inlunarweektodate - script and chart function

This function finds if timestamp lies inside the part of the lunar week up to and including the last millisecond of base_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:

InLunarWeekToDate (timestamp, base_date, period_no [, first_week_day])

Return data type: Boolean

In Qlik Sense, the Boolean true value is represented by -1, and the false value is represented by 0.

Example diagram of inlunarweektodate function, showing the dates for which the function will return a TRUE value, given the input information. — Example diagram of inlunarweektodate() function

IThe inlunarweektodate() function acts as the end point of the lunar week. In contrast, the inlunarweek() function, determines which lunar week the base_date falls into. For example, if the base_date were January 5, any timestamp between January 1 and January 5 would return a Boolean result of TRUE, while dates on January 6 and 7, and later, would return a Boolean result of FALSE.

Arguments
Argument	Description
timestamp	The date that you want to compare with base_date.
base_date	Date that is used to evaluate the lunar week.
period_no	The lunar week can be offset by period_no. period_no is an integer, where the value 0 indicates the lunar week which contains base_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 inlunarweektodate() function returns a Boolean result. Typically, this type of function will be used as a condition in an IF expression. The inlunarweektodate() function would be used when the user would like the calculation to return an aggregation or calculation, dependent on whether the evaluated date occurred during a particular segment of the week in question.

For example, the inlunarweektodate() function can be used to identify all equipment manufactured in a particular week up to and including a particular date.

Function examples
Example	Result
inlunarweektodate('01/12/2013', '01/13/2013', 0)	Returns TRUE, since the value of the timestamp, 01/12/2013, falls in the part of the week 01/08/2013 to 01/13/2013.
inlunarweektodate('01/12/2013', '01/11/2013', 0)	Returns FALSE, since the value of the timestamp is later than the value of base_date, even though the two dates are in the same lunar week before 01/12/2012.
inlunarweektodate('01/12/2006', '01/05/2006', 1)	Returns TRUE. Specifying a value of 1 for period_no shifts the base_date forward one week, so the value of timestamp falls in the part of the lunar week.

The inlunarweektodate() function is often used in combination with the following functions:

Related functions
Function	Interaction
lunarweekname - script and chart function	This function is used to determine the lunar week number of the year in which an input date occurs.

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 the month of January, which is loaded into a table called Transactions. The default DateFormat system variable MM/DD/YYYY is used.
Create a field in_lunar_week_to_date, that determines which transactions took place in lunar week to date of January 10.

Load script

SET DateFormat='MM/DD/YYYY';

Transactions:
    Load
        *,
        inlunarweektodate(date,'01/10/2022', 0) as in_lunar_week_to_date
        ;
Load
*
Inline 
[
id,date,amount
8188,'1/10/2022',37.23
8189,'1/17/2022',17.17
8190,'1/26/2022',88.27
8191,'1/12/2022',57.42
8192,'1/19/2022',53.80
8193,'1/21/2022',82.06
8194,'1/1/2022',40.39
8195,'1/27/2022',87.21
8196,'1/11/2022',95.93
8197,'1/29/2022',45.89
8198,'1/31/2022',36.23
8199,'1/18/2022',25.66
8200,'1/23/2022',82.77
8201,'1/15/2022',69.98
8202,'1/4/2022',76.11
];

Results

Load the data and open a sheet. Create a new table and add these fields as dimensions:

date
in_lunar_week_to_date

Results table
date	in_lunar_week_to_date
1/1/2022	0
1/4/2022	0
1/10/2022	-1
1/11/2022	0
1/12/2022	0
1/15/2022	0
1/17/2022	0
1/18/2022	0
1/19/2022	0
1/21/2022	0
1/23/2022	0
1/26/2022	0
1/27/2022	0
1/29/2022	0
1/31/2022	0

Example of the use of the inlunarweektodate function showing the range of dates for which the function will return a value of TRUE, given the input information. This example contains no additional arguments. — inlunarweektodate() function, no additional arguments

The in_lunar_week_to_date field is created in the preceding load statement by using the inlunarweektodate() function and passing the date field, a hard-coded date for January 10 as our base_date, and an offset of 0 as the function’s arguments.

Because lunar weeks begin on January 1, January 10 would fall in the lunar week that begins on January 8; and because we are using the inlunarweektodate() function, that lunar week would then end on the 10th. Therefore, any transactions that occur between those two dates in January would return a Boolean value of TRUE. This is validated in the results table.

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. However, in this example, the task is to create a field, 2_lunar_weeks_later, that determines whether or not the transactions took place two weeks after the lunar week to date of January 1.

Load script

SET DateFormat='MM/DD/YYYY';
Transactions:
    Load
        *,
        inlunarweektodate(date,'01/10/2022', 2) as [2_lunar_weeks_later]
        ;
Load
*
Inline
[
id,date,amount
8188,'1/10/2022',37.23
8189,'1/17/2022',17.17
8190,'1/26/2022',88.27
8191,'1/12/2022',57.42
8192,'1/19/2022',53.80
8193,'1/21/2022',82.06
8194,'1/1/2022',40.39
8195,'1/27/2022',87.21
8196,'1/11/2022',95.93
8197,'1/29/2022',45.89
8198,'1/31/2022',36.23
8199,'1/18/2022',25.66
8200,'1/23/2022',82.77
8201,'1/15/2022',69.98
8202,'1/4/2022',76.11
];

Results

Load the data and open a sheet. Create a new table and add these fields as dimensions:

date
2_lunar_weeks_later

Results table
date	2_lunar_weeks_later
1/1/2022	0
1/4/2022	0
1/10/2022	0
1/11/2022	0
1/12/2022	0
1/15/2022	0
1/17/2022	0
1/18/2022	0
1/19/2022	0
1/21/2022	0
1/23/2022	-1
1/26/2022	0
1/27/2022	0
1/29/2022	0
1/31/2022	0

In this instance, the inlunarweektodate() function determines that the lunar week up to January 10 equates to three days (January 8, 9, 10). Since a period_no of 2 was used as the offset argument, this lunar week is shifted by 14 days. Therefore, this defines that three-day lunar week to include January 22, 23, and 24. Any transaction that takes place between January 22 and January 24 will return a Boolean result of TRUE.

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.
The default DateFormat system variable MM/DD/YYYY is used.
A first_week_date argument of 3. This sets lunar weeks to begin on January 3.

Load script

SET DateFormat='MM/DD/YYYY'; 

Transactions:
    Load
        *,
        inlunarweek(date,'01/10/2022', 0,3) as in_lunar_week_to_date
        ;
Load
*
Inline
[
id,date,amount
8188,'1/10/2022',37.23
8189,'1/17/2022',17.17
8190,'1/26/2022',88.27
8191,'1/12/2022',57.42
8192,'1/19/2022',53.80
8193,'1/21/2022',82.06
8194,'1/1/2022',40.39
8195,'1/27/2022',87.21
8196,'1/11/2022',95.93
8197,'1/29/2022',45.89
8198,'1/31/2022',36.23
8199,'1/18/2022',25.66
8200,'1/23/2022',82.77
8201,'1/15/2022',69.98
8202,'1/4/2022',76.11
];

Results

Load the data and open a sheet. Create a new table and add these fields as dimensions:

date
in_lunar_week_to_date

Results table
date	in_lunar_week_to_date
1/1/2022	0
1/4/2022	-1
1/10/2022	-1
1/11/2022	0
1/12/2022	0
1/15/2022	0
1/17/2022	0
1/18/2022	0
1/19/2022	0
1/21/2022	0
1/23/2022	0
1/26/2022	0
1/27/2022	0
1/29/2022	0
1/31/2022	0

In this instance, because the first_week_date argument of 3 is used in the inlunarweek() function, the first lunar week will be from January 3 to January 10. Because January 10 is also the base_date, any transaction that falls between these two dates will return a Boolean value of TRUE.

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 determines whether the transactions took place in the lunar week up to January 10 is created as a measure in a chart object of the application.

Load script

SET DateFormat='MM/DD/YYYY';

Transactions: 
Load
*
Inline
[
id,date,amount
8188,'1/10/2022',37.23
8189,'1/17/2022',17.17
8190,'1/26/2022',88.27
8191,'1/12/2022',57.42
8192,'1/19/2022',53.80
8193,'1/21/2022',82.06
8194,'1/1/2022',40.39
8195,'1/27/2022',87.21
8196,'1/11/2022',95.93
8197,'1/29/2022',45.89
8198,'1/31/2022',36.23
8199,'1/18/2022',25.66
8200,'1/23/2022',82.77
8201,'1/15/2022',69.98
8202,'1/4/2022',76.11
];

Results

Load the data and open a sheet. Create a new table and add this field as a dimension: date.

Create the following measure:

=inlunarweektodate(date,'01/10/2022', 0)

Results table
date	=inlunarweektodate(date,'01/10/2022', 0)
1/1/2022	0
1/4/2022	0
1/10/2022	-1
1/11/2022	0
1/12/2022	0
1/15/2022	0
1/17/2022	0
1/18/2022	0
1/19/2022	0
1/21/2022	0
1/23/2022	0
1/26/2022	0
1/27/2022	0
1/29/2022	0
1/31/2022	0

Example diagram of the use of the inlunarweektodate function, using a chart expression determining whether a transaction takes place in the lunar week that contains the 10th of January. — inlunarweektodate() function, chart object example

The in_lunar_week_to_date measure is created in the chart object by using the inlunarweektodate() function and passing the date field, a hard-coded date for January 10 as our base_date, and an offset of 0 as the function’s arguments.

Because lunar weeks begin on January 1, January 10 would fall in the lunar week that begins on January 8. Additionally, since we are using the inlunarweektodate() function, that lunar week would then terminate on the 10th. Therefore, any transactions that occur between those two dates in January would return a Boolean value of TRUE. This is validated in the results table.

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 Products.
Information consisting of product ID, manufacture date, and cost price.

It has been identified that due to equipment error, products that were manufactured in the lunar week of January 12 were defective. The issue was resolved on January 13. The end user would like a chart object that displays, by week, the status of whether the products manufactured ‘defective’ or ‘faultless’ and the cost of the products manufactured in that week.

Load script

SET TimestampFormat='M/D/YYYY h:mm:ss[.fff]';

Products:
Load
*
Inline 
[
product_id,manufacture_date,cost_price
8188,'01/02/2022 12:22:06',37.23
8189,'01/05/2022 01:02:30',17.17
8190,'01/06/2022 15:36:20',88.27
8191,'01/08/2022 10:58:35',57.42
8192,'01/09/2022 08:53:32',53.80
8193,'01/10/2022 21:13:01',82.06
8194,'01/11/2022 00:57:13',40.39
8195,'01/12/2022 09:26:02',87.21
8196,'01/13/2022 15:05:09',95.93
8197,'01/14/2022 18:44:57',45.89
8198,'01/15/2022 06:10:46',36.23
8199,'01/16/2022 06:39:27',25.66
8200,'01/17/2022 10:44:16',82.77
8201,'01/18/2022 18:48:17',69.98
8202,'01/26/2022 04:36:03',76.11
8203,'01/27/2022 08:07:49',25.12
8204,'01/28/2022 12:24:29',46.23
8205,'01/30/2022 11:56:56',84.21
8206,'01/30/2022 14:40:19',96.24
8207,'01/31/2022 05:28:21',67.67
];

Results

Do the following:

Load the data and open a sheet. Create a new table.
Create a dimension to show the week names:

=weekname(manufacture_date)
Next, create a dimension which uses the inlunarweektodate() function to identify which of the products are defective and which are faultless:

=if(inlunarweektodate(manufacture_date,makedate(2022,01,12),0),'Defective','Faultless')
Create a measure to sum the cost_price of the products:

=sum(cost_price)
Set the measure's Number formatting to Money.

Results table
=lunarweekname(manufacture_date)	=if(InLunarWeekToDate(manufacture_date,makedate(2022,01,12),0),'Defective','Faultless')	=Sum(cost_price)
2022/01	Faultless	$142.67
2022/02	Defective	$320.88
2022/02	Faultless	$141.82
2022/03	Faultless	$214.64
2022/04	Faultless	$147.46
2022/05	Faultless	$248.12

The inlunarweektodate() function returns a Boolean value when evaluating the manufacturing dates of each of the products. For those that return a Boolean value of TRUE, it marks the products as ‘Defective’. For any product returning a value of FALSE, and therefore not made in the lunar week up to January 12, it marks the products as ‘Faultless’.

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