- 07 Apr 2023
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# Rolling Functions

- Updated on 07 Apr 2023
- 2 Minutes to read

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## Rolling Functions

Rolling window functions allow you to create visualizations which are "summaries" of your data. For example, applying a mean rolling function to your dataset of individual sales allows you to see trends in how your sales are performing over a set rolling window, for example, 7 days.

In instances when there is a lot of data variability, a rolling function will enable you to use a statistical value to represent the underlying values.

**Note: **Rolling functions can only be used when the x-axis is set to a time or date-time column, as opposed to categorical.

## Setting up a Rolling Function

- Start by expanding the drop down for
**Advanced Analytics** - Specify the
**Rolling Function**you'd like to apply.**Mean**: The average of the values within the specified window.**Sum**: The sum of the values within the specified window.**Std**: This means*Standard Deviation*, which represents the dispersion of the data relative to its mean for the window.**CumSum :**This means*Cumulative Sum*, which shows the cumulative data — it does not require period or min period values as it will calculate in a growing window.

- Specify the number of Periods the function will take into account when calculating the rolling window
**Periods**is the number of time intervals which will be used by the rolling function. The time interval that is used for the period is the length of the**Time Grain**selected in the**Time**dropdown.- For example, if "days" is selected with a setting of 7 for "Periods", then the function will be based on 7 days.

- Specify the
**Min Periods**which should be taken into account when calculating the data point.**Tip**: this should be identical to**Periods**to ensure that every data point is calculated with the same window size.

#### Example

In this example, we are using the Vehicle Sales dataset and asking the question: "How is the sum of daily sales changing relative to the last 7 days of sales?" We have **order date **as our x-axis and the metric we are plotting is the **sum(sales). **

Since our **Time grain **is set to* Day *when, we specify our *Periods* as 7 we are saying "Take the last **7 days** of data and apply the specified function to them to create a new data point."

## Rolling Functions

__Rolling Function Options__:

**Mean**: The average of the values within the specified window.**Sum**: The sum of the values within the specified window.**Std**: This means*Standard Deviation*, which represents the dispersion of the data relative to its mean for the window.**CumSum :**This means*Cumulative Sum*, which shows the cumulative data — it does not require period or min period values as it will calculate in a growing window.

## Example

This case represents the number of new cases reported per day per country for the top 10 countries. New cases per day fluctuate day-by-day and, consequently, it is very difficult to see trends.

Rolling functions support a better representation of data by representing the moving average in a 10-day period. This facilitates the easier identification of trends.

## Cumulative Data Over a Period of Time

With rolling windows, it is also possible to look at cumulative data over time.