OVER(PARTITION BY WEEK ORDER BY DAY ASC) WEEKLY_RUNNING_TOTAL

SELECT 
  WEEK, DAY, DAILY_HOURS_WATCHED, SUM(DAILY_HOURS_WATCHED) OVER(PARTITION BY WEEK ORDER BY DAY ASC) WEEKLY_RUNNING_TOTAL
FROM 
  TWO_WEEKS
ORDER BY WEEK,DAY

Now we can see the RUNNING_TOTAL restart for the 2nd week.

How We Built This

This page was built using Count. It combines the best features of a SQL IDE, Data Visualization Tool, and Computational Notebooks. In the Count notebook, each cell acts like a CTE, meaning you can reference any other cell in your queries.

This makes not only for far more readable reports (like this one) but also a much faster and more powerful way to do your analysis, essentially turning your analysis into a connected graph of data frames rather than one-off convoluted queries and CSV files. And with a built-in visualization framework, you won't have to export your data to make your charts. Go from raw data to interactive report in one document.