monthsstart - script and chart function

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, bi_monthly_start, that groups transactions into bi-monthly segments and returns the starting timestamp of the segment for each transaction.

Load script

SET DateFormat='MM/DD/YYYY';

Transactions:
    Load
        *,
        monthsstart(2,date) as bi_monthly_start,
        timestamp(monthsstart(2,date)) as bi_monthly_start_timestamp
        ;
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 these fields as dimensions:

• date

• bi_monthly_start

• bi_monthly_start_timestamp

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

Transaction 8195 takes place on May 22. The monthsstart() function initially divides the year into bi-monthly segments. Transaction 8195 falls into the segment between May 1 and June 30. Therefore, the function returns the first millisecond of this segment, May 1, 2022 at 12:00:00 AM.

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_bi_monthly_start, that returns the first millisecond of the bi-monthly segment before the transaction took place.

Load script

SET DateFormat='MM/DD/YYYY';

Transactions:
    Load
        *,
        monthsstart(2,date,-1) as prev_bi_monthly_start,
	    timestamp(monthsstart(2,date,-1)) as prev_bi_monthly_start_timestamp
	    ;
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 these fields as dimensions:

• date

• prev_bi_monthly_start

• prev_bi_monthly_start_timestamp

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

Transaction 8195 occurs in the segment between May and June. Therefore, the previous bi-monthly segment was between March 1 and April 30, so the function returns the first millisecond of this segment, March 1, 2022 at 12:00:00 AM.

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, bi_monthly_start, that groups transactions into bi-monthly segments and returns the starting timestamp of the set for each transaction.

However, in this example, we also need to set April as the first month of the financial year.

Load script

SET DateFormat='MM/DD/YYYY';

Transactions:
    Load
        *,
        monthsstart(2,date,0,4) as bi_monthly_start,
        timestamp(monthsstart(2,date,0,4)) as bi_monthly_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

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

• date

• bi_monthly_start

• bi_monthly_start_timestamp

By using 4 as the first_month_of_year argument in the monthsstart() 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.

Transaction 8195 took place on May 22 and falls into the segment between April 1 and May 31. Therefore, the function returns the first millisecond of this segment, April 1, 2022 at 12:00:00 AM.

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 which groups transactions into bi-monthly segments and returns the starting timestamp of the set for each transaction 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
8188,2/19/2022,37.23
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.

Create the following measures:

=monthsstart(2,date)

=timestamp(monthsstart(2,date))

These calculations will retrieve the starting timestamp of the bi-monthly segment in which each transaction took place.

Transaction 8195 took place on May 22. The monthsstart() function initially divides the year into bi-monthly segments. Transaction 8195 falls into the segment between May 1 and June 30. Therefore, the function returns the first millisecond of this segment, 05/01/2022 12:00:00 AM.

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 start of the month, 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 for the period of their choosing. The financial year begins in January.

Load script

SET DateFormat='MM/DD/YYYY';

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

Load the data and open a sheet.

At the start of the load script, a variable (vPeriod) has been created that will be tied to the variable input control. Next, configure the variable as a custom object in the sheet.

Do the following:

1. In the assets panel, click Custom objects.

2. Select Qlik Dashboard bundle, and create a Variable input object.

3. Enter a title for the chart object.

4. Under Variable, select vPeriod as the Name and set the object to show as a Drop down.

5. Under Values, configure the object to use dynamic values. Enter the following:
   ='1~month|2~bi-month|3~quarter|4~tertial|6~half-year'

Next, create the results table.

Do the following:

1. Create a new table. Add the following fields as dimensions.

   • employee_id

   • employee_name

2. Create a measure to calculate the accumulated interest:

   =start_balance*(rate*(today(1)-monthsstart($(vPeriod),today(1)))/365)

3. Set the measure's Number formatting to Money. Click Done editing. You can now modify the data shown in the table by adjusting the time segment in the variable object.

This is what the results table will look like when the month period option is selected:

The monthsstart() function, using the user’s input as its first argument and today’s date as its second argument, returns the start date of the period of the user’s choosing. By subtracting that result from the current date, the expression returns the number of days that have elapsed so far in this period.

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 period.