weekend - script and chart function
This function returns a value corresponding to a timestamp of the last millisecond of the last day (Sunday) of the calendar week containing date. The default output format will be the DateFormat set in the script.
Syntax:
WeekEnd(date [, period_no[, first_week_day]])
Return data type: dual
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 However, this can be superseded by the first_week_day argument in the weekend() function.
Argument | Description |
---|---|
date | 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 |
broken_weeks |
If you don't specify broken_weeks, the value of variable BrokenWeeks will be used to define if weeks are broken or not. For more information about the system variable, see BrokenWeeks |
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.
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.
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 script
SET FirstWeekDay=6;
Transactions:
Load
*,
weekend(date) as end_of_week,
timestamp(weekend(date)) as end_of_week_timestamp
;
Load
*
Inline
[
id,date,amount
8188,1/7/2022,17.17
8189,1/19/2022,37.23
8190,2/28/2022,88.27
8191,2/5/2022,57.42
8192,3/16/2022,53.80
8193,4/1/2022,82.06
8194,5/7/2022,40.39
8195,5/16/2022,87.21
8196,6/15/2022,95.93
8197,6/26/2022,45.89
8198,7/9/2022,36.23
8199,7/22/2022,25.66
8200,7/23/2022,82.77
8201,7/27/2022,69.98
8202,8/2/2022,76.11
8203,8/8/2022,25.12
8204,8/19/2022,46.23
8205,9/26/2022,84.21
8206,10/14/2022,96.24
8207,10/29/2022,67.67
];
Results
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
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 script
SET DateFormat='MM/DD/YYYY';
Transactions:
Load
*,
weekend(date,-1) as previous_week_end,
timestamp(weekend(date,-1)) as previous_week_end_timestamp
;
Load
*
Inline
[
id,date,amount
8188,1/7/2022,17.17
8189,1/19/2022,37.23
8190,2/28/2022,88.27
8191,2/5/2022,57.42
8192,3/16/2022,53.80
8193,4/1/2022,82.06
8194,5/7/2022,40.39
8195,5/16/2022,87.21
8196,6/15/2022,95.93
8197,6/26/2022,45.89
8198,7/9/2022,36.23
8199,7/22/2022,25.66
8200,7/23/2022,82.77
8201,7/27/2022,69.98
8202,8/2/2022,76.11
8203,8/8/2022,25.12
8204,8/19/2022,46.23
8205,9/26/2022,84.21
8206,10/14/2022,96.24
8207,10/29/2022,67.67
];
Results
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
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 script
SET DateFormat='MM/DD/YYYY';
Transactions:
Load
*,
weekend(date,0,1) as end_of_week,
timestamp(weekend(date,0,1)) as end_of_week_timestamp,
;
Load
*
Inline
[
id,date,amount
8188,1/7/2022,17.17
8189,1/19/2022,37.23
8190,2/28/2022,88.27
8191,2/5/2022,57.42
8192,3/16/2022,53.80
8193,4/1/2022,82.06
8194,5/7/2022,40.39
8195,5/16/2022,87.21
8196,6/15/2022,95.93
8197,6/26/2022,45.89
8198,7/9/2022,36.23
8199,7/22/2022,25.66
8200,7/23/2022,82.77
8201,7/27/2022,69.98
8202,8/2/2022,76.11
8203,8/8/2022,25.12
8204,8/19/2022,46.23
8205,9/26/2022,84.21
8206,10/14/2022,96.24
8207,10/29/2022,67.67
];
Results
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
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 script
Transactions:
Load
*
Inline
[
id,date,amount
8188,1/7/2022,17.17
8189,1/19/2022,37.23
8190,2/28/2022,88.27
8191,2/5/2022,57.42
8192,3/16/2022,53.80
8193,4/1/2022,82.06
8194,5/7/2022,40.39
8195,5/16/2022,87.21
8196,6/15/2022,95.93
8197,6/26/2022,45.89
8198,7/9/2022,36.23
8199,7/22/2022,25.66
8200,7/23/2022,82.77
8201,7/27/2022,69.98
8202,8/2/2022,76.11
8203,8/8/2022,25.12
8204,8/19/2022,46.23
8205,9/26/2022,84.21
8206,10/14/2022,96.24
8207,10/29/2022,67.67
];
Results
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))
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 script
Employee_Expenses:
Load
*
Inline
[
employee_id,employee_name,avg_daily_claim
182,Mark, $15
183,Deryck, $12.5
184,Dexter, $12.5
185,Sydney,$27
186,Agatha,$18
];
Results
Do the following:
-
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.
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.