329.AGRICULTURAL MACHINERY DATASET CREATION WITH DATA STEP | PROC SQL | PROC MEANS | PROC FREQ | PROC CORR | MACROS FOR PERFORMANCE & DATE CALCULATIONS

AGRICULTURAL MACHINERY DATASET CREATION WITH DATA STEP | PROC SQL | PROC MEANS | PROC FREQ | PROC CORR | MACROS FOR PERFORMANCE & DATE CALCULATIONS

options nocenter;

STEP 1: CREATE BASE DATASET WITH 12 AGRICULTURAL MACHINES

data agri_machines_raw;

    length Machine_Name $30 Brand $20 Fuel_Type $10 Maintenance_Level $10;

    input Machine_Name $ Brand $ Fuel_Type $ Efficiency Cost Usage_Hours   

         Purchase_Date_char:$10. Last_Service_char:$10. Maintenance_Level $;

    datalines;

Tractor_MidRange JohnDeere Diesel 85 1200000 2200 15JAN2018 10JAN2024 High

Tractor_Compact Mahindra Diesel 78 800000 1800 20JUN2019 15DEC2023 Medium

Combine_Harvester NewHolland Diesel 90 3500000 2500 01APR2017 20NOV2023 High

Rotavator_Heavy Kubota Diesel 82 450000 1600 10SEP2020 05FEB2024 Medium

Seed_Drill_Pneumatic Mahindra Diesel 88 600000 1900 25NOV2020 01JAN2024 Medium

Boom_Sprayer_Trailed CLAAS Diesel 80 1100000 2000 12AUG2018 18OCT2023 Medium

Baler_Round JohnDeere Diesel 87 1500000 2100 05MAY2016 22DEC2023 High

Thresher_Stationary LocalBrand Electric 75 300000 1400 30JAN2021 27JAN2024 Medium

Power_Tiller_Compact VST Diesel 70 250000 1300 18JUL2022 03MAR2024 Low

Rice_Transplanter Kubota Diesel 83 900000 1750 09JUN2019 28NOV2023 Medium

Sugarcane_Harvester CaseIH Diesel 92 5500000 2700 21FEB2016 15SEP2023 High

Potato_Digger_Trailed LocalBrand Petrol 68 350000 1200 11OCT2022 10FEB2024 Low

;

run;

proc print data=agri_machines_raw;

run;

OUTPUT:

Obs	Machine_Name	Brand	Fuel_Type	Maintenance_Level	Efficiency	Cost	Usage_Hours	Purchase_Date_char	Last_Service_char
1	Tractor_MidRange	JohnDeere	Diesel	High	85	1200000	2200	15JAN2018	10JAN2024
2	Tractor_Compact	Mahindra	Diesel	Medium	78	800000	1800	20JUN2019	15DEC2023
3	Combine_Harvester	NewHolland	Diesel	High	90	3500000	2500	01APR2017	20NOV2023
4	Rotavator_Heavy	Kubota	Diesel	Medium	82	450000	1600	10SEP2020	05FEB2024
5	Seed_Drill_Pneumatic	Mahindra	Diesel	Medium	88	600000	1900	25NOV2020	01JAN2024
6	Boom_Sprayer_Trailed	CLAAS	Diesel	Medium	80	1100000	2000	12AUG2018	18OCT2023
7	Baler_Round	JohnDeere	Diesel	High	87	1500000	2100	05MAY2016	22DEC2023
8	Thresher_Stationary	LocalBrand	Electric	Medium	75	300000	1400	30JAN2021	27JAN2024
9	Power_Tiller_Compact	VST	Diesel	Low	70	250000	1300	18JUL2022	03MAR2024
10	Rice_Transplanter	Kubota	Diesel	Medium	83	900000	1750	09JUN2019	28NOV2023
11	Sugarcane_Harvester	CaseIH	Diesel	High	92	5500000	2700	21FEB2016	15SEP2023
12	Potato_Digger_Trailed	LocalBrand	Petrol	Low	68	350000	1200	11OCT2022	10FEB2024

STEP 2: USE PROC SQL TO CONVERT CHAR DATES TO SAS DATES AND KEEP MAINTENANCE_LEVEL

proc sql;

    create table agri_machines as

    select  Machine_Name,Brand,Fuel_Type,Efficiency,Cost,Usage_Hours,

            input(Purchase_Date_char, date9.) as Purchase_Date format=date9.,

            input(Last_Service_char , date9.) as Last_Service_Date format=date9.,

            Maintenance_Level

    from agri_machines_raw;

quit;

proc print data=agri_machines;

run;

OUTPUT:

Obs	Machine_Name	Brand	Fuel_Type	Efficiency	Cost	Usage_Hours	Purchase_Date	Last_Service_Date	Maintenance_Level
1	Tractor_MidRange	JohnDeere	Diesel	85	1200000	2200	15JAN2018	10JAN2024	High
2	Tractor_Compact	Mahindra	Diesel	78	800000	1800	20JUN2019	15DEC2023	Medium
3	Combine_Harvester	NewHolland	Diesel	90	3500000	2500	01APR2017	20NOV2023	High
4	Rotavator_Heavy	Kubota	Diesel	82	450000	1600	10SEP2020	05FEB2024	Medium
5	Seed_Drill_Pneumatic	Mahindra	Diesel	88	600000	1900	25NOV2020	01JAN2024	Medium
6	Boom_Sprayer_Trailed	CLAAS	Diesel	80	1100000	2000	12AUG2018	18OCT2023	Medium
7	Baler_Round	JohnDeere	Diesel	87	1500000	2100	05MAY2016	22DEC2023	High
8	Thresher_Stationary	LocalBrand	Electric	75	300000	1400	30JAN2021	27JAN2024	Medium
9	Power_Tiller_Compact	VST	Diesel	70	250000	1300	18JUL2022	03MAR2024	Low
10	Rice_Transplanter	Kubota	Diesel	83	900000	1750	09JUN2019	28NOV2023	Medium
11	Sugarcane_Harvester	CaseIH	Diesel	92	5500000	2700	21FEB2016	15SEP2023	High
12	Potato_Digger_Trailed	LocalBrand	Petrol	68	350000	1200	11OCT2022	10FEB2024	Low

STEP 3: DEFINE MACROS FOR PERFORMANCE GRADING AND SERVICE SCHEDULING USING INTCK / INTNX

/* Macro to grade performance based on efficiency and usage */

%macro perf_grade(efficiency, usage);

    length Performance_Grade $1;

    if &efficiency >= 90 and &usage >= 2200 then Performance_Grade = 'A';

    else if &efficiency >= 80 and &usage >= 1700 then Performance_Grade = 'B';

    else if &efficiency >= 70 and &usage >= 1300 then Performance_Grade = 'C';

    else Performance_Grade = 'D';

%mend;

/* Macro to compute date-based metrics using INTCK and INTNX */

%macro service_schedule(purchase, last_service);

    /* Age in years from purchase date to today */

    Age_Years = intck('year', &purchase, today());

    /* Months since last service */

    Months_Since_Service = intck('month', &last_service, today());

    /* Next service due 6 months after last service */

    Next_Service_Due = intnx('month', &last_service, 6, 'same');

    format Next_Service_Due date9.;

%mend;

STEP 4: CREATE FINAL ANALYTICAL DATASET WITH MACROS

data agri_machines_final;

    set agri_machines;

    /* Call macro for performance grading */

    %perf_grade(Efficiency, Usage_Hours);

    /* Call macro for date-based calculations */

    %service_schedule(Purchase_Date, Last_Service_Date);

    /* Simple numeric coding for Maintenance_Level */

    length Maint_Level_Code 8;

    if Maintenance_Level = '' then do;

        /* Derive level based on Usage_Hours as example rule */

        if Usage_Hours >= 2300 then Maintenance_Level = 'High';

        else if Usage_Hours >= 1700 then Maintenance_Level = 'Medium';

        else Maintenance_Level = 'Low';

    end;

    if Maintenance_Level = 'High'   then Maint_Level_Code = 3;

    else if Maintenance_Level = 'Medium' then Maint_Level_Code = 2;

    else if Maintenance_Level = 'Low'    then Maint_Level_Code = 1;

run;

proc print data=agri_machines_final;

run;

OUTPUT:

Obs	Machine_Name	Brand	Fuel_Type	Efficiency	Cost	Usage_Hours	Purchase_Date	Last_Service_Date	Maintenance_Level	Performance_Grade	Age_Years	Months_Since_Service	Next_Service_Due	Maint_Level_Code
1	Tractor_MidRange	JohnDeere	Diesel	85	1200000	2200	15JAN2018	10JAN2024	High	B	7	23	10JUL2024	3
2	Tractor_Compact	Mahindra	Diesel	78	800000	1800	20JUN2019	15DEC2023	Medium	C	6	24	15JUN2024	2
3	Combine_Harvester	NewHolland	Diesel	90	3500000	2500	01APR2017	20NOV2023	High	A	8	25	20MAY2024	3
4	Rotavator_Heavy	Kubota	Diesel	82	450000	1600	10SEP2020	05FEB2024	Medium	C	5	22	05AUG2024	2
5	Seed_Drill_Pneumatic	Mahindra	Diesel	88	600000	1900	25NOV2020	01JAN2024	Medium	B	5	23	01JUL2024	2
6	Boom_Sprayer_Trailed	CLAAS	Diesel	80	1100000	2000	12AUG2018	18OCT2023	Medium	B	7	26	18APR2024	2
7	Baler_Round	JohnDeere	Diesel	87	1500000	2100	05MAY2016	22DEC2023	High	B	9	24	22JUN2024	3
8	Thresher_Stationary	LocalBrand	Electric	75	300000	1400	30JAN2021	27JAN2024	Medium	C	4	23	27JUL2024	2
9	Power_Tiller_Compact	VST	Diesel	70	250000	1300	18JUL2022	03MAR2024	Low	C	3	21	03SEP2024	1
10	Rice_Transplanter	Kubota	Diesel	83	900000	1750	09JUN2019	28NOV2023	Medium	B	6	25	28MAY2024	2
11	Sugarcane_Harvester	CaseIH	Diesel	92	5500000	2700	21FEB2016	15SEP2023	High	A	9	27	15MAR2024	3
12	Potato_Digger_Trailed	LocalBrand	Petrol	68	350000	1200	11OCT2022	10FEB2024	Low	D	3	22	10AUG2024	1

STEP 5: PROC MEANS – SUMMARY STATISTICS

proc means data=agri_machines_final n mean std min max;

    var Efficiency Cost Usage_Hours Age_Years Months_Since_Service;

    class Fuel_Type;

run;

OUTPUT:

The MEANS Procedure

Fuel_Type	N Obs	Variable	N	Mean	Std Dev	Minimum	Maximum
Diesel	10	Efficiency Cost Usage_Hours Age_Years Months_Since_Service	10 10 10 10 10	83.5000000 1580000.00 1985.00 6.5000000 24.0000000	6.4678693 1650286.17 415.0301194 1.9002924 1.8257419	70.0000000 250000.00 1300.00 3.0000000 21.0000000	92.0000000 5500000.00 2700.00 9.0000000 27.0000000
Electric	1	Efficiency Cost Usage_Hours Age_Years Months_Since_Service	1 1 1 1 1	75.0000000 300000.00 1400.00 4.0000000 23.0000000	. . . . .	75.0000000 300000.00 1400.00 4.0000000 23.0000000	75.0000000 300000.00 1400.00 4.0000000 23.0000000
Petrol	1	Efficiency Cost Usage_Hours Age_Years Months_Since_Service	1 1 1 1 1	68.0000000 350000.00 1200.00 3.0000000 22.0000000	. . . . .	68.0000000 350000.00 1200.00 3.0000000 22.0000000	68.0000000 350000.00 1200.00 3.0000000 22.0000000

STEP 6: PROC FREQ – CATEGORICAL DISTRIBUTIONS

proc freq data=agri_machines_final;

    tables Brand Fuel_Type Maintenance_Level Performance_Grade / nocum nopercent;

run;

OUTPUT:

The FREQ Procedure

Brand	Frequency
CLAAS	1
CaseIH	1
JohnDeere	2
Kubota	2
LocalBrand	2
Mahindra	2
NewHolland	1
VST	1

Fuel_Type	Frequency
Diesel	10
Electric	1
Petrol	1

Maintenance_Level	Frequency
High	4
Low	2
Medium	6

Performance_Grade	Frequency
A	2
B	5
C	4
D	1

STEP 7: PROC CORR – CORRELATION BETWEEN NUMERIC VARIABLES

proc corr data=agri_machines_final nosimple;

    var Efficiency Cost Usage_Hours Age_Years Maint_Level_Code;

run;

OUTPUT:

The CORR Procedure

5 Variables:	Efficiency Cost Usage_Hours Age_Years Maint_Level_Code

Pearson Correlation Coefficients, N = 12 Prob > |r| under H0: Rho=0
	Efficiency	Cost	Usage_Hours	Age_Years	Maint_Level_Code
Efficiency	1.00000	0.69319 0.0124	0.90328 <.0001	0.84375 0.0006	0.87939 0.0002
Cost	0.69319 0.0124	1.00000	0.85796 0.0004	0.74394 0.0055	0.67402 0.0162
Usage_Hours	0.90328 <.0001	0.85796 0.0004	1.00000	0.92227 <.0001	0.89245 <.0001
Age_Years	0.84375 0.0006	0.74394 0.0055	0.92227 <.0001	1.00000	0.90951 <.0001
Maint_Level_Code	0.87939 0.0002	0.67402 0.0162	0.89245 <.0001	0.90951 <.0001	1.00000

STEP 8: PROC SQL – EXAMPLE QUERY FOR HIGH PERFORMANCE MACHINES

proc sql;

    select Machine_Name,Brand,Fuel_Type,Efficiency,Usage_Hours,Performance_Grade,

           Age_Years,Months_Since_Service,Next_Service_Due

    from agri_machines_final

    where Performance_Grade in ('A', 'B')

    order by Efficiency desc, Usage_Hours desc;

quit;

OUTPUT:

Machine_Name	Brand	Fuel_Type	Efficiency	Usage_Hours	Performance_Grade	Age_Years	Months_Since_Service	Next_Service_Due
Sugarcane_Harvester	CaseIH	Diesel	92	2700	A	9	27	15MAR2024
Combine_Harvester	NewHolland	Diesel	90	2500	A	8	25	20MAY2024
Seed_Drill_Pneumatic	Mahindra	Diesel	88	1900	B	5	23	01JUL2024
Baler_Round	JohnDeere	Diesel	87	2100	B	9	24	22JUN2024
Tractor_MidRange	JohnDeere	Diesel	85	2200	B	7	23	10JUL2024
Rice_Transplanter	Kubota	Diesel	83	1750	B	6	25	28MAY2024
Boom_Sprayer_Trailed	CLAAS	Diesel	80	2000	B	7	26	18APR2024

To Visit My Previous Original Data Management Dataset:Click Here

To Visit My Previous Cricket World Cup Dataset:Click Here

To Visit My Previous Home Tour Dataset:Click Here

To Visit My Previous Real-World Dal Price Dataset:Click Here  

Follow Us On : 

 

--- FOLLOW OUR BLOG FOR MORE INFORMATION.

--->PLEASE DO COMMENTS AND SHARE OUR BLOG.