Usually Summary reports, show up break up of count of records processed of the total records.
Representing these Summary reports through diagrams will help client understand better.
Declare-Chart is used to describe the layout of the chart.
Array is created to store data.
Through Print-Chart we read data from Array and print onto the chart.
Below is one sample code to retrieve data and display in chart.
Begin-Setup
Create-Array ! This Array is passed to the Print-Chart
Name = emp_hired
Size = 10 ! Maximum of 10 rows of data
Field = count:number:3 ! Fields in each row
Declare-Chart tot_emp_hired
Chart-Size = ( 30,30)
Title = 'Employee Fetched'
Type = histogram
3D-Effects = yes
X-Axis-Label = 'Company'
Y-Axis-Label = 'Employee Hired'
End-Declare
End-Setup
begin-program
do Stdapi-Init !stdapi.sqc
do Init-DateTime ! datetime.sqc
do Init-Number ! number.sqc
do Get-Current-DateTime! curdttim.sqc
do EmpChartReport
do Stdapi-Term !stdapi.sqc
end-program
begin-procedure EmpChartReport
! Load the array with the Chart Data. Max rows is specified in Create-Array
Put 20
Into emp_hired(0) count(0)
Put 12
Into emp_hired(1) count(0)
Put 4
Into emp_hired(2) count(0)
Move 5 TO #x
Move 10 TO #y
Move 3 TO #row
Move 3 TO #col
Print-Chart tot_emp_hired (#x,#y)
Fill = color
Sub-Title = 'Employee hired report'
Data-Array-Row-Count = #row
Data-Array-Column-Count = #col
Data-Array-Column-Labels = ('ABC1', 'ABC2', 'ABC3')
Data-Array = emp_hired
end-procedure
Sample Output:
![](data:image/png;base64,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)
Representing these Summary reports through diagrams will help client understand better.
Declare-Chart is used to describe the layout of the chart.
Array is created to store data.
Through Print-Chart we read data from Array and print onto the chart.
Below is one sample code to retrieve data and display in chart.
Begin-Setup
Create-Array ! This Array is passed to the Print-Chart
Name = emp_hired
Size = 10 ! Maximum of 10 rows of data
Field = count:number:3 ! Fields in each row
Declare-Chart tot_emp_hired
Chart-Size = ( 30,30)
Title = 'Employee Fetched'
Type = histogram
3D-Effects = yes
X-Axis-Label = 'Company'
Y-Axis-Label = 'Employee Hired'
End-Declare
End-Setup
begin-program
do Stdapi-Init !stdapi.sqc
do Init-DateTime ! datetime.sqc
do Init-Number ! number.sqc
do Get-Current-DateTime! curdttim.sqc
do EmpChartReport
do Stdapi-Term !stdapi.sqc
end-program
begin-procedure EmpChartReport
! Load the array with the Chart Data. Max rows is specified in Create-Array
Put 20
Into emp_hired(0) count(0)
Put 12
Into emp_hired(1) count(0)
Put 4
Into emp_hired(2) count(0)
Move 5 TO #x
Move 10 TO #y
Move 3 TO #row
Move 3 TO #col
Print-Chart tot_emp_hired (#x,#y)
Fill = color
Sub-Title = 'Employee hired report'
Data-Array-Row-Count = #row
Data-Array-Column-Count = #col
Data-Array-Column-Labels = ('ABC1', 'ABC2', 'ABC3')
Data-Array = emp_hired
end-procedure
Sample Output: