indaytotime - script and chart function

This function returns True if timestamp lies inside the part of day containing base_timestamp up until and including the exact millisecond of base_timestamp.

Syntax:

InDayToTime (timestamp, base_timestamp, period_no[, day_start])

The indaytotime() function returns a Boolean result depending on when a timestamp value occurs during the segment of the day. The start boundary of this segment is the start of the day, which is set as midnight by default; the start of the day can be modified by the day_start argument of the indaytotime() function. The end boundary of the day segment is determined by a base_timestamp argument of the function.

Diagram showing the relations between the variables of the indaytotime function. — Diagram of indaytotime function.

When to use it

The indaytotime() function returns a Boolean result. Typically, this type of function will be used as a condition in an if expression. The indaytotime() function returns an aggregation or calculation depending on if a timestamp occurred in the segment of the day up to and including the time of the base timestamp.

For example, the indaytotime() function can be used to show the sum of ticket sales for shows that have taken place so far today.

Return data type: Boolean

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

Arguments
Argument	Description
timestamp	The date and time that you want to compare with base_timestamp.
base_timestamp	Date and time that is used to evaluate the timestamp.
period_no	The day can be offset by period_no. period_no is an integer, where the value 0 indicates the day which contains base_timestamp. Negative values in period_no indicate preceding days and positive values indicate succeeding days.
day_start	(optional) If you want to work with days not starting midnight, indicate an offset as a fraction of a day in day_start. For example, use 0.125 to denote 3 AM.

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.

Function examples
Example	Result
indaytotime ('01/12/2006 12:23:00 PM', '01/12/2006 11:59:00 PM', 0)	Returns True
indaytotime ('01/12/2006 12:23:00 PM', '01/12/2006 12:00:00 AM', 0)	Returns False
indaytotime '01/11/2006 12:23:00 PM', '01/12/2006 11:59:00 PM', -1)	Returns True

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 period between January 4 and 5 is loaded into a table called 'Transactions'.
A date field which is provided in the TimeStamp system variable (M/D/YYYY h:mm:ss[.fff] TT) format.
A preceding load which contains the indaytotime() function which is set as the 'in_day_to_time', field that determines whether each of the transactions take place before 9:00 AM.

Load script

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

Transactions:
    Load
        *,
        indaytotime(date,'01/05/2022 9:00:00 AM',0) as in_day_to_time
        ;
Load
*
Inline 
[
id,date,amount
8188,'01/04/2022 3:41:54 AM',25.66
8189,'01/04/2022 4:19:43 AM',87.21
8190,'01/04/2022 4:53:47 AM',53.80
8191,'01/04/2022 8:38:53 AM',69.98
8192,'01/04/2022 10:37:52 AM',57.42
8193,'01/04/2022 1:54:10 PM',45.89
8194,'01/04/2022 5:53:23 PM',82.77
8195,'01/04/2022 8:13:26 PM',36.23
8196,'01/04/2022 10:00:49 PM',76.11
8197,'01/05/2022 7:45:37 AM',82.06
8198,'01/05/2022 8:44:36 AM',17.17
8199,'01/05/2022 11:26:08 AM',40.39
8200,'01/05/2022 6:43:08 PM',37.23
8201,'01/05/2022 10:54:10 PM',88.27
8202,'01/05/2022 11:09:09 PM',95.93
];

Results

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

date
in_day_to_time

Results table
date	in_day_to_time
01/04/2022 3:41:54 AM	0
01/04/2022 4:19:43 AM	0
01/04/2022 04:53:47 AM	0
01/04/2022 8:38:53 AM	0
01/04/2022 10:37:52 AM	0
01/04/2022 1:54:10 PM	0
01/04/2022 5:53:23 PM	0
01/04/2022 8:13:26 PM	0
01/04/2022 10:00:49 PM	0
01/05/2022 7:45:37 AM	-1
01/05/2022 8:44:36 AM	-1
01/05/2022 11:26:08 AM	0
01/05/2022 6:43:08 PM	0
01/05/2022 10:54:10 PM	0
01/05/2022 11:09:09 PM	0

Diagram showing indaytotime() function with a 9:00 limit. — Example 1 diagram of indaytotime function with 9:00 AM limit..

The in_day_to_time field is created in the preceding load statement by using the indaytotime() function and passing the date field, a hard-coded timestamp for 9:00 AM January 5 and an offset of 0 as the function’s arguments. Any transactions that occur between midnight and 9:00 AM on January 5 return TRUE.

Example 2 – period_no

Overview

The load script uses the same dataset and scenario that were used in the first example.

However, in this example, you will calculate whether the transaction date occurred one day before 9:00 AM on January 5.

Load script

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

Transactions:
    Load
        *,
        indaytotime(date,'01/05/2022 9:00:00 AM', -1) as in_day_to_time
        ;
Load
*
Inline 
[
id,date,amount
8188,'01/04/2022 3:41:54 AM',25.66
8189,'01/04/2022 4:19:43 AM',87.21
8190,'01/04/2022 4:53:47 AM',53.80
8191,'01/04/2022 8:38:53 AM',69.98
8192,'01/04/2022 10:37:52 AM',57.42
8193,'01/04/2022 1:54:10 PM',45.89
8194,'01/04/2022 5:53:23 PM',82.77
8195,'01/04/2022 8:13:26 PM',36.23
8196,'01/04/2022 10:00:49 PM',76.11
8197,'01/05/2022 7:45:37 AM',82.06
8198,'01/05/2022 8:44:36 AM',17.17
8199,'01/05/2022 11:26:08 AM',40.39
8200,'01/05/2022 6:43:08 PM',37.23
8201,'01/05/2022 10:54:10 PM',88.27
8202,'01/05/2022 11:09:09 PM',95.93
];

Results

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

date
in_day_to_time

Results table
date	in_day_to_time
01/04/2022 3:41:54 AM	-1
01/04/2022 4:19:43 AM	-1
01/04/2022 04:53:47 AM	-1
01/04/2022 8:38:53 AM	-1
01/04/2022 10:37:52 AM	0
01/04/2022 1:54:10 PM	0
01/04/2022 5:53:23 PM	0
01/04/2022 8:13:26 PM	0
01/04/2022 10:00:49 PM	0
01/05/2022 7:45:37 AM	0
01/05/2022 8:44:36 AM	0
01/05/2022 11:26:08 AM	0
01/05/2022 6:43:08 PM	0
01/05/2022 10:54:10 PM	0
01/05/2022 11:09:09 PM	0

Diagram showing the indaytotime () function that uses a period_no of -1 to return transactions from January 4. — Example 2 diagram of indaytotime function with transactions from January 4.

In this example, because an offset of -1 was used as the offset argument in the indaytotime() function, the function determines whether each transaction date took place before 9:00 AM on January 4. This can be verified in the output table where a transaction returns a Boolean result of TRUE.

Example 3 – day_start

Overview

The same dataset and scenario as the first example are used.

However, in this example, the company policy is that the workday begins and ends at 8AM.

Load script

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

Transactions:
    Load
        *,
        indaytotime(date,'01/05/2022 9:00:00 AM', 0,8/24) as in_day_to_time
        ;
Load
*
Inline 
[
id,date,amount
8188,'01/04/2022 3:41:54 AM',25.66
8189,'01/04/2022 4:19:43 AM',87.21
8190,'01/04/2022 4:53:47 AM',53.80
8191,'01/04/2022 8:38:53 AM',69.98
8192,'01/04/2022 10:37:52 AM',57.42
8193,'01/04/2022 1:54:10 PM',45.89
8194,'01/04/2022 5:53:23 PM',82.77
8195,'01/04/2022 8:13:26 PM',36.23
8196,'01/04/2022 10:00:49 PM',76.11
8197,'01/05/2022 7:45:37 AM',82.06
8198,'01/05/2022 8:44:36 AM',17.17
8199,'01/05/2022 11:26:08 AM',40.39
8200,'01/05/2022 6:43:08 PM',37.23
8201,'01/05/2022 10:54:10 PM',88.27
8202,'01/05/2022 11:09:09 PM',95.93
];

Results

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

date
in_day_to_time

Results table
date	in_day_to_time
01/04/2022 3:41:54 AM	0
01/04/2022 4:19:43 AM	0
01/04/2022 04:53:47 AM	0
01/04/2022 8:38:53 AM	0
01/04/2022 10:37:52 AM	0
01/04/2022 1:54:10 PM	0
01/04/2022 5:53:23 PM	0
01/04/2022 8:13:26 PM	0
01/04/2022 10:00:49 PM	0
01/05/2022 7:45:37 AM	0
01/05/2022 8:44:36 AM	-1
01/05/2022 11:26:08 AM	0
01/05/2022 6:43:08 PM	0
01/05/2022 10:54:10 PM	0
01/05/2022 11:09:09 PM	0

Diagram showing the indaytotime() function that shows transactions from 8:00am to 9:00am. — Example 3 diagram of indaytotime function with transactions from 8:00 AM to 9:00 AM.,

Because the start_day argument of 8/24, which equates to 8:00 AM, is used in the indaytotime() function, each day begins and ends at 8:00 AM. Therefore, the indaytotime() function will return a Boolean result of TRUE for any transaction that took place between 8:00 AM and 9:00 AM on January 5.

Example 4 – Chart object

Overview

The same dataset and scenario as the first example are used.

However, in this example, the dataset is unchanged and loaded into the application. You will calculate to determine if a transaction takes place on January 5 before 9:00 AM by creating a measure in a chart object.

Load script


Transactions:
Load
*
Inline 
[
id,date,amount
8188,'01/04/2022 3:41:54 AM',25.66
8189,'01/04/2022 4:19:43 AM',87.21
8190,'01/04/2022 4:53:47 AM',53.80
8191,'01/04/2022 8:38:53 AM',69.98
8192,'01/04/2022 10:37:52 AM',57.42
8193,'01/04/2022 1:54:10 PM',45.89
8194,'01/04/2022 5:53:23 PM',82.77
8195,'01/04/2022 8:13:26 PM',36.23
8196,'01/04/2022 10:00:49 PM',76.11
8197,'01/05/2022 7:45:37 AM',82.06
8198,'01/05/2022 8:44:36 AM',17.17
8199,'01/05/2022 11:26:08 AM',40.39
8200,'01/05/2022 6:43:08 PM',37.23
8201,'01/05/2022 10:54:10 PM',88.27
8202,'01/05/2022 11:09:09 PM',95.93
];

Results

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

date.

To determine if a transaction takes place on January 5 before 9:00 AM, create the following measure:

=indaytotime(date,'01/05/2022 9:00:00 AM',0)

Results table
date	=indaytotime(date,'01/05/2022 9:00:00 AM',0)
01/04/2022 3:41:54 AM	0
01/04/2022 4:19:43 AM	0
01/04/2022 04:53:47 AM	0
01/04/2022 8:38:53 AM	0
01/04/2022 10:37:52 AM	0
01/04/2022 1:54:10 PM	0
01/04/2022 5:53:23 PM	0
01/04/2022 8:13:26 PM	0
01/04/2022 10:00:49 PM	0
01/05/2022 7:45:37 AM	-1
01/05/2022 8:44:36 AM	-1
01/05/2022 11:26:08 AM	0
01/05/2022 6:43:08 PM	0
01/05/2022 10:54:10 PM	0
01/05/2022 11:09:09 PM	0

The in_day_to_time measure is created in the chart object by using the indaytotime() function and passing the date field, a hard-coded timestamp for 9:00 AM on January 5 and an offset of 0 as the function’s arguments. Any transactions that occur between midnight and 9:00 AM on January 5 return TRUE. This is validated in the results table.

Example 5 – Scenario

Overview

In this example, a dataset containing ticket sales for a local cinema is loaded into a table called Ticket_Sales. Today is May 3, 2022 and it is 11:00 AM.

The user would like a KPI chart object to show the revenue earned from all shows that have taken place so far today.

Load script

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

Ticket_Sales:
Load
*
Inline 
[
sale ID, show time, ticket price
1,05/01/2022 09:30:00 AM,10.50
2,05/03/2022 05:30:00 PM,21.00
3,05/03/2022 09:30:00 AM,10.50
4,05/03/2022 09:30:00 AM,31.50
5,05/03/2022 09:30:00 AM,10.50
6,05/03/2022 12:00:00 PM,42.00
7,05/03/2022 12:00:00 PM,10.50
8,05/03/2022 05:30:00 PM,42.00
9,05/03/2022 08:00:00 PM,31.50
10,05/04/2022 10:30:00 AM,31.50
11,05/04/2022 12:00:00 PM,10.50
12,05/04/2022 05:30:00 PM,10.50
13,05/05/2022 05:30:00 PM,21.00
14,05/06/2022 12:00:00 PM,21.00
15,05/07/2022 09:30:00 AM,42.00
16,05/07/2022 10:30:00 AM,42.00
17,05/07/2022 10:30:00 AM,10.50
18,05/07/2022 05:30:00 PM,10.50
19,05/08/2022 05:30:00 PM,21.00
20,05/11/2022 09:30:00 AM,10.50
];

Results

Do the following:

Create a KPI object.
Create a measure that will show the sum of all ticket sales for shows that have taken place today so far using the indaytotime() function:

=sum(if(indaytotime([show time],'05/03/2022 11:00:00 AM',0),[ticket price],0))

Create a label for the KPI object, ‘Current Revenue’.
Set the measure’s Number Formatting to Money.

The sum total of ticket sales up to 11:00 AM on May 3, 2022 is $52.50.

The indaytotime () function returns a Boolean value when comparing the show times of each of the ticket sales to the current time ('05/03/2022 11:00:00 AM’). For any show on May 3 before 11:00 AM, the indaytotime () function returns a Boolean value of TRUE and its ticket price will be included in the sum total.

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