inweektodate - script and chart function

This function returns True if timestamp lies inside the part of week containing base_date up until and including the last millisecond of base_date.

Syntax:

InWeekToDate (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 the range of date within which the inweektodate function will return a value of TRUE. In this case, it returns TRUE for certain days of the week, and FALSE for others. — Diagram of inweektodate function

The inweektodate() function uses the base_date parameter to identify a maximum boundary date of a week segment, as well as its corresponding date for the start of the week, which is based on the FirstWeekDay system variable (or user-defined first_week_day parameter). Once this week segment has been defined, the function will then return Boolean results when comparing the prescribed date values to that segment.

When to use it

The inweektodate() function returns a Boolean result. Typically, this type of function will be used as a condition in an if expression. This will return an aggregation or calculation dependent on whether a date evaluated occurred during the week in question up to and including a particular date.

For example, the inweektodate() function can be used to calculate all sales made during a specified week up to a particular date.

Arguments
Argument	Description
timestamp	The date that you want to compare with base_date.
base_date	Date that is used to evaluate the week.
period_no	The week can be offset by period_no. period_no is an integer, where the value 0 indicates the week which contains base_date. Negative values in period_no indicate preceding weeks and positive values indicate succeeding weeks.
first_week_day	By default, the first day of the week is Sunday (as determined by the FirstWeekDay system variable), starting at midnight between Saturday and Sunday. The first_week_day parameter supersedes the FirstWeekDay variable. To indicate the week starting on another day, specify a flag between 0 and 6. For a week starting on Monday and ending on Sunday, use a flag of 0 for Monday, 1 for Tuesday, 2 for Wednesday, 3 for Thursday, 4 for Friday, 5 for Saturday, and 6 for Sunday.

Function examples
Example	Interaction
inweektodate('01/12/2006', '01/12/2006', 0)	Returns TRUE.
inweektodate('01/12/2006', '01/11/2006', 0)	Returns FALSE.
inweektodate('01/12/2006', '01/18/2006', -1)	Returns FALSE. Because period_no is specified as -1, the effective data that timestamp is measured against is 01/11/2006.
inweektodate('01/11/2006', '01/12/2006', 0, 3 )	Returns FALSE, since first_week_day is specified as 3 (Thursday), which makes 01/12/2006 the first day of the week following the week containing 01/12/2006.

These topics may help you work with this function:

Related topics
Topic	Default Flag / Value	Description
FirstWeekDay	6 / Sunday	Defines the start day of each week.

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. For more information, see Modifying regional settings for apps and scripts.

Default regional settings in apps are based on the user profile. These regional format settings are not related to the language displayed in the Qlik Cloud user interface. Qlik Cloud will be displayed in the same language as the browser you are using.

If you are an app creator, you can set the default region for apps you create. For more information, see Setting your preferred regional settings for creating apps and scripts in Qlik Cloud Analytics.

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 2022, which is loaded into a table called Transactions.
The data field provided in the TimestampFormat='M/D/YYYY h:mm:ss[.fff]' format.
The creation of a field, in_week_to_date, which determines which transactions took place in the week up until January 14, 2022.
The creation of an additional field, named weekday, using the weekday() function. This new field is created to show which day of the week corresponds to each date.

Load script

SET TimestampFormat='M/D/YYYY h:mm:ss[.fff]';
SET FirstWeekDay=6;
Transactions:
    Load
        *,
        weekday(date) as week_day,
        inweektodate(date,'01/14/2022', 0) as in_week_to_date
        ;
Load
*
Inline
[
id,date,amount
8188,'2022-01-02 12:22:06',37.23
8189,'2022-01-05 01:02:30',17.17
8190,'2022-01-06 15:36:20',88.27
8191,'2022-01-08 10:58:35',57.42
8192,'2022-01-09 08:53:32',53.80
8193,'2022-01-10 21:13:01',82.06
8194,'2022-01-11 00:57:13',40.39
8195,'2022-01-12 09:26:02',87.21
8196,'2022-01-13 15:05:09',95.93
8197,'2022-01-14 18:44:57',45.89
8198,'2022-01-15 06:10:46',36.23
8199,'2022-01-16 06:39:27',25.66
8200,'2022-01-17 10:44:16',82.77
8201,'2022-01-18 18:48:17',69.98
8202,'2022-01-26 04:36:03',76.11
8203,'2022-01-27 08:07:49',25.12
8204,'2022-01-28 12:24:29',46.23
8205,'2022-01-30 11:56:56',84.21
8206,'2022-01-30 14:40:19',96.24
8207,'2022-01-31 05:28:21',67.67
];

Results

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

date
week_day
in_week_to_date

Results table
date	week_day	in_week_to_date
2022-01-02 12:22:06	Sun	0
2022-01-05 01:02:30	Wed	0
2022-01-06 15:36:20	Thu	0
2022-01-08 10:58:35	Sat	0
2022-01-09 08:53:32	Sun	-1
2022-01-10 21:13:01	Mon	-1
2022-01-11 00:57:13	Tue	-1
2022-01-12 09:26:02	Wed	-1
2022-01-13 15:05:09	Thu	-1
2022-01-14 18:44:57	Fri	-1
2022-01-15 06:10:46	Sat	0
2022-01-16 06:39:27	Sun	0
2022-01-17 10:44:16	Mon	0
2022-01-18 18:48:17	Tue	0
2022-01-26 04:36:03	Wed	0
2022-01-27 08:07:49	Thu	0
2022-01-28 12:24:29	Fri	0
2022-01-30 11:56:56	Sun	0
2022-01-30 14:40:19	Sun	0
2022-01-31 05:28:21	Mon	0

The in_week_to_date field is created in the preceding load statement by using the inweektodate() function. The first argument provided identifies which field is being evaluated. The second argument is a hard-coded date for January 14, which is the base_date that identifies which week to segment and defines the end boundary of that segment. A period_no of 0 is the final argument, meaning that the function is not comparing weeks preceding or following the segmented week.

The FirstWeekDay system variable determines that weeks begin on a Sunday and end on a Saturday. Therefore, January would be broken into weeks according to the diagram below, with the dates between January 9 and 14 providing the valid period for the inweekdodate() calculation:

Diagram showing the range of dates for which the inweektodate function will return a value of TRUE. In this example, the dates returning a TRUE value are January 9 to 14, inclusive. — Calendar diagram showing transaction dates which would return a Boolean result of TRUE

Any transaction that occurs in between January 9 and 14 returns a Boolean result of TRUE. Transactions before and after the dates return a Boolean result of FALSE.

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, prev_week_to_date, that determines which transactions took place a full week before the week segment ending on January 14, 2022.
The creation of an additional field, named weekday, using the weekday() function. This is to show which day of the week corresponds to each date.

Load script

SET FirstWeekDay=6;
SET TimestampFormat='M/D/YYYY h:mm:ss[.fff]';
Transactions:
    Load
        *,
        weekday(date) as week_day,
        inweektodate(date,'01/14/2022', -1) as prev_week_to_date
        ;
Load
*
Inline
[
id,date,amount
8188,'2022-01-02 12:22:06',37.23
8189,'2022-01-05 01:02:30',17.17
8190,'2022-01-06 15:36:20',88.27
8191,'2022-01-08 10:58:35',57.42
8192,'2022-01-09 08:53:32',53.80
8193,'2022-01-10 21:13:01',82.06
8194,'2022-01-11 00:57:13',40.39
8195,'2022-01-12 09:26:02',87.21
8196,'2022-01-13 15:05:09',95.93
8197,'2022-01-14 18:44:57',45.89
8198,'2022-01-15 06:10:46',36.23
8199,'2022-01-16 06:39:27',25.66
8200,'2022-01-17 10:44:16',82.77
8201,'2022-01-18 18:48:17',69.98
8202,'2022-01-26 04:36:03',76.11
8203,'2022-01-27 08:07:49',25.12
8204,'2022-01-28 12:24:29',46.23
8205,'2022-01-30 11:56:56',84.21
8206,'2022-01-30 14:40:19',96.24
8207,'2022-01-31 05:28:21',67.67
];

Results

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

date
week_day
prev_week_to_date

Results table
date	week_day	prev_week_to_date
2022-01-02 12:22:06	Sun	-1
2022-01-05 01:02:30	Wed	-1
2022-01-06 15:36:20	Thu	-1
2022-01-08 10:58:35	Sat	0
2022-01-09 08:53:32	Sun	0
2022-01-10 21:13:01	Mon	0
2022-01-11 00:57:13	Tue	0
2022-01-12 09:26:02	Wed	0
2022-01-13 15:05:09	Thu	0
2022-01-14 18:44:57	Fri	0
2022-01-15 06:10:46	Sat	0
2022-01-16 06:39:27	Sun	0
2022-01-17 10:44:16	Mon	0
2022-01-18 18:48:17	Tue	0
2022-01-26 04:36:03	Wed	0
2022-01-27 08:07:49	Thu	0
2022-01-28 12:24:29	Fri	0
2022-01-30 11:56:56	Sun	0
2022-01-30 14:40:19	Sun	0
2022-01-31 05:28:21	Mon	0

A period_no value of -1 indicates that the inweektodate () function compares the input quarter segment to the preceding week. The week segment initially equates to between January 9 and January 14. The period_no then offsets both the start and end boundary of this segment to one week earlier, causing the date boundaries to become January 2 to January 7.

Therefore, any transaction that occurs between January 2 and 8 (not including January 8 itself) 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 creation of a field, in_week_to_date, that determines which transactions took place in the week up until January 14, 2022.
The creation of an additional field, named weekday, using the weekday() function. This is to show which day of the week corresponds to each date.

In this example, we consider Monday as the first day of the week.

Load script

SET FirstWeekDay=6;
SET TimestampFormat='M/D/YYYY h:mm:ss[.fff]';

Transactions:
    Load
        *,
        weekday(date) as week_day,
        inweektodate(date,'01/14/2022', 0, 0) as in_week_to_date
        ;
Load
*
Inline
[
id,date,amount
8188,'2022-01-02 12:22:06',37.23
8189,'2022-01-05 01:02:30',17.17
8190,'2022-01-06 15:36:20',88.27
8191,'2022-01-08 10:58:35',57.42
8192,'2022-01-09 08:53:32',53.80
8193,'2022-01-10 21:13:01',82.06
8194,'2022-01-11 00:57:13',40.39
8195,'2022-01-12 09:26:02',87.21
8196,'2022-01-13 15:05:09',95.93
8197,'2022-01-14 18:44:57',45.89
8198,'2022-01-15 06:10:46',36.23
8199,'2022-01-16 06:39:27',25.66
8200,'2022-01-17 10:44:16',82.77
8201,'2022-01-18 18:48:17',69.98
8202,'2022-01-26 04:36:03',76.11
8203,'2022-01-27 08:07:49',25.12
8204,'2022-01-28 12:24:29',46.23
8205,'2022-01-30 11:56:56',84.21
8206,'2022-01-30 14:40:19',96.24
8207,'2022-01-31 05:28:21',67.67
];

Results

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

date
week_day
in_week_to_date

Results table
date	week_day	in_week_to_date
2022-01-02 12:22:06	Sun	0
2022-01-05 01:02:30	Wed	0
2022-01-06 15:36:20	Thu	0
2022-01-08 10:58:35	Sat	0
2022-01-09 08:53:32	Sun	0
2022-01-10 21:13:01	Mon	-1
2022-01-11 00:57:13	Tue	-1
2022-01-12 09:26:02	Wed	-1
2022-01-13 15:05:09	Thu	-1
2022-01-14 18:44:57	Fri	-1
2022-01-15 06:10:46	Sat	0
2022-01-16 06:39:27	Sun	0
2022-01-17 10:44:16	Mon	0
2022-01-18 18:48:17	Tue	0
2022-01-26 04:36:03	Wed	0
2022-01-27 08:07:49	Thu	0
2022-01-28 12:24:29	Fri	0
2022-01-30 11:56:56	Sun	0
2022-01-30 14:40:19	Sun	0
2022-01-31 05:28:21	Mon	0

By using 0 as the first_week_day argument in the inweektodate() function, the function argument supersedes the FirstWeekDay system variable and sets Monday as the first day of the week.

Diagram showing the range of transaction dates for which the inweektodate function will return a value of TRUE. In this example, the dates returning a TRUE value are January 10 to 14, inclusive. — Calendar diagram showing transaction dates which would return a Boolean result of TRUE

Therefore, any transaction that occurs in between January 10 and 14 will return a Boolean result of TRUE, while transactions with dates outside these boundaries will return a value of FALSE.

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 which transactions took place in the week up until January 14, 2022 is created as a measure in the chart object.

Load script

SET DateFormat='MM/DD/YYYY';

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

Results

Do the following:

Load the data and open a sheet. Create a new table and add this field as a dimension: date.
To calculate whether transactions took place in the same week up until the 14th of January, create the following measure:

=inweektodate(date,'01/14/2022',0)
To show which day of the week corresponds to each date, create an additional measure:

=weekday(date)

Results table
date	week_day	in_week_to_date
2022-01-02 12:22:06	Sun	0
2022-01-05 01:02:30	Wed	0
2022-01-06 15:36:20	Thu	0
2022-01-08 10:58:35	Sat	0
2022-01-09 08:53:32	Sun	-1
2022-01-10 21:13:01	Mon	-1
2022-01-11 00:57:13	Tue	-1
2022-01-12 09:26:02	Wed	-1
2022-01-13 15:05:09	Thu	-1
2022-01-14 18:44:57	Fri	-1
2022-01-15 06:10:46	Sat	0
2022-01-16 06:39:27	Sun	0
2022-01-17 10:44:16	Mon	0
2022-01-18 18:48:17	Tue	0
2022-01-26 04:36:03	Wed	0
2022-01-27 08:07:49	Thu	0
2022-01-28 12:24:29	Fri	0
2022-01-30 11:56:56	Sun	0
2022-01-30 14:40:19	Sun	0
2022-01-31 05:28:21	Mon	0

The in_week_to_date field is created as a measure in the chart object using the inweektodate() function. The first argument provided identifies which field is being evaluated. The second argument is a hard-coded date for January 14, which is the base_date that identifies which week to segment and defines the end boundary of that segment. A period_no of 0 is the final argument, meaning that the function is not comparing weeks preceding or following the segmented week.

Any transaction that occurs in between January 9 and 14 returns a Boolean result of TRUE. Transactions before and after the dates return a Boolean result of FALSE.

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 concerning product ID, manufacture date, and cost price.

It has been identified that due to equipment error, products that were manufactured in the 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 are ‘defective’ or ‘faultless’, and the cost of the products manufactured in that week.

Load script

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

=if(inWeektodate(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
weekname(manufacture_date)	if(inweektodate(manufacture_date,makedate(2022,01,12),0),'Defective','Faultless')	Sum(cost_price)
2022/02	Faultless	$200.09
2022/03	Defective	$263.46
2022/03	Faultless	$178.05
2022/04	Faultless	$178.41
2022/05	Faultless	$147.46
2022/06	Faultless	$248.12

The inweektodate() 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 week up to January 12, it marks the products as ‘Faultless’.

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inweektodate - script and chart function

When to use it

Regional settings

Example 1 – No additional arguments

Example 2 – period_no

Example 3 – first_week_day

Example 4 – Chart object example

Example 5 – Scenario

Variable

Aggregation

Dataset

Field

Preceding load

Data load editor

Load script

Measure

Sheet

Dimension

In Analytics Services:

In Data Integration:

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