Introduction to Google Data Studio Function : HOUR
In this session, we would be diving into Google Data Studio's valuable function known as "HOUR". This robust function plays a key role in extracting the hour component from any given date & time field or expression, proving to be immensely beneficial in data analysis and time series computations.
Understanding How the HOUR Function Works.
The HOUR function in Google Data Studio operates on two distinct syntax models.
md
- HOUR(datetime_expression)
- HOUR(X [,Input Format])
The first syntax requires a 'datetime_expression,' which refers to either a Date & Time field or an expression which effectively evaluates to a date and time value.
The second syntax deals with a 'Compatibility mode Date value.' It operates on 'X,' a field or expression that evaluates to text, number, or compatibility mode date. The function also takes an optional parameter called 'Input Format' to deal with the string representation of date values if the input is in text form.
Example Usage of the HOUR Function
To appreciate the utility of the HOUR function, let's consider an example where an e-commerce company wants to inspect their sales patterns based on the hourly sales data.
They have a timestamp of the sales records in the format: 'YYYY-MM-DD HH:MM:SS'
Original Sales Data:
md
| Sale_TimeStamp | Sale_Amount |
|:-:|:--:|
| 2022-02-01 11:34:57 | 2000 |
| 2022-02-01 15:15:32 | 3500 |
| 2022-02-02 11:47:20 | 1800 |
To get the hour from each sale timestamp:
md
HOUR(Sale_TimeStamp)
This will give you the hour of the sale from each timestamp which you can use in your further data analysis.
Converted Sales Data:
md
| Hour | Sale_Amount |
|:-:|:--:|
| 11 | 2000 |
| 15 | 3500 |
| 11 | 1800 |
Limitations of the HOUR Function
One primary limitation of the HOUR function is that it does not return the hour based on any specific timezone; it gives the hour based on the timezone that the input datetime_expression is in.
Tips on Using the HOUR Function
Ensure that time data is in a consistent and suitable format while using the HOUR function. Although it supports a variety of input formats, cumbersome errors could be averted by maintaining a consistent time data format across the entire dataset.
In conclusion, the HOUR function serves as a powerful tool allowing detailed time-segmented data analysis. It's crucial for scenarios where granular analysis based on the hour of the day is required, providing dynamic insights into hourly patterns and trends.
