Moving average in Tableau - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a moving average in data visualization?

A moving average smooths data by calculating the average of a set number of data points over time. It helps show trends by reducing short-term fluctuations.

beginner

How do you create a moving average in Tableau?

You use a table calculation called WINDOW_AVG() over a measure, specifying the range of data points to average, like the last 3 months.

beginner

Why use a moving average instead of raw data points?

Because it reduces noise and shows clearer trends, making it easier to understand overall patterns in data.

intermediate

What does WINDOW_AVG(SUM([Sales]), -2, 0) mean in Tableau?

It calculates the average of the current and previous two data points of the SUM of Sales, creating a 3-point moving average.

intermediate

What is the difference between a simple moving average and a weighted moving average?

A simple moving average gives equal weight to all points, while a weighted moving average gives more importance to recent points.

What is the main purpose of a moving average in a dashboard?

A To filter out all data except the latest

B To highlight short-term spikes

C To count the number of data points

D To smooth data and show trends

A moving average smooths data to reveal trends by reducing short-term fluctuations.

Which Tableau function is commonly used to calculate a moving average?

A WINDOW_AVG()

B LOOKUP()

C IF()

D SUM()

WINDOW_AVG() calculates the average over a window of data points, perfect for moving averages.

In a 3-point moving average, how many data points are averaged?

A 1

B 2

C 3

D 4

A 3-point moving average averages three data points at a time.

What does the '-2' mean in WINDOW_AVG(SUM([Sales]), -2, 0)?

A Start averaging two points after current

B Start averaging two points before current

C Ignore two points

D Average only current point

The '-2' means the window starts two points before the current point.

Which moving average type gives more importance to recent data?

A Weighted moving average

B Simple moving average

C Cumulative average

D Median average

Weighted moving averages assign more weight to recent data points.

Explain how a moving average helps in understanding sales trends over time.

Think about how averaging helps see the big picture beyond daily ups and downs.

Describe the steps to create a 3-point moving average in Tableau using WINDOW_AVG.

Focus on the function and the window range parameters.

Practice

1. What is the main purpose of using a moving average in Tableau visualizations?

easy

A. To smooth out short-term fluctuations and highlight longer-term trends

B. To count the total number of data points in a dataset

C. To filter out all data except the latest value

D. To create a pie chart from time series data

Solution

1. Step 1: Understand moving average concept

   A moving average calculates the average of data points over a specific number of periods to reduce noise.

2. Step 2: Identify its purpose in visualization

   This smoothing helps reveal underlying trends by minimizing short-term ups and downs.

3. Final Answer:

   To smooth out short-term fluctuations and highlight longer-term trends -> Option A

4. Quick Check:

   Moving average = smoothing trends [OK]

Hint: Moving average smooths data to show trends clearly [OK]

Common Mistakes:

• Confusing moving average with total sum
• Thinking it filters data instead of smoothing
• Assuming it creates categorical charts

2. Which of the following is the correct Tableau calculation syntax to compute a 3-period moving average of SUM(Sales)?

easy

A. WINDOW_AVG(SUM([Sales]), 0, 2)

B. WINDOW_AVG(SUM([Sales]), -1, 1)

C. WINDOW_AVG(SUM([Sales]), -2, 0)

D. WINDOW_AVG(SUM([Sales]), -1, 2)

Solution

1. Step 1: Understand WINDOW_AVG parameters

   WINDOW_AVG(expression, start, end) averages values from start to end relative to current row.

2. Step 2: Define 3-period window around current row

   For 3 periods centered on current row, use -1 (previous), 0 (current), and 1 (next), so range is -1 to 1.

3. Final Answer:

   WINDOW_AVG(SUM([Sales]), -1, 1) -> Option B

4. Quick Check:

   3-period window = -1 to 1 [OK]

Hint: Use negative and positive offsets to set window range [OK]

Common Mistakes:

• Using incorrect window range that doesn't cover 3 periods
• Confusing start and end parameters
• Omitting SUM aggregation inside WINDOW_AVG

3. Given the following data points for Sales over 5 days: [100, 120, 140, 160, 180], what is the 3-day moving average value for day 3 using WINDOW_AVG(SUM([Sales]), -1, 1)?

medium

A. 120

B. 160

C. 130

D. 140

Solution

1. Step 1: Identify the 3-day window for day 3

   Day 3 includes day 2 (120), day 3 (140), and day 4 (160) because window is from -1 to +1 relative to day 3.

2. Step 2: Calculate average of these values

   (120 + 140 + 160) / 3 = 420 / 3 = 140

3. Final Answer:

   140 -> Option D

4. Quick Check:

   Average of 120,140,160 = 140 [OK]

Hint: Average values one before, current, and one after [OK]

Common Mistakes:

• Including wrong days in the window
• Calculating sum instead of average
• Using only previous days without current or next

4. You wrote this Tableau calculation for a 5-day moving average: WINDOW_AVG(SUM([Sales]), -2, 2). However, the moving average is not showing correctly on the first two days. What is the likely issue?

medium

A. The window range includes days outside the data, causing NULLs to affect the average

B. SUM aggregation is missing inside WINDOW_AVG

C. WINDOW_AVG requires only positive offsets for the window

D. The calculation should use WINDOW_SUM instead of WINDOW_AVG

Solution

1. Step 1: Analyze window range impact on edge rows

   Window from -2 to 2 means for first two days, some offsets point to non-existent previous days (before data starts).

2. Step 2: Understand effect of NULLs in window

   These NULLs can cause the average to be incorrect or missing because Tableau includes them in calculation.

3. Final Answer:

   The window range includes days outside the data, causing NULLs to affect the average -> Option A

4. Quick Check:

   Edge rows have incomplete windows causing NULL impact [OK]

Hint: Check window range near data edges for NULLs [OK]

Common Mistakes:

• Assuming WINDOW_AVG ignores NULLs automatically
• Confusing aggregation functions inside WINDOW_AVG
• Thinking window offsets must be positive only

5. You want to create a 7-day moving average of daily sales but only for weekdays (Monday to Friday). Which approach correctly handles this in Tableau?

hard

A. Use WINDOW_AVG(SUM([Sales]), -3, 3) and filter out weekends before calculation

B. Use WINDOW_AVG(SUM([Sales]), -6, 0) ignoring weekends in data

C. Create a calculated field that excludes weekends, then use WINDOW_AVG over consecutive weekdays

D. Apply WINDOW_AVG on all days and manually remove weekend values from the result

Solution

1. Step 1: Understand weekday filtering impact

   Simply filtering weekends after calculation or ignoring them breaks the consecutive window needed for moving average.

2. Step 2: Use calculated field to exclude weekends before averaging

   By creating a field that removes weekends, the WINDOW_AVG function works on consecutive weekdays only, producing accurate 7-day averages.

3. Final Answer:

   Create a calculated field that excludes weekends, then use WINDOW_AVG over consecutive weekdays -> Option C

4. Quick Check:

   Exclude weekends before WINDOW_AVG for correct weekday moving average [OK]

Hint: Filter weekends before applying moving average [OK]

Common Mistakes:

• Applying WINDOW_AVG including weekends causing wrong averages
• Using wrong window size ignoring missing days
• Filtering weekends after calculation instead of before