This function returns
a value corresponding to a timestamp of the last millisecond of the
last day of the calendar week containing date.
The default output format will be the DateFormat set in the script.
The weekend() function determines which week the date falls into. It then returns a timestamp, in date format, for the last millisecond of that week. The first day of the week is determined by the FirstWeekDay environment variable. However, this can be superseded by the first_week_day argument in the weekend() function.
Arguments
Argument
Description
timestamp
The date or timestamp to evaluate.
period_no
shift is an integer, where the value
0 indicates the week which contains date.
Negative values in shift indicate preceding weeks and positive values
indicate succeeding weeks.
first_week_day
Specifies the day on which the week starts. If omitted, the value of variable FirstWeekDay is used.
The possible values for first_week_day are 0 for Monday, 1 for Tuesday, 2 for Wednesday, 3 for Thursday, 4 for Friday, 5 for Saturday, and 6 for Sunday.
For more information about the system variable, see FirstWeekDay
When to use it
The weekend() function is commonly used as part of an expression when the user would like the calculation to use remaining days of the week for the specified date. For example, it could be used if a user would like to calculate the total interest not yet incurred during the week.
The following examples assume:
SET FirstWeekDay=0;
Example
Result
weekend('01/10/2013')
Returns 01/12/2013 23:59:59.
weekend('01/10/2013', -1)
Returns
01/05/2013 23:59:59..
weekend('01/10/2013', 0, 1)
Returns 01/14/2013 23:59:59.
Regional settings
Unless otherwise specified, the examples in this topic use the following date format: MM/DD/YYYY. The date format is specified in the SET DateFormat statement in your data load script. The default date formatting may be different in your system, due to your regional settings and other factors. You can change the formats in the examples below to suit your requirements. Or you can change the formats in your load script to match these examples.
Default regional settings in apps are based on the regional system settings of the computer or server where Qlik Sense is installed. If the Qlik Sense server you are accessing is set to Sweden, the Data load editor will use Swedish regional settings for dates, time, and currency. These regional format settings are not related to the language displayed in the Qlik Sense user interface. Qlik Sense will be displayed in the same language as the browser you are using.
Examples:
If you want ISO settings for weeks and week numbers, make sure to have the following in the script:
Set DateFormat ='YYYY-MM-DD';
Set FirstWeekDay =0; // Monday as first week day
Set BrokenWeeks =0; //(use unbroken weeks)
Set ReferenceDay =4; // Jan 4th is always in week 1
If you want US settings, make sure to have the following in the script:
Set DateFormat ='M/D/YYYY';
Set FirstWeekDay =6; // Sunday as first week day
Set BrokenWeeks =1; //(use broken weeks)
Set ReferenceDay =1; // Jan 1st is always in week 1
The examples above results in the following from the weekend() function:
Example of Weekend function
Date
ISO week end
US week end
Sat 2020 Dec 26
2020-12-27
12/26/2020
Sun 2020 Dec 27
2020-12-27
1/2/2021
Mon 2020 Dec 28
2021-01-03
1/2/2021
Tue 2020 Dec 29
2021-01-03
1/2/2021
Wed 2020 Dec 30
2021-01-03
1/2/2021
Thu 2020 Dec 31
2021-01-03
1/2/2021
Fri 2021 Jan 1
2021-01-03
1/2/2021
Sat 2021 Jan 2
2021-01-03
1/2/2021
Sun 2021 Jan 3
2021-01-03
1/9/2021
Mon 2021 Jan 4
2021-01-10
1/9/2021
Tue 2021 Jan 5
2021-01-10
1/9/2021
Information noteThe week ends are on Sundays in the ISO column, and on Saturdays in the US column.
Example 1 – Basic example
Overview
Open the Data load editor and add the load script below to a new tab.
The load script contains:
A dataset containing a set of transactions for 2022, which is loaded into a table called Transactions.
The date field provided in the DateFormat system variable (MM/DD/YYYY) format.
The creation of a field, end_of_week, that returns a timestamp for the end of the week when the transactions took place.
Load the data and open a sheet. Create a new table and add these fields as dimensions:
date
end_of_week
end_of_week_timestamp
Results table
date
end_of_week
end_of_week_timestamp
1/7/2022
01/08/2022
1/8/2022 11:59:59 PM
1/19/2022
01/22/2022
1/22/2022 11:59:59 PM
2/5/2022
02/05/2022
2/5/2022 11:59:59 PM
2/28/2022
03/05/2022
3/5/2022 11:59:59 PM
3/16/2022
03/19/2022
3/19/2022 11:59:59 PM
4/1/2022
04/02/2022
4/2/2022 11:59:59 PM
5/7/2022
05/07/2022
5/7/2022 11:59:59 PM
5/16/2022
05/21/2022
5/21/2022 11:59:59 PM
6/15/2022
06/18/2022
6/18/2022 11:59:59 PM
6/26/2022
07/02/2022
7/2/2022 11:59:59 PM
7/9/2022
07/09/2022
7/9/2022 11:59:59 PM
7/22/2022
07/23/2022
7/23/2022 11:59:59 PM
7/23/2022
07/23/2022
7/23/2022 11:59:59 PM
7/27/2022
07/30/2022
7/30/2022 11:59:59 PM
8/2/2022
08/06/2022
8/6/2022 11:59:59 PM
8/8/2022
08/13/2022
8/13/2022 11:59:59 PM
8/19/2022
08/20/2022
8/20/2022 11:59:59 PM
9/26/2022
10/01/2022
10/1/2022 11:59:59 PM
10/14/2022
10/15/2022
10/15/2022 11:59:59 PM
10/29/2022
10/29/2022
10/29/2022 11:59:59 PM
The end_of_week field is created in the preceding load statement by using the weekend() function and passing the date field as the function’s argument.
The weekend() function identifies which week the date value falls into and returns a timestamp for the last millisecond of that week.
Transaction 8191 took place on February 5. The FirstWeekDay system variable sets the first day of the week to a Sunday. The weekend() function identifies that the first Saturday after February 5– and therefore the end of the week – was on February 5. Therefore, the end_of_week value for that transaction returns the last millisecond of that day, which is February 5 at 11:59:59 PM.
Example 2 – period_no
Overview
Open the Data load editor and add the load script below to a new tab.
The load script contains:
The same dataset and scenario as the first example.
The creation of a field, previous_week_end,that returns the timestamp for the start of the week before the transaction took place.
Load the data and open a sheet. Create a new table and add these fields as dimensions:
date
previous_week_end
previous_week_end_timestamp
Results table
date
end_of_week
end_of_week_timestamp
1/7/2022
01/01/2022
1/1/2022 11:59:59 PM
1/19/2022
01/15/2022
1/15/2022 11:59:59 PM
2/5/2022
01/29/2022
1/29/2022 11:59:59 PM
2/28/2022
02/26/2022
2/26/2022 11:59:59 PM
3/16/2022
03/12/2022
3/12/2022 11:59:59 PM
4/1/2022
03/26/2022
3/26/2022 11:59:59 PM
5/7/2022
04/30/2022
4/30/2022 11:59:59 PM
5/16/2022
05/14/2022
5/14/2022 11:59:59 PM
6/15/2022
06/11/2022
6/11/2022 11:59:59 PM
6/26/2022
06/25/2022
6/25/2022 11:59:59 PM
7/9/2022
07/02/2022
7/2/2022 11:59:59 PM
7/22/2022
07/16/2022
7/16/2022 11:59:59 PM
7/23/2022
07/16/2022
7/16/2022 11:59:59 PM
7/27/2022
07/23/2022
7/23/2022 11:59:59 PM
8/2/2022
07/30/2022
7/30/2022 11:59:59 PM
8/8/2022
08/06/2022
8/6/2022 11:59:59 PM
8/19/2022
08/13/2022
8/13/2022 11:59:59 PM
9/26/2022
09/24/2022
9/24/2022 11:59:59 PM
10/14/2022
10/08/2022
10/8/2022 11:59:59 PM
10/29/2022
10/22/2022
10/22/2022 11:59:59 PM
In this instance, because a period_no of -1 was used as the offset argument in the weekend() function, the function first identifies the week in which the transactions take place. It then looks one week prior and identifies the last millisecond of that week.
Transaction 8196 took place on June 15. The weekend() function identifies that the week begins on June 12. Therefore, the previous week ends on June 11 at 11:59:59 PM; this is the value returned for the previous_week_end field.
Example 3 – first_week_day
Overview
Open the Data load editor and add the load script below to a new tab.
The load script contains the same dataset and scenario as the first example. However, in this example, we need to set Tuesday as the first day of the work week.
Load the data and open a sheet. Create a new table and add these fields as dimensions:
date
end_of_week
end_of_week_timestamp
Results table
date
end_of_week
end_of_week_timestamp
1/7/2022
01/10/2022
1/10/2022 11:59:59 PM
1/19/2022
01/24/2022
1/24/2022 11:59:59 PM
2/5/2022
02/07/2022
2/7/2022 11:59:59 PM
2/28/2022
02/28/2022
2/28/2022 11:59:59 PM
3/16/2022
03/21/2022
3/21/2022 11:59:59 PM
4/1/2022
04/04/2022
4/4/2022 11:59:59 PM
5/7/2022
05/09/2022
5/9/2022 11:59:59 PM
5/16/2022
05/16/2022
5/16/2022 11:59:59 PM
6/15/2022
06/20/2022
6/20/2022 11:59:59 PM
6/26/2022
06/27/2022
6/27/2022 11:59:59 PM
7/9/2022
07/11/2022
7/11/2022 11:59:59 PM
7/22/2022
07/25/2022
7/25/2022 11:59:59 PM
7/23/2022
07/25/2022
7/25/2022 11:59:59 PM
7/27/2022
08/01/2022
8/1/2022 11:59:59 PM
8/2/2022
08/08/2022
8/8/2022 11:59:59 PM
8/8/2022
08/08/2022
8/8/2022 11:59:59 PM
8/19/2022
08/22/2022
8/22/2022 11:59:59 PM
9/26/2022
09/26/2022
9/26/2022 11:59:59 PM
10/14/2022
10/17/2022
10/17/2022 11:59:59 PM
10/29/2022
10/31/2022
10/31/2022 11:59:59 PM
In this instance, because the first_week_date argument of 1 is used in the weekend() function, it sets the first day of the week to Tuesday.
Transaction 8191 took place on February 5. The weekend() function identifies that the first Monday after the this date – and therefore the end of the week and value returned – was on February 6 at 11:59:59 PM.
Example 4 – Chart object example
Overview
Open the Data load editor and add the load script below to a new tab.
The load script contains the same dataset and scenario as the first example. However, in this example, the unchanged dataset is loaded into the application. The calculation that returns a timestamp for the end of the week when the transactions took place is created as a measure in a chart object of the application.
Load the data and open a sheet. Create a new table and add this field as a dimension: date.
To calculate the start of the week that a transaction takes place in, add the following measures:
=weekend(date)
=timestamp(weekend(date))
Results table
date
=weekend(date)
=timestamp(weekend(date))
1/7/2022
01/08/2022
1/8/2022 11:59:59 PM
1/19/2022
01/22/2022
1/22/2022 11:59:59 PM
2/5/2022
02/05/2022
2/5/2022 11:59:59 PM
2/28/2022
03/05/2022
3/5/2022 11:59:59 PM
3/16/2022
03/19/2022
3/19/2022 11:59:59 PM
4/1/2022
04/02/2022
4/2/2022 11:59:59 PM
5/7/2022
05/07/2022
5/7/2022 11:59:59 PM
5/16/2022
05/21/2022
5/21/2022 11:59:59 PM
6/15/2022
06/18/2022
6/18/2022 11:59:59 PM
6/26/2022
07/02/2022
7/2/2022 11:59:59 PM
7/9/2022
07/09/2022
7/9/2022 11:59:59 PM
7/22/2022
07/23/2022
7/23/2022 11:59:59 PM
7/23/2022
07/23/2022
7/23/2022 11:59:59 PM
7/27/2022
07/30/2022
7/30/2022 11:59:59 PM
8/2/2022
08/06/2022
8/6/2022 11:59:59 PM
8/8/2022
08/13/2022
8/13/2022 11:59:59 PM
8/19/2022
08/20/2022
8/20/2022 11:59:59 PM
9/26/2022
10/01/2022
10/1/2022 11:59:59 PM
10/14/2022
10/15/2022
10/15/2022 11:59:59 PM
10/29/2022
10/29/2022
10/29/2022 11:59:59 PM
The end_of_week measure is created in the chart object by using the weekend() function and passing the date field as the function’s argument.
The weekend() function identifies which week the date value falls into, returning a timestamp for the last millisecond of that week.
Transaction 8191 took place on February 5. The FirstWeekDay system variable sets the first day of the week to a Sunday. The weekend() function identifies that the first Saturday after February 5 – and therefore the end of the week – was on February 5. Therefore, the end_of_week value for that transaction returns the last millisecond of that day, which is February 5 at 11:59:59 PM.
Example 5 – Scenario
Overview
Open the Data load editor and add the load script below to a new tab.
The load script contains:
A dataset which is loaded into a table called Employee_Expenses.
Data consisting of employee IDs, employee names, and the average daily expense claims of each employee.
The end user would like a chart object that displays, by employee ID and employee name, the estimated expense claims still to be incurred for the remainder of the week.
Load the data and open a sheet. Create a new table and add these fields as dimensions:
employee_id
employee_name
Next, create a measure to calculate the accumulated interest:
=(weekend(today(1))-today(1))*avg_daily_claim
Set the measure's Number formatting to Money.
Results table
employee_id
employee_name
=(weekend(today(1))-today(1))*avg_daily_claim
182
Mark
$90.00
183
Deryck
$75.00
184
Dexter
$75.00
185
Sydney
$162.00
186
Agatha
$108.00
The weekend() function, by using today’s date as its only argument, returns the end date of the current week. Then, by subtracting today’s date from the week end date, the expression returns the number of days that remain this week.
This value is then multiplied by the average daily expense claim by each employee to calculate the estimated value of claims that each employee is expected to make in the remaining week.
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