329.How Can SAS Analyze Agricultural Machinery Performance Using DATA Step, PROC SQL, and Smart Date Calculations?

How Can SAS Analyze Agricultural Machinery Performance Using DATA Step, PROC SQL, and Smart 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

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