monthsend - script and chart function

This function returns a value corresponding to the timestamp of the last millisecond of the month, bi-month, quarter, four-month period, or half-year containing a base date. It is also possible to find the timestamp for the end of a previous or following time period. The default output format is the DateFormat set in the script.

Syntax:

MonthsEnd(n_months, date[, period_no [, first_month_of_year]])

Return data type: dual

Diagram showing how monthsend function can be used to identify the latest timestamp of chosen period. — Diagram of monthsend function.

Arguments
Argument	Description
n_months	The number of months that defines the period. An integer or expression that resolves to an integer that must be one of: 1 (equivalent to the inmonth() function), 2 (bi-month), 3 (equivalent to the inquarter()function), 4 (four-month period), or 6 (half year).
date	The date or timestamp to evaluate.
period_no	The period can be offset by period_no, an integer, or expression resolving to an integer, where the value 0 indicates the period that contains base_date. Negative values in period_no indicate preceding periods and positive values indicate succeeding periods.
first_month_of_year	If you want to work with (fiscal) years not starting in January, indicate a value between 2 and 12 in first_month_of_year.

The monthsend() function divides the year into segments based on the n_months argument provided. It then evaluates what segment each date provided falls into and returns the last millisecond, in date format, of that segment. The function can return the end timestamp from preceding or following segments as well as redefine the first month of the year.

The following segments of the year are available in the function as n_month arguments.

n_month arguments
Period	Number of months
month	1
bi-month	2
quarter	3
four months	4
half-year	6

When to use it

The monthsend() function is used as part of an expression when the user would like the calculation to use the fraction of the month that has elapsed so far. The user has the opportunity, using a variable to select the period of their choosing. For example, the monthsend() can provide an input variable to let the user calculate the total interest not yet incurred during the month, quarter, or half-year.

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
monthsend(4, '07/19/2013')	Returns 08/31/2013.
monthsend(4, '10/19/2013', -1)	Returns 08/31/2013.
monthsend(4, '10/19/2013', 0, 2)	Returns 01/31/2014. Because the start of the year becomes month 2.

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 is loaded into a table called ‘Transactions’.
A date field provided in the DateFormat system variable (MM/DD/YYYY) format.
A preceding load statement containing:
- The monthsend function which is set as the field, ‘bi_monthly_end’. This groups transactions into bi-monthly segments.
- The timestamp function which returns the starting timestamp of the segment for each transaction.

Load script

SET DateFormat='MM/DD/YYYY'; 

Transactions: 
    Load 
    *, 
    monthsend(2,date) as bi_monthly_end, 
    timestamp(monthsend(2,date)) as bi_monthly_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 
8195,5/22/2022,87.21 
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:

id
date
bi_monthly_end
bi_monthly_end_timestamp

Results table
id	date	bi_monthly_end	bi_monthly_end_timestamp
8188	1/7/2022	02/28/2022	2/28/2022 11:59:59 PM
8189	1/19/2022	02/28/2022	2/28/2022 11:59:59 PM
8190	2/5/2022	02/28/2022	2/28/2022 11:59:59 PM
8191	2/28/2022	02/28/2022	2/28/2022 11:59:59 PM
8192	3/16/2022	04/30/2022	4/30/2022 11:59:59 PM
8193	4/1/2022	04/30/2022	4/30/2022 11:59:59 PM
8194	5/7/2022	06/30/2022	6/30/2022 11:59:59 PM
8195	5/22/2022	06/30/2022	6/30/2022 11:59:59 PM
8196	6/15/2022	06/30/2022	6/30/2022 11:59:59 PM
8197	6/26/2022	06/30/2022	6/30/2022 11:59:59 PM
8198	7/9/2022	08/31/2022	8/31/2022 11:59:59 PM
8199	7/22/2022	08/31/2022	8/31/2022 11:59:59 PM
8200	7/23/2022	08/31/2022	8/31/2022 11:59:59 PM
8201	7/27/2022	08/31/2022	8/31/2022 11:59:59 PM
8202	8/2/2022	08/31/2022	8/31/2022 11:59:59 PM
8203	8/8/2022	08/31/2022	8/31/2022 11:59:59 PM
8204	8/19/2022	08/31/2022	8/31/2022 11:59:59 PM
8205	9/26/2022	10/31/2022	10/31/2022 11:59:59 PM
8206	10/14/2022	10/31/2022	10/31/2022 11:59:59 PM
8207	10/29/2022	10/31/2022	10/31/2022 11:59:59 PM

The ‘bi_monthly_end’ field is created in the preceding load statement by using the monthsend() function. The first argument provided is 2, dividing the year into bi-monthly segments. The second argument identifies which field is being evaluated.

Diagram showing how the monthsend function identifies the selected bi-monthly segment. — Diagram of monthsend function with bi-monthly segments.

Transaction 8195 takes place on May 22. The monthsend() function initially divides the year into bi-monthly segments. Transaction 8195 falls into the segment between May 1 and June 30. As a result, the function returns the last millisecond of this segment, 06/30/2022 11:59:59 PM.

Example 2 - period_no

Overview

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

In this example, the task is to create a field, ‘prev_bi_monthly_end’, that returns the first millisecond of the bi-monthly segment before the transaction took place.

Load script

SET DateFormat='MM/DD/YYYY'; 

Transactions: 
    Load 
    *, 
    monthsend(2,date,-1) as prev_bi_monthly_end, 
    timestamp(monthsend(2,date,-1)) as prev_bi_monthly_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 
8195,5/22/2022,87.21 
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:

id
date
prev_bi_monthly_end
prev_bi_monthly_end_timestamp

Results table
id	date	prev_bi_monthly_end	prev_bi_monthly_end_timestamp
8188	1/7/2022	12/31/2021	12/31/2021 11:59:59 PM
8189	1/19/2022	12/31/2021	12/31/2021 11:59:59 PM
8190	2/5/2022	12/31/2021	12/31/2021 11:59:59 PM
8191	2/28/2022	12/31/2021	12/31/2021 11:59:59 PM
8192	3/16/2022	02/28/2022	2/28/2022 11:59:59 PM
8193	4/1/2022	02/28/2022	2/28/2022 11:59:59 PM
8194	5/7/2022	04/30/2022	4/30/2022 11:59:59 PM
8195	5/22/2022	04/30/2022	4/30/2022 11:59:59 PM
8196	6/15/2022	04/30/2022	4/30/2022 11:59:59 PM
8197	6/26/2022	04/30/2022	4/30/2022 11:59:59 PM
8198	7/9/2022	06/30/2022	6/30/2022 11:59:59 PM
8199	7/22/2022	06/30/2022	6/30/2022 11:59:59 PM
8200	7/23/2022	06/30/2022	6/30/2022 11:59:59 PM
8201	7/27/2022	06/30/2022	6/30/2022 11:59:59 PM
8202	8/2/2022	06/30/2022	6/30/2022 11:59:59 PM
8203	8/8/2022	06/30/2022	6/30/2022 11:59:59 PM
8204	8/19/2022	06/30/2022	6/30/2022 11:59:59 PM
8205	9/26/2022	08/31/2022	8/31/2022 11:59:59 PM
8206	10/14/2022	08/31/2022	8/31/2022 11:59:59 PM
8207	10/29/2022	08/31/2022	8/31/2022 11:59:59 PM

By using -1 as the period_no argument in the monthsend() function, after initially dividing a year into bi-monthly segments, the function returns the last millisecond of the previous bi-monthly segment to when a transaction takes place.

Diagram of monthsend function with a negative one period_no which returns the previous bi-monthly segment. — Diagram of monthsend function that returns the previous bi-monthly segment.

Transaction 8195 occurs in the segment between May and June. As a result, the previous bi-monthly segment was between March 1 and April 30 and so the function returns the last millisecond of this segment, 04/30/2022 11:59:59 PM.

Example 3 – first_month_of_year

Overview

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

In this example, the organizational policy is for April to be the first month of the financial year.

Create a field, ‘bi_monthly_end’, that groups transactions into bi-monthly segments and returns the last millisecond timestamp of the segment for each transaction.

Load script

SET DateFormat='MM/DD/YYYY'; 
						
Transactions: 
    Load 
    *, 
    monthsend(2,date,0,4) as bi_monthly_end, 
    timestamp(monthsend(2,date,0,4)) as bi_monthly_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 
8195,5/22/2022,87.21 
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:

id
date
bi_monthly_end
bi_monthly_end_timestamp

Results table
id	date	bi_monthly_end	bi_monthly_end_timestamp
8188	1/7/2022	01/31/2022	1/31/2022 11:59:59 PM
8189	1/19/2022	01/31/2022	1/31/2022 11:59:59 PM
8190	2/5/2022	03/31/2022	3/31/2022 11:59:59 PM
8191	2/28/2022	03/31/2022	3/31/2022 11:59:59 PM
8192	3/16/2022	03/31/2022	3/31/2022 11:59:59 PM
8193	4/1/2022	05/31/2022	5/31/2022 11:59:59 PM
8194	5/7/2022	05/31/2022	5/31/2022 11:59:59 PM
8195	5/22/2022	05/31/2022	5/31/2022 11:59:59 PM
8196	6/15/2022	07/31/2022	7/31/2022 11:59:59 PM
8197	6/26/2022	07/31/2022	7/31/2022 11:59:59 PM
8198	7/9/2022	07/31/2022	7/31/2022 11:59:59 PM
8199	7/22/2022	07/31/2022	7/31/2022 11:59:59 PM
8200	7/23/2022	07/31/2022	7/31/2022 11:59:59 PM
8201	7/27/2022	07/31/2022	7/31/2022 11:59:59 PM
8202	8/2/2022	09/30/2022	9/30/2022 11:59:59 PM
8203	8/8/2022	09/30/2022	9/30/2022 11:59:59 PM
8204	8/19/2022	09/30/2022	9/30/2022 11:59:59 PM
8205	9/26/2022	09/30/2022	9/30/2022 11:59:59 PM
8206	10/14/2022	11/30/2022	11/30/2022 11:59:59 PM
8207	10/29/2022	11/30/2022	11/30/2022 11:59:59 PM

By using 4 as the first_month_of_year argument in the monthsend() function, the function begins the year on April 1. It then divides the year into bi-monthly segments: Apr-May, Jun-Jul, Aug-Sep, Oct-Nov, Dec-Jan, Feb-Mar.

Diagram of monthsend function with bi-monthly segments and April set as the first month of the year. — Diagram of monthsend function with the first month of the year set as April

Transaction 8195 took place on May 22 and falls into the segment between April 1 and May 31. As a result, the function returns the last millisecond of this segment, 05/31/2022 11:59:59 PM.

Example 4 - Chart object example

Overview

The same dataset and scenario as the first example are used. However in this example, the dataset is unchanged, and loaded into the app.

In this example, the task is to create a calculation that groups transactions into bi-monthly segments and returns the last millisecond timestamp of the segment for each transaction as a measure in a chart object of an app.

Load script

SET DateFormat='MM/DD/YYYY'; 
						
Transactions: 
Load 
* 
Inline  
[ 
id,date,amount 
8188,2/19/2022,37.23 
8189,3/7/2022,17.17
8190,3/30/2022,88.27 
8191,4/5/2022,57.42 
8192,4/16/2022,53.80 
8193,5/1/2022,82.06 
8194,5/7/2022,40.39 
8195,5/22/2022,87.21 
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 fetch the last millisecond timestamp of the bi-monthly segment when the transaction took place, create the following measures:

=monthsEnd(2,date)
=timestamp(monthsend(2,date))

Results table
id	date	=monthsend(2,date)	=timestamp(monthsend(2,date))
8188	1/7/2022	02/28/2022	2/28/2022 11:59:59 PM
8189	1/19/2022	02/28/2022	2/28/2022 11:59:59 PM
8190	2/5/2022	02/28/2022	2/28/2022 11:59:59 PM
8191	2/28/2022	02/28/2022	2/28/2022 11:59:59 PM
8192	3/16/2022	04/30/2022	4/30/2022 11:59:59 PM
8193	4/1/2022	04/30/2022	4/30/2022 11:59:59 PM
8194	5/7/2022	06/30/2022	6/30/2022 11:59:59 PM
8195	5/22/2022	06/30/2022	6/30/2022 11:59:59 PM
8196	6/15/2022	06/30/2022	6/30/2022 11:59:59 PM
8197	6/26/2022	06/30/2022	6/30/2022 11:59:59 PM
8198	7/9/2022	08/31/2022	8/31/2022 11:59:59 PM
8199	7/22/2022	08/31/2022	8/31/2022 11:59:59 PM
8200	7/23/2022	08/31/2022	8/31/2022 11:59:59 PM
8201	7/27/2022	08/31/2022	8/31/2022 11:59:59 PM
8202	8/2/2022	08/31/2022	8/31/2022 11:59:59 PM
8203	8/8/2022	08/31/2022	8/31/2022 11:59:59 PM
8204	8/19/2022	08/31/2022	8/31/2022 11:59:59 PM
8205	9/26/2022	10/31/2022	10/31/2022 11:59:59 PM
8206	10/14/2022	10/31/2022	10/31/2022 11:59:59 PM
8207	10/29/2022	10/31/2022	10/31/2022 11:59:59 PM

The ‘bi_monthly_end’ field is created as a measure in the chart object by using the monthsend() function. The first argument provided is 2, which divides the year into bi-monthly segments. The second argument identifies which field is being evaluated.

Diagram showing how the monthsend function identifies the selected bi-monthly segment using a chart object. — Diagram of monthsend function with bi-monthly segments.

Transaction 8195 takes place on May 22. The monthsend() function initially divides the year into bi-monthly segments. Transaction 8195 falls into the segment between May 1 and June 30. As a result, the function returns the first millisecond of this segment, 06/30/2022 11:59:59 PM.

Example 5 – Scenario

Overview

Open the data load editor and add the load script below to a new tab.

In this example, a dataset is loaded into a table called ‘Employee_Expenses’. The table contains the following fields:

Employee IDs
Employee names
The average daily expense claims of each employee.

The end user would like a chart that displays, by employee id and employee name, the estimated expense claim for the remainder of a period of their own choosing. The financial year begins in January.

Load script

SET vPeriod = 1; 

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

Load the data and open a new sheet.

At the start of the load script, a variable, vPeriod, is created that is tied to the variable input control.

Do the following:

In the assets panel, click Custom objects.
Select Qlik Dashboard bundle, create a Variable input object.
Enter a title for the chart object.
Under Variable, select vPeriod as the name and set the object to show as a Drop down.

Under Values, click Dynamic values. Enter the following:

='1~month|2~bi-month|3~quarter|4~tertial|6~half-year'.

Create a new table and these fields as dimensions:

employee_id
employee_name

To calculate the accumulated interest, create this measure:

=floor(monthsend($(vPeriod),today(1))-today(1))*avg_daily_claim

This measure is dynamic and will produce different table results depending on the date when you load the data.

Set the measure’s Number formatting to Money.

Results table
employee_id	employee_name	=floor(monthsend($(vPeriod),today(1))-today(1))*avg_daily_claim
182	Mark	$1410.00
183	Deryck	$1175.00
184	Dexter	$1175.00
185	Sydney	$2538.00
186	Agatha	$1692.00

The monthsend() function uses the user input as its first argument and today’s date as its second argument. This returns the end date for the user selected period of time. Then, the expression returns the number of days that remain the selected period of time by subtracting today’s date from this end date.

This value is then multiplied by the average daily expense claim by each employee to calculate the estimated value of claims each employee is expected to make in the remaining days of this period.

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