Rolling period calculations in Tableau - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate a 3-month rolling sum of Sales.

Tableau

WINDOW_SUM(SUM([Sales]), -[1], 0)

Drag options to blanks, or click blank then click option'

A2

B1

C3

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 3 instead of 2 for the offset

Using positive offsets which look forward instead of backward

The WINDOW_SUM function sums values over a window. To get a 3-month rolling sum including the current month and two previous months, use -2 as the start offset.

2fill in blank

medium

Complete the code to calculate a 7-day rolling average of Profit.

Tableau

WINDOW_AVG(SUM([Profit]), -[1], 0)

Drag options to blanks, or click blank then click option'

A1

B7

C6

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using 7 instead of 6 for the offset

Using positive offsets

For a 7-day rolling average including today and 6 previous days, the window starts at -6.

3fill in blank

hard

Fix the error in the rolling calculation to correctly compute a 12-month rolling sum of Quantity.

Tableau

WINDOW_SUM(SUM([Quantity]), [1], 0)

Drag options to blanks, or click blank then click option'

A11

B-11

C-12

D12

Attempts:

3 left

💡 Hint

Common Mistakes

Using positive offsets

Using -12 which excludes current month

The window should start 11 months before the current month (offset -11) to include 12 months total (current plus 11 previous).

4fill in blank

hard

Fill both blanks to calculate a 4-week rolling average of Sales with correct window offsets.

Tableau

WINDOW_AVG(SUM([Sales]), [1], [2])

Drag options to blanks, or click blank then click option'

A-3

B0

C3

D-4

Attempts:

3 left

💡 Hint

Common Mistakes

Using -4 which excludes current week

Using positive offsets

The window starts 3 weeks before (offset -3) and ends at the current week (offset 0) to cover 4 weeks total.

5fill in blank

hard

Fill all three blanks to calculate a 5-day rolling sum of Profit with correct window offsets and aggregation.

Tableau

[1](SUM([Profit]), [2], [3])

Drag options to blanks, or click blank then click option'

AWINDOW_SUM

B-4

C0

DWINDOW_AVG

Attempts:

3 left

💡 Hint

Common Mistakes

Using WINDOW_AVG instead of WINDOW_SUM

Incorrect offsets that exclude days

Use WINDOW_SUM to sum over the window starting 4 days before (offset -4) and ending today (offset 0) for a 5-day rolling sum.

Practice

1. What is the main purpose of a rolling period calculation in Tableau?

easy

A. To filter data based on a fixed date range

B. To calculate values over a moving window of past data points

C. To create a static total of all data points

D. To sort data alphabetically

Solution

1. Step 1: Understand rolling period concept

   Rolling period calculations summarize data over a moving window, like last 3 months.

2. Step 2: Identify purpose in Tableau

   Tableau uses rolling calculations to track recent trends dynamically, not fixed totals or sorting.

3. Final Answer:

   To calculate values over a moving window of past data points -> Option B

4. Quick Check:

   Rolling calculation = moving window summary [OK]

Hint: Rolling means moving window over recent data points [OK]

Common Mistakes:

• Confusing rolling with fixed totals
• Thinking rolling sorts data
• Assuming rolling filters data

2. Which Tableau function is used to calculate a rolling sum over a window of data?

easy

A. RUNNING_SUM()

B. SUM()

C. WINDOW_SUM()

D. TOTAL()

Solution

1. Step 1: Identify rolling sum function

   WINDOW_SUM() calculates sum over a specified window, perfect for rolling sums.

2. Step 2: Differentiate from similar functions

   SUM() totals all data, TOTAL() sums entire partition, RUNNING_SUM() accumulates from start to current row, not a fixed window.

3. Final Answer:

   WINDOW_SUM() -> Option C

4. Quick Check:

   Rolling sum = WINDOW_SUM() [OK]

Hint: Use WINDOW_SUM() for sums over moving windows [OK]

Common Mistakes:

• Using SUM() which sums all data
• Confusing RUNNING_SUM() with rolling window
• Using TOTAL() which sums entire partition

3. Given the Tableau calculation WINDOW_AVG(SUM([Sales]), -2, 0), what does it compute?

medium

A. Average of sales for the current and previous two rows

B. Average of sales for the next two rows only

C. Sum of sales for all rows

D. Average of sales for the current row only

Solution

1. Step 1: Analyze WINDOW_AVG parameters

   WINDOW_AVG computes average over a window; -2 to 0 means from two rows before to current row.

2. Step 2: Understand SUM inside WINDOW_AVG

   SUM([Sales]) aggregates sales per row, then WINDOW_AVG averages over the window of 3 rows.

3. Final Answer:

   Average of sales for the current and previous two rows -> Option A

4. Quick Check:

   Window from -2 to 0 = current + 2 previous rows [OK]

Hint: Negative start index means look back rows [OK]

Common Mistakes:

• Thinking window looks forward only
• Confusing sum and average
• Ignoring window range parameters

4. You wrote this Tableau calculation for a 3-month rolling sum: WINDOW_SUM(SUM([Profit]), 0, 2). The results seem incorrect. What is the likely issue?

medium

A. SUM() cannot be used inside WINDOW_SUM()

B. The calculation needs to use RUNNING_SUM() instead

C. WINDOW_SUM() requires negative indices only

D. The window range is forward-looking, not backward-looking

Solution

1. Step 1: Check window range meaning

   Range 0 to 2 means current row and next two rows, which looks forward, not backward.

2. Step 2: Understand rolling sum intent

   Rolling sums usually look backward (previous periods), so range should be negative to zero, e.g., -2 to 0.

3. Final Answer:

   The window range is forward-looking, not backward-looking -> Option D

4. Quick Check:

   Rolling sum needs backward window range [OK]

Hint: Rolling sums usually use negative start index [OK]

Common Mistakes:

• Using forward window range for rolling sums
• Thinking SUM() is invalid inside WINDOW_SUM()
• Confusing RUNNING_SUM() with rolling sum

5. You want to create a 6-month rolling average of sales in Tableau, but your data has missing months. Which approach ensures accurate rolling calculations?

hard

A. Use a continuous date axis and fill missing months with zero sales before applying WINDOW_AVG

B. Apply WINDOW_AVG directly on raw data without adjustments

C. Use RUNNING_SUM instead of WINDOW_AVG to ignore missing months

D. Filter out months with missing sales before calculation

Solution

1. Step 1: Understand impact of missing months

   Missing months cause gaps, so rolling averages skip those periods, giving inaccurate results.

2. Step 2: Fill missing months with zero sales

   Using a continuous date axis and filling missing months with zero ensures the rolling window covers all months evenly.

3. Step 3: Apply WINDOW_AVG on adjusted data

   Now WINDOW_AVG calculates correctly over 6 months including zeros for missing months.

4. Final Answer:

   Use a continuous date axis and fill missing months with zero sales before applying WINDOW_AVG -> Option A

5. Quick Check:

   Fill gaps first for accurate rolling average [OK]

Hint: Fill missing dates with zeros before rolling average [OK]

Common Mistakes:

• Ignoring missing months causing wrong averages
• Using RUNNING_SUM which accumulates, not averages
• Filtering out missing months, shrinking window size