lunarweekstart - script and chart function
This function returns a value corresponding to a timestamp of the first millisecond of the first day of the lunar week containing date. Lunar weeks in Qlik Sense are defined by counting January 1 as the first day of the week and, apart from the final week of the year, will contain exactly seven days.
Syntax:
LunarweekStart(date[, period_no[, first_week_day]])
Return data type: dual
The lunarweekstart() function determines which lunar week the date falls into. It then returns a timestamp, in date format, for the first millisecond of that week.
Argument | Description |
---|---|
date | The date or timestamp to evaluate. |
period_no | period_no is an integer or expression resolving to an integer, where the value 0 indicates the lunar week which contains date. Negative values in period_no indicate preceding lunar weeks and positive values indicate succeeding lunar weeks. |
first_week_day | An offset that may be greater than or less than zero. This changes the beginning of the year by the specified number of days and/or fractions of a day. |
When to use it
The lunarweekstart() function is commonly used as part of an expression when the user would like the calculation to use the fraction of the week that has elapsed thus far. Unlike the weekstart() function, at the start of each new calendar year, week’s begin on January 1 and each subsequent week begins seven days later. The lunarweekstart() function is not affected by the FirstWeekDay system variable.
For example, the lunarweekstart() can be used to calculate the interest that has been accumulated in a week to date.
Example | Result |
---|---|
lunarweekstart('01/12/2013') | Returns 01/08/2013. |
lunarweekstart('01/12/2013', -1) | Returns 01/01/2013. |
lunarweekstart('01/12/2013', 0, 1 ) | Returns 01/09/2013, because setting first_week_day to 1 means the beginning of the year is changed to 01/02/2013. |
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 – No additional arguments
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 start_of_week, that returns a timestamp for the start of the lunar week in which the transactions took place.
Load script
SET DateFormat='MM/DD/YYYY';
Transactions:
Load
*,
lunarweekstart(date) as start_of_week,
timestamp(lunarweekstart(date)) as start_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
-
start_of_week
-
start_of_week_timestamp
date | start_of_week | start_of_week_timestamp |
---|---|---|
1/7/2022 | 01/01/2022 | 1/1/2022 12:00:00 AM |
1/19/2022 | 01/15/2022 | 1/15/2022 12:00:00 AM |
2/5/2022 | 02/05/2022 | 2/5/2022 12:00:00 AM |
2/28/2022 | 02/26/2022 | 2/26/2022 12:00:00 AM |
3/16/2022 | 03/12/2022 | 3/12/2022 12:00:00 AM |
4/1/2022 | 03/26/2022 | 3/26/2022 12:00:00 AM |
5/7/2022 | 05/07/2022 | 5/7/2022 12:00:00 AM |
5/16/2022 | 05/14/2022 | 5/14/2022 12:00:00 AM |
6/15/2022 | 06/11/2022 | 6/11/2022 12:00:00 AM |
6/26/2022 | 06/25/2022 | 6/25/2022 12:00:00 AM |
7/9/2022 | 07/09/2022 | 7/9/2022 12:00:00 AM |
7/22/2022 | 07/16/2022 | 7/16/2022 12:00:00 AM |
7/23/2022 | 07/23/2022 | 7/23/2022 12:00:00 AM |
7/27/2022 | 07/23/2022 | 7/23/2022 12:00:00 AM |
8/2/2022 | 07/30/2022 | 7/30/2022 12:00:00 AM |
8/8/2022 | 08/06/2022 | 8/6/2022 12:00:00 AM |
8/19/2022 | 08/13/2022 | 8/13/2022 12:00:00 AM |
9/26/2022 | 09/24/2022 | 9/24/2022 12:00:00 AM |
10/14/2022 | 10/08/2022 | 10/8/2022 12:00:00 AM |
10/29/2022 | 10/29/2022 | 10/29/2022 12:00:00 AM |
The start_of_week field is created in the preceding load statement by using the lunarweekstart() function and passing the date field as the function’s argument.
The lunarweekstart() function identifies the lunar week into which the date falls, returning a timestamp for the first millisecond of that week.
Transaction 8189 took place on January 19. The lunarweekstart() function identifies that the lunar week begins on January 15. Therefore, the start_of_week value for that transaction returns the first millisecond of that day, which is January 15 at 12:00:00 AM.
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_lunar_week_start, that returns the timestamp for the start of the lunar week before the transaction took place.
Load script
SET DateFormat='MM/DD/YYYY';
Transactions:
Load
*,
lunarweekstart(date,-1) as previous_lunar_week_start,
timestamp(lunarweekstart(date,-1)) as previous_lunar_week_start_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
date | previous_lunar_week_start | previous_lunar_week_start_timestamp |
---|---|---|
1/7/2022 | 12/24/2021 | 12/24/2021 12:00:00 AM |
1/19/2022 | 01/08/2022 | 1/8/2022 12:00:00 AM |
2/5/2022 | 01/29/2022 | 1/29/2022 12:00:00 AM |
2/28/2022 | 02/19/2022 | 2/19/2022 12:00:00 AM |
3/16/2022 | 03/05/2022 | 3/5/2022 12:00:00 AM |
4/1/2022 | 03/19/2022 | 3/19/2022 12:00:00 AM |
5/7/2022 | 04/30/2022 | 4/30/2022 12:00:00 AM |
5/16/2022 | 05/07/2022 | 5/7/2022 12:00:00 AM |
6/15/2022 | 06/04/2022 | 6/4/2022 12:00:00 AM |
6/26/2022 | 06/18/2022 | 6/18/2022 12:00:00 AM |
7/9/2022 | 07/02/2022 | 7/2/2022 12:00:00 AM |
7/22/2022 | 07/09/2022 | 7/9/2022 12:00:00 AM |
7/23/2022 | 07/16/2022 | 7/16/2022 12:00:00 AM |
7/27/2022 | 07/16/2022 | 7/16/2022 12:00:00 AM |
8/2/2022 | 07/23/2022 | 7/23/2022 12:00:00 AM |
8/8/2022 | 07/30/2022 | 7/30/2022 12:00:00 AM |
8/19/2022 | 08/06/2022 | 8/6/2022 12:00:00 AM |
9/26/2022 | 09/17/2022 | 9/17/2022 12:00:00 AM |
10/14/2022 | 10/01/2022 | 10/1/2022 12:00:00 AM |
10/29/2022 | 10/22/2022 | 10/22/2022 12:00:00 AM |
In this instance, because a period_no of -1 was used as the offset argument in the lunarweekstart() function, the function first identifies the lunar week that the transactions take place in. It then shifts one week prior and identifies the first millisecond of that lunar week.
Transaction 8189 took place on January 19. The lunarweekstart() function identifies that the lunar week begins on January 15. Therefore, the previous lunar week began on January 8 at 12:00:00 AM; this is the value returned for the previous_lunar_week_start 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. In this example, we set lunar weeks to begin on January 5.
Load script
SET DateFormat='MM/DD/YYYY';
Transactions:
Load
*,
lunarweekstart(date,0,4) as start_of_week,
timestamp(lunarweekstart(date,0,4)) as start_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
-
start_of_week
-
start_of_week_timestamp
date | start_of_week | start_of_week_timestamp |
---|---|---|
1/7/2022 | 01/05/2022 | 1/5/2022 12:00:00 AM |
1/19/2022 | 01/19/2022 | 1/19/2022 12:00:00 AM |
2/5/2022 | 02/02/2022 | 2/2/2022 12:00:00 AM |
2/28/2022 | 02/23/2022 | 2/23/2022 12:00:00 AM |
3/16/2022 | 03/16/2022 | 3/16/2022 12:00:00 AM |
4/1/2022 | 03/30/2022 | 3/30/2022 12:00:00 AM |
5/7/2022 | 05/04/2022 | 5/4/2022 12:00:00 AM |
5/16/2022 | 05/11/2022 | 5/11/2022 12:00:00 AM |
6/15/2022 | 06/15/2022 | 6/15/2022 12:00:00 AM |
6/26/2022 | 06/22/2022 | 6/22/2022 12:00:00 AM |
7/9/2022 | 07/06/2022 | 7/6/2022 12:00:00 AM |
7/22/2022 | 07/20/2022 | 7/20/2022 12:00:00 AM |
7/23/2022 | 07/20/2022 | 7/20/2022 12:00:00 AM |
7/27/2022 | 07/27/2022 | 7/27/2022 12:00:00 AM |
8/2/2022 | 07/27/2022 | 7/27/2022 12:00:00 AM |
8/8/2022 | 08/03/2022 | 8/3/2022 12:00:00 AM |
8/19/2022 | 08/17/2022 | 8/17/2022 12:00:00 AM |
9/26/2022 | 09/21/2022 | 9/21/2022 12:00:00 AM |
10/14/2022 | 10/12/2022 | 10/12/2022 12:00:00 AM |
10/29/2022 | 10/26/2022 | 10/26/2022 12:00:00 AM |
In this instance, because the first_week_date argument of 4 is used in the lunarweekstart() function, it offsets the start of the year from January 1 to January 5.
Transaction 8189 took place on January 19. Due to lunar weeks beginning on January 5, the lunarweekstart() function identifies that the lunar week containing January 19 begins on January 19 at 12:00:00 AM as well. Therefore, that is the value returned for the start_of_week field.
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 start of the lunar week in which 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.
Add the following measures:
=lunarweekstart(date)
=timestamp(lunarweekstart(date))
date | =lunarweekstart(date) | =timestamp(lunarweekstart(date)) |
---|---|---|
1/7/2022 | 01/01/2022 | 1/1/2022 12:00:00 AM |
1/19/2022 | 01/15/2022 | 1/15/2022 12:00:00 AM |
2/5/2022 | 02/05/2022 | 2/5/2022 12:00:00 AM |
2/28/2022 | 02/26/2022 | 2/26/2022 12:00:00 AM |
3/16/2022 | 03/12/2022 | 3/12/2022 12:00:00 AM |
4/1/2022 | 03/26/2022 | 3/26/2022 12:00:00 AM |
5/7/2022 | 05/07/2022 | 5/7/2022 12:00:00 AM |
5/16/2022 | 05/14/2022 | 5/14/2022 12:00:00 AM |
6/15/2022 | 06/11/2022 | 6/11/2022 12:00:00 AM |
6/26/2022 | 06/25/2022 | 6/25/2022 12:00:00 AM |
7/9/2022 | 07/09/2022 | 7/9/2022 12:00:00 AM |
7/22/2022 | 07/16/2022 | 7/16/2022 12:00:00 AM |
7/23/2022 | 07/23/2022 | 7/23/2022 12:00:00 AM |
7/27/2022 | 07/23/2022 | 7/23/2022 12:00:00 AM |
8/2/2022 | 07/30/2022 | 7/30/2022 12:00:00 AM |
8/8/2022 | 08/06/2022 | 8/6/2022 12:00:00 AM |
8/19/2022 | 08/13/2022 | 8/13/2022 12:00:00 AM |
9/26/2022 | 09/24/2022 | 9/24/2022 12:00:00 AM |
10/14/2022 | 10/08/2022 | 10/8/2022 12:00:00 AM |
10/29/2022 | 10/29/2022 | 10/29/2022 12:00:00 AM |
The start_of_week measure is created in the chart object by using the lunarweekstart() function, and passing the date field as the function’s argument.
The lunarweekstart() function identifies which lunar week the date value falls into, returning a timestamp for the last millisecond of that week.
Transaction 8189 took place on January 19. The lunarweekstart() function identifies that the lunar week begins on January 15. Therefore, the start_of_week value for that transaction is first millisecond of that day, which is the January 15 at 12:00:00 AM.
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 containing a set of loan balances, which is loaded into a table called Loans.
-
Data consisting of loan IDs, the balance at the beginning of the week, and the simple interest rate charged on each loan per annum.
The end user would like a chart object that displays, by loan ID, the current interest that has been accrued on each loan in the week to date.
Load script
Loans:
Load
*
Inline
[
loan_id,start_balance,rate
8188,$10000.00,0.024
8189,$15000.00,0.057
8190,$17500.00,0.024
8191,$21000.00,0.034
8192,$90000.00,0.084
];
Results
Do the following:
-
Load the data and open a sheet. Create a new table.
-
Add the following fields as dimensions.
-
loan_id
-
start_balance
-
-
Next, create the following measure to calculate the accumulated interest:
=start_balance*(rate*(today(1)-lunarweekstart(today(1)))/365)
-
Set the measure's Number formatting to Money.
loan_id | start_balance | =start_balance*(rate*(today(1)- lunarweekstart (today(1)))/365) |
---|---|---|
8188 | $10000.00 | $15.07 |
8189 | $15000.00 | $128.84 |
8190 | $17500.00 | $63.29 |
8191 | $21000.00 | $107.59 |
8192 | $90000.00 | $1139.18 |
The lunarweekstart() function, using today’s date as its only argument, returns the start date of the current year. By subtracting that result from the current date, the expression returns the number of days that have elapsed so far this week.
This value is then multiplied by the interest rate and divided by 365 to return the effective interest rate incurred for this period. The result is then multiplied by the starting balance of the loan to return the interest that has been accrued so far this week.