Date and Time Data Types in Google Data Studio – Tutorial

Google Data Studio

Table of Contents

  1. Absolute dates
  2. Absolute date types
  3. Using incorrect absolute date type
  4. Absolute time
  5. Data types for absolute time
  6. Using incorrect absolute time type
  7. Changing a field’s type from absolute date to absolute time
  8. Relative dates
  9. Relative date types
  10. Relative time
  11. Data types for relative time
  12. Related articles

When you create or edit a data source in Google Data Studio, you get the option to select the data type of the data source field. 

One of the data types supported by Google Data Studio is: ‘Date & Time’:

As you can see from the screenshot, Google Data Studio supports several different types of date and time.

The data types for ‘date’ fields can be divided into the following two categories:

  • Absolute Dates
  • Relative Dates

The data types for ‘time’ fields can be divided into the following two categories:

  • Absolute time 
  • Relative time

Absolute dates

The absolute date refers to a specific date that you can point to on a calendar. 

Following are examples of absolute dates in the context of Google Data Studio:

  • 2019
  • Q1 2019
  • Nov 2019
  • Nov 10, 2019

Absolute date types

These are the data types meant for absolute dates.

Following are absolute date types:

  • Year (YYYY) – example: 2017, 2018, 2019 etc
  • Year Quarter (QYYYY) – example: Q1 2019, Q2 2019, Q3 2019 etc
  • Year Month (YYYYMM) – example: Oct 2019, Sept 2019, Nov 2019 etc.
  • ISO Year Week (YYYYww) 
  • Date (YYYYMMDD) – example: 2019-01-03, 2019-01-03, 2019-01-05 etc

You should select that data type for your absolute date which matches the date type in your data set. 

For example, consider the following data set:

Here the field ‘order date’ contains a date in the format: YYYY-MM-DD

So when setting up the data source schema, we should set the data type of ‘Order Date’ field to ‘Date (YYYYMMDD)’:

Here is how the report created from this data source would look like:

As you can see from the report above, a date field’s type is not always the same as its display format. 

The visual appearance of the actual date in the report depends upon the following two factors:

  • Your Google account’s language setting. 
  • How data aggregates in individual charts

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Consider another data set:

Here the field ‘order date’ contains the date in the format: YYYY-MM

So when setting up the data source schema, we should set the data type of ‘Order Date’ field to ‘Year Month (YYYYMM)’:

Here is how the report created from this data source would look like:

From the report above, we can once again conclude that a date field’s type is not always the same as its display format. 

Note: Here I didn’t select the Date (YYYYMMDD) data type because our data set doesn’t contain the day information (DD). 

Using incorrect absolute date type

Consider the following data set:

Here the field ‘order date’ contains the date in the format: YYYY-MM

So when setting up the data source schema, we should set the data type of ‘Order Date’ field to ‘Year Month (YYYYMM)’:

But what if we set the data type of ‘Order Date’ field to Date (YYYYMMDD):

The data type ‘Date (YYYYMMDD)‘ tells Data Studio to expect year, month and day in the ‘order date’ fields in our data set.

Now since the day information is missing in our data set, what Data Studio will do is add the default day ‘1’ to all ‘order date’ fields in our data set when you create/refresh your report.

So when you create a report from the data source, it would look like the one below:

But this report doesn’t match with our data set:

The ‘Order Date’ 2019-01 in our data set denotes ‘Jan 2019’ and not ‘Jan 1, 2019’.

The ‘Order Date’ 2019-02 in our data set denotes ‘Feb 2019’ and not ‘Feb 1, 2019’.

…

The ‘Order Date’ 2019-12 in our data set denotes ‘Dec 2019’ and not ‘Dec 1, 2019’.

That’s why using the correct absolute date type (the one that matches with data set) is very important. 

Absolute time

Absolute time refers to a specific time that you can point to on a clock.

Following are examples of absolute time in the context of Google Data Studio:

  • Sept 10, 2019, 10 PM
  • Sept 10, 2019, 10:30 PM

Data types for absolute time

These are the data types meant for absolute times.

Following are data types for absolute time:

  • Date Hour (YYYYMMDDhh)
  • Date Hour Minute (YYYYMMDDhhmm)

You should select that data type for your absolute time which matches the time type in your data set. 

For example, consider the following data set:

Here the field ‘order date’ contains both date and time and is in the format: YYYY-MM-DD-HH

So when setting up the data source schema, we should set the data type of ‘Order Date’ field to ‘Date Hour (YYYYMMDDhh)’:

Here is how the report created from this data source would look like:

Note: We didn’t use the Date Hour Minute (YYYYMMDDhhmm) data type because the minute information is not available in our data set.

Consider the following data set:

Here the field ‘order date’ contains both date and time and is in the format: YYYY-MM-DD-HH-MM

Since the minute information is available in the data set, we should set the data type of ‘Order Date’ field to ‘Date Hour Minute (YYYYMMDDhhmm)’:

Here is how the report created from this data source would look like:

Using incorrect absolute time type

Consider the following data set:

Here the field ‘order date’ contains both date and time and is in the format: YYYY-MM-DD-HH

So when setting up the data source schema, we should set the data type of ‘Order Date’ field to ‘Date Hour (YYYYMMDDhh)’:

But what if we set the data type of ‘Order Date’ field to ‘Date Hour Minute (YYYYMMDDhhmm)’:

The data type ‘Date Hour Minute (YYYYMMDDhhmm)’ tells Data Studio to expect year, month, hour and minute data in the ‘order date’ fields in our data set.

Now since the minute information is missing in our data set, what Data Studio will do is add the default minute ‘0’ to all ‘order date’ fields in our data set when you create/refresh your report.

So when you create a report from the data source, it would look like the one below:

Changing a field’s type from absolute date to absolute time

You should change a field’s type from absolute date to absolute time only when the time information is available in your data set. Otherwise, you would get incorrect/unexpected time data in your report.

For example, consider the following data set:

Here the ‘order date’ field does not contain any time data. 

Now let’s set the data type of ‘Order Date’ field to ‘Date Hour Minute (YYYYMMDDhh)’:

The data type ‘Date Hour Minute (YYYYMMDDhhmm)’ tells Data Studio to expect year, month, hour and minute data in the ‘order date’ fields in our data set.

Now since the hour and minute information is missing in our data set, what Data Studio will do is add the default hour ‘12’ and default minute ‘0’ to all ‘order date’ fields in our data set when you create/refresh your report.

The default time that Data Studio would append to the dates in the ‘Order Date’ field would be 12:00 am

So when you create a report from the data source, it would look like the one below:

That’s why it is important that you change a field’s type from absolute date to absolute time only when the time information is available in your data set. 

Relative dates

The relative date is the date that does not refer to a specific date.

Following are examples of relative dates in the context of Google Data Studio:

  • Q1, Q2, Q3, Q4 – here we don’t know, we are referring to the quarter of which year. 
  • Jan, Feb, March, April… – here we don’t know, we are referring to the month of which year. 
  • Mon, Tue, Wed, Thu… – here we don’t know, we are referring to the day of which week or month or year. 

Relative date types

These are the data types meant for relative dates.

Following are relative date types:

  • Quarter (1,2,3,4)
  • Month (MM)
  • ISO Week (ww)
  • Month Day (MMDD)
  • Day of Week (D)
  • Day of Month (DD)

You should select that data type for your relative date which matches the date type in your data set. 

For example, consider the following data set:

Here the field ‘order date’ contains data that refers to a quarter of a year like quarter 1, quarter 2, quarter 3 and quarter 4.

So when setting up the data source schema, we should set the data type of ‘Order Date’ field to ‘Quarter (1,2,3,4)’:

Here is how the report created from this data source would look like:

Consider another data set:

Here the field ‘order date’ contains data that refers to the days of the week: Monday, Tuesday, Wednesday, etc

So when setting up the data source schema, we should set the data type of ‘Order Date’ field to ‘Day of Week (D)’:

Here is how the report created from this data source would look like:

Relative time

Relative time is the time which does not refer to a specific point in time. 

Following are examples of relative time in the context of Google Data Studio:

  • 1 am, 3 am, 7 pm, etc – here we don’t know which day, month or year the time occurred. 

Data types for relative time

These are the data types meant for relative times.

Following are data types for relative time:

  • Hour (hh)
  • Minute (mm)

You should select that data type for your relative time which matches the time type in your data set. 

For example, consider the following data set:

Here the field ‘Order Time’ refers to the time of a day: 9 am, 10 am, 3 pm (15) and 8 pm (20).

So when setting up the data source schema, we should set the data type of ‘Order Time’ field to ‘Hour (hh)’:

Here is how the report created from this data source would look like:

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