Formula Rejection in Google Data Studio
If there is an error in your calculated field’s formula, you’ll see a warning message like WHEN conditions must compare a dimension or metric with a literal value and you won’t be able to save the field:
When this happens it means your formula has been rejected by Data Studio.
If you are new to using calculated fields, you will often see yourself in a situation where your formula has been rejected by the Data Studio.
The most common reasons why your formula is rejected by Data Studio:
#1 You used an invalid field name
If your field name is valid, it will appear as a green or blue chip in the formula:
However, if your field name is not valid, it will not appear as a green or blue chip in the formula.
If you reference a field that does not exist in your data source then it is going to be an invalid field.
Similarly, if your field has got a spelling mistake or some special character (like white space) in it then it is going to be invalid.
And when Data Studio encounters an invalid field in a formula it rejects the formula.
For example in the screenshot below, Data Studio does not recognize ‘Sales’ as a valid field and that’s why you see the error message ‘Unknow dimension or metric id: Sales‘:
#2 You used an invalid function name
If your function name is valid, it will automatically appear as an uppercase green text:
A function that is not spelt correctly or which is not supported by Data Studio is an example of an invalid function.
For example, in the screenshot below the function ‘concatenate’ is not a valid function name:
Therefore it is rejected by Data Studio and you see the error message ‘Unsupported operator: CONCATENATE”.
However, the function ‘CONCAT‘ is valid and that’s why it appears as an uppercase green text:
#3 Missing quotes
Make sure all literal strings are properly quoted with single or double-quotes.
For example, in the formula below the string ‘yes’ is not surrounded by a single or double quote and because of that reason, Data Studio rejected the formula and you see the error message: ‘Invalid formula: Invalid input expression – Invalid filter argument‘:
When the string ‘yes’ is surrounded by a single quote, the formula becomes valid:
#4 Mismatched parentheses
When nesting functions, it is quite easy to miss a closing parenthesis. You need to make sure that you have the same number of opening parentheses as closing parentheses.
For example, in the formula below we are nesting the ‘CONCAT’ function:
However, since the number of opening parentheses is not the same as the closing parentheses, Data Studio rejected the formula and you see the error message: Syntax error: Expected “)”.
The formula below contains the same number of opening parentheses as closing parentheses and is therefore valid:
#5 Not using the correct parameters for your function can cause your formula to be rejected by Data Studio.
#6 Applying an aggregation function on already aggregated data
For example, the ‘Sessions’ metric is already summed in Google Analytics.
So the formula SUM(Sessions) will be rejected by Data Studio and you will see the error message ‘Re-aggregating metrics is not supported‘:
Another example:
#7 Mixing dimensions and metrics in function arguments
An expression can have either metrics or dimensions, but not both.
For example, the formula below is made up of an expression that contains both dimension (the field ‘medium’) and metric (the field ‘Sessions’) and because of that reason, Data Studio rejected the formula and you see the error message: ‘Sorry Calculated fields can’t mix metrics (aggregated values) and dimensions (non-aggregated values). Please check the aggregation types of the fields in this formula.‘:
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#1 Why digital analytics is the key to online business success.
#2 The number 1 reason why most marketers are not able to scale their advertising and maximize sales.
#3 Why Google and Facebook ads don’t work for most businesses & how to make them work.
#4 Why you won’t get any competitive advantage in the marketplace just by knowing Google Analytics.
#5 The number 1 reason why conversion optimization is not working for your business.
#6 How to advertise on any marketing platform for FREE with an unlimited budget.
#7 How to learn and master digital analytics and conversion optimization in record time.
My best selling books on Digital Analytics and Conversion Optimization
Maths and Stats for Web Analytics and Conversion Optimization
This expert guide will teach you how to leverage the knowledge of maths and statistics in order to accurately interpret data and take actions, which can quickly improve the bottom-line of your online business.
Master the Essentials of Email Marketing Analytics
This book focuses solely on the ‘analytics’ that power your email marketing optimization program and will help you dramatically reduce your cost per acquisition and increase marketing ROI by tracking the performance of the various KPIs and metrics used for email marketing.
Attribution Modelling in Google Analytics and Beyond
Attribution modelling is the process of determining the most effective marketing channels for investment. This book has been written to help you implement attribution modelling. It will teach you how to leverage the knowledge of attribution modelling in order to allocate marketing budget and understand buying behaviour.
Attribution Modelling in Google Ads and Facebook
This book has been written to help you implement attribution modelling in Google Ads (Google AdWords) and Facebook. It will teach you, how to leverage the knowledge of attribution modelling in order to understand the customer purchasing journey and determine the most effective marketing channels for investment.
About the Author
Himanshu Sharma
If there is an error in your calculated field’s formula, you’ll see a warning message like WHEN conditions must compare a dimension or metric with a literal value and you won’t be able to save the field:
When this happens it means your formula has been rejected by Data Studio.
If you are new to using calculated fields, you will often see yourself in a situation where your formula has been rejected by the Data Studio.
The most common reasons why your formula is rejected by Data Studio:
#1 You used an invalid field name
If your field name is valid, it will appear as a green or blue chip in the formula:
However, if your field name is not valid, it will not appear as a green or blue chip in the formula.
If you reference a field that does not exist in your data source then it is going to be an invalid field.
Similarly, if your field has got a spelling mistake or some special character (like white space) in it then it is going to be invalid.
And when Data Studio encounters an invalid field in a formula it rejects the formula.
For example in the screenshot below, Data Studio does not recognize ‘Sales’ as a valid field and that’s why you see the error message ‘Unknow dimension or metric id: Sales‘:
#2 You used an invalid function name
If your function name is valid, it will automatically appear as an uppercase green text:
A function that is not spelt correctly or which is not supported by Data Studio is an example of an invalid function.
For example, in the screenshot below the function ‘concatenate’ is not a valid function name:
Therefore it is rejected by Data Studio and you see the error message ‘Unsupported operator: CONCATENATE”.
However, the function ‘CONCAT‘ is valid and that’s why it appears as an uppercase green text:
#3 Missing quotes
Make sure all literal strings are properly quoted with single or double-quotes.
For example, in the formula below the string ‘yes’ is not surrounded by a single or double quote and because of that reason, Data Studio rejected the formula and you see the error message: ‘Invalid formula: Invalid input expression – Invalid filter argument‘:
When the string ‘yes’ is surrounded by a single quote, the formula becomes valid:
#4 Mismatched parentheses
When nesting functions, it is quite easy to miss a closing parenthesis. You need to make sure that you have the same number of opening parentheses as closing parentheses.
For example, in the formula below we are nesting the ‘CONCAT’ function:
However, since the number of opening parentheses is not the same as the closing parentheses, Data Studio rejected the formula and you see the error message: Syntax error: Expected “)”.
The formula below contains the same number of opening parentheses as closing parentheses and is therefore valid:
#5 Not using the correct parameters for your function can cause your formula to be rejected by Data Studio.
#6 Applying an aggregation function on already aggregated data
For example, the ‘Sessions’ metric is already summed in Google Analytics.
So the formula SUM(Sessions) will be rejected by Data Studio and you will see the error message ‘Re-aggregating metrics is not supported‘:
Another example:
#7 Mixing dimensions and metrics in function arguments
An expression can have either metrics or dimensions, but not both.
For example, the formula below is made up of an expression that contains both dimension (the field ‘medium’) and metric (the field ‘Sessions’) and because of that reason, Data Studio rejected the formula and you see the error message: ‘Sorry Calculated fields can’t mix metrics (aggregated values) and dimensions (non-aggregated values). Please check the aggregation types of the fields in this formula.‘:
Get the ebook on Google Data Studio (50+ Pages)
Register for the FREE TRAINING...
"How to use Digital Analytics to generate floods of new Sales and Customers without spending years figuring everything out on your own."
Here’s what we’re going to cover in this training…
#1 Why digital analytics is the key to online business success.
#2 The number 1 reason why most marketers are not able to scale their advertising and maximize sales.
#3 Why Google and Facebook ads don’t work for most businesses & how to make them work.
#4 Why you won’t get any competitive advantage in the marketplace just by knowing Google Analytics.
#5 The number 1 reason why conversion optimization is not working for your business.
#6 How to advertise on any marketing platform for FREE with an unlimited budget.
#7 How to learn and master digital analytics and conversion optimization in record time.
My best selling books on Digital Analytics and Conversion Optimization
Maths and Stats for Web Analytics and Conversion Optimization
This expert guide will teach you how to leverage the knowledge of maths and statistics in order to accurately interpret data and take actions, which can quickly improve the bottom-line of your online business.
Master the Essentials of Email Marketing Analytics
This book focuses solely on the ‘analytics’ that power your email marketing optimization program and will help you dramatically reduce your cost per acquisition and increase marketing ROI by tracking the performance of the various KPIs and metrics used for email marketing.
Attribution Modelling in Google Analytics and Beyond
Attribution modelling is the process of determining the most effective marketing channels for investment. This book has been written to help you implement attribution modelling. It will teach you how to leverage the knowledge of attribution modelling in order to allocate marketing budget and understand buying behaviour.
Attribution Modelling in Google Ads and Facebook
This book has been written to help you implement attribution modelling in Google Ads (Google AdWords) and Facebook. It will teach you, how to leverage the knowledge of attribution modelling in order to understand the customer purchasing journey and determine the most effective marketing channels for investment.
About the Author
Himanshu Sharma