Why time analysis reveals trends in Tableau - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a continuous date axis in Tableau.

Tableau

DATETRUNC('[1]', [Order Date])

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

AWEEK

BDAY

CYEAR

DMONTH

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing DAY or WEEK which creates too granular data for broad trends.

Using YEAR in DATETRUNC groups dates by year, which helps reveal yearly trends.

2fill in blank

medium

Complete the calculation to find the moving average over 3 months.

Tableau

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

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A1

B3

C4

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using -3 which covers 4 months, or -1 which covers 2 months.

The window starts 2 rows before the current row and ends at the current row, covering 3 months total.

3fill in blank

hard

Fix the error in this calculated field to compute year-over-year sales growth.

Tableau

([Sales] - LOOKUP([Sales], -[1])) / LOOKUP([Sales], -[1])

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A1

B12

C6

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 or 3 which compares to previous months, not the same month last year.

Using 12 as the offset compares sales to the same month last year, revealing yearly growth.

4fill in blank

hard

Fill both blanks to create a filter that shows data only for the last 6 months.

Tableau

DATEDIFF('[1]', [2], TODAY()) <= 6

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A[Order Date]

BNOW()

CMONTH

DDAY

Attempts:

3 left

💡 Hint

Common Mistakes

Using NOW() instead of [Order Date], or DAY instead of MONTH.

DATEDIFF calculates the difference in months between [Order Date] and today, filtering last 6 months.

5fill in blank

hard

Fill all three blanks to calculate cumulative sales over time.

Tableau

RUNNING_SUM(SUM([1])) OVER (ORDER BY [2] [3])

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A[Sales]

B[Order Date]

CASC

DDESC

Attempts:

3 left

💡 Hint

Common Mistakes

Ordering DESC which reverses cumulative order, or summing wrong field.

RUNNING_SUM sums sales cumulatively ordered by date ascending to show growth over time.