423.Can We Detect and Correct Intentional Errors in Industrial Equipment Maintenance Data Using Advanced SAS Programming?

Detecting and Correcting Intentional Errors in Industrial Equipment Maintenance Data Using Advanced SAS Programming

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA STEP | LENGTH | INPUT | DATALINES | SET | MERGE  | PROC SORT | PROC PRINT | PROC TRANSPOSE | PROC APPEND | PROC DATASETS DELETE | MACRO / %MACRO / %MEND | NUMERIC AND CHARACTER FUNCTIONS

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

1. Introduction

In modern industrial environments, maintenance analytics plays a critical role in ensuring operational efficiency, equipment reliability, and cost optimization. Manufacturing plants, oil refineries, pharmaceutical facilities, and logistics warehouses all rely heavily on machinery that must operate continuously with minimal downtime.

However, industrial maintenance datasets often contain numerous data quality issues such as:

·  Incorrect maintenance costs

·  Invalid equipment IDs

·  Missing dates

·  Incorrect percentages

·  Fraudulent maintenance fees

·  Duplicate maintenance records

·  Incorrect downtime calculations

Data engineers and SAS programmers are responsible for detecting and correcting these errors using structured data validation techniques.

In this project, we simulate an Industrial Equipment Maintenance dataset containing:

·  equipment usage data

·  maintenance records

·  operational failures

·  downtime analytics

·  cost tracking

·  fraud detection indicators

The dataset intentionally includes multiple errors, which will later be identified and corrected using advanced SAS programming techniques.

2. Table of Contents

1. Introduction
2. Business Context
3. Dataset Structure
4. Raw Dataset Creation (with errors)
5. Intentional Error Injection
6. Data Cleaning Process
7. SAS Macro for Fraud Detection
8. Date Handling Functions
9. Dataset Combination Techniques
10. Data Transformation using PROC TRANSPOSE
11. Numeric Function Applications
12. Character Function Applications
13. Utilization Classification
14. Dataset Appending and Merging
15. Corrected Dataset Generation
16. 20 Key Points About This Project
17. Conclusion

3. Business Context

Industrial companies track maintenance performance using key variables such as:

Variable	Description
Equipment_ID	Unique machine identifier
Usage_Hours	Total operating hours
Failure_Count	Number of breakdown events
Maintenance_Cost	Cost of repair
Downtime_Hours	Machine downtime
Condition_Index	Equipment health score
Remaining_Life	Estimated life remaining
Percentage	Maintenance completion %
Fees	Service fees
Utilization_Class	Equipment utilization category
Maintenance_Date	Date of service

Maintenance fraud or errors may occur in cases such as:

• Excessive maintenance cost compared to usage
• Fake downtime reporting
• Repeated maintenance billing
• Missing equipment identifiers
• Incorrect life estimation

Detecting such anomalies is an important task for SAS programmers working in industrial analytics.

1. Raw Dataset Creation (With Intentional Errors)

title "Industrial Equipment Maintenance Dataset With Intentional Errors";

data equipment_raw;

length Equipment_ID $10 Equipment_Type $20 Location $20 Technician $20;

input Equipment_ID $ Equipment_Type $ Usage_Hours

      Failure_Count Maintenance_Cost Downtime_Hours

      Condition_Index Remaining_Life Percentage

      Fees Location $ Technician $ Maintenance_Date :ddmmyy10.;

format Maintenance_Date date9.;

datalines;

EQ001 Pump 1200 2 5000 10 85 24 90 250 Hyderabad Ravi 12-01-2025

EQ002 Motor 900 5 12000 30 60 18 85 500 Chennai Kiran 14-02-2025

EQ003 Compressor 1500 3 8000 15 75 20 88 350 Mumbai Suresh 21-03-2025

EQ004 Pump 2000 7 20000 45 40 10 95 800 Pune Ramesh 11-04-2025

EQ005 Turbine 500 1 3000 5 92 30 101 200 Delhi Ajay 22-05-2025

EQ006 Pump 700 2 -500 8 88 26 85 150 Hyderabad Ravi 15-06-2025

EQ007 Motor 1100 0 2500 0 95 35 80 100 Chennai Kiran 18-07-2025

EQ008 Compressor 1600 6 15000 40 55 15 92 650 Mumbai Suresh 25-08-2025

EQ009 Pump . 3 6000 12 70 20 88 300 Pune Ramesh 19-09-2025

EQ010 Turbine 1300 4 9000 22 65 18 89 400 Delhi Ajay 02-10-2025

EQ011 Pump 1000 2 5500 11 80 23 87 250 Hyderabad Ravi 17-11-2025

EQ012 Motor 1400 3 9500 20 72 19 84 450 Chennai Kiran 09-12-2025

EQ013 Compressor 1700 8 21000 60 35 8 99 900 Mumbai Suresh 01-01-2026

EQ014 Pump 850 2 4500 9 82 24 86 230 Pune Ramesh 14-02-2026

EQ015 Turbine 600 1 3200 6 90 28 83 180 Delhi Ajay 05-03-2026

;

run;

proc print data=equipment_raw;

run;

title;

OUTPUT:

Industrial Equipment Maintenance Dataset With Intentional Errors

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025
2	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025
3	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025
4	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025
5	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025
6	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025
7	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025
8	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025
9	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025
10	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025
11	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025
12	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025
13	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026
14	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026
15	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026

Intentional Errors Introduced

This dataset deliberately contains multiple issues:

Error 1 — Invalid Percentage

Percentage = 101

Percentage cannot exceed 100.

Error 2 — Negative Maintenance Cost

Maintenance_Cost = -500

Maintenance costs cannot be negative.

Error 3 — Missing Usage Hours

Usage_Hours = .

This affects utilization calculations.

Error 4 — Abnormally High Maintenance Cost

21000

Possible fraud.

2. Fraud Detection Macro

%macro fraud_detection;

data fraud_flag;

set equipment_raw;

if Maintenance_Cost > 20000 then Fraud_Flag = "YES";

else Fraud_Flag = "NO";

run;

proc print data=fraud_flag;

run;

%mend;

%fraud_detection;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date	Fraud_Flag
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	NO
2	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	NO
3	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	NO
4	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	NO
5	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025	NO
6	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025	NO
7	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	NO
8	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	NO
9	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025	NO
10	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	NO
11	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	NO
12	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	NO
13	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	YES
14	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	NO
15	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	NO

3. Utilization Classification

data utilization_class;

set equipment_raw;

length Utilization_Class $8.;

if Usage_Hours >=1500 then Utilization_Class="HIGH";

else if Usage_Hours >=800 then Utilization_Class="MEDIUM";

else Utilization_Class="LOW";

run;

proc print data=utilization_class;

run;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date	Utilization_Class
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	MEDIUM
2	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	MEDIUM
3	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	HIGH
4	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	HIGH
5	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025	LOW
6	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025	LOW
7	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	MEDIUM
8	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	HIGH
9	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025	LOW
10	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	MEDIUM
11	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	MEDIUM
12	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	MEDIUM
13	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	HIGH
14	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	MEDIUM
15	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	LOW

4. Date Functions

data MDY;

set equipment_raw;

Next_Service_Date = mdy(month(Maintenance_Date),day(Maintenance_Date)

                    ,year(Maintenance_Date)+1);

run;

proc print data=MDY;

run;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date	Next_Service_Date
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	24118
2	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	24151
3	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	24186
4	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	24207
5	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025	24248
6	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025	24272
7	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	24305
8	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	24343
9	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025	24368
10	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	24381
11	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	24427
12	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	24449
13	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	24472
14	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	24516
15	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	24535

Creates next service schedule.

data INTCK;

set equipment_raw;

Service_Age = intck('month',Maintenance_Date,today());

run;

proc print data=INTCK;

run;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date	Service_Age
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	14
2	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	13
3	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	12
4	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	11
5	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025	10
6	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025	9
7	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	8
8	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	7
9	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025	6
10	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	5
11	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	4
12	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	3
13	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	2
14	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	1
15	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	0

Counts months since last service.

data INTNX;

set equipment_raw;

Service_Age = intck('month',Maintenance_Date,today());

run;

proc print data=INTNX;

run;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date	Service_Age
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	14
2	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	13
3	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	12
4	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	11
5	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025	10
6	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025	9
7	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	8
8	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	7
9	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025	6
10	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	5
11	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	4
12	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	3
13	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	2
14	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	1
15	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	0

Calculates next month service.

5. Character Functions

data Char;

set equipment_raw;

Technician_Name = propcase(Technician);

Location_Upper = upcase(Location);

Location_Lower = lowcase(Location);

Clean_ID = strip(trim(Equipment_ID));

Full_ID = catx("-",Equipment_ID,Equipment_Type);

run;

proc print data=Char;

run;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date	Technician_Name	Location_Upper	Location_Lower	Clean_ID	Full_ID
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	Ravi	HYDERABAD	hyderabad	EQ001	EQ001-Pump
2	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	Kiran	CHENNAI	chennai	EQ002	EQ002-Motor
3	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	Suresh	MUMBAI	mumbai	EQ003	EQ003-Compressor
4	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	Ramesh	PUNE	pune	EQ004	EQ004-Pump
5	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025	Ajay	DELHI	delhi	EQ005	EQ005-Turbine
6	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025	Ravi	HYDERABAD	hyderabad	EQ006	EQ006-Pump
7	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	Kiran	CHENNAI	chennai	EQ007	EQ007-Motor
8	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	Suresh	MUMBAI	mumbai	EQ008	EQ008-Compressor
9	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025	Ramesh	PUNE	pune	EQ009	EQ009-Pump
10	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	Ajay	DELHI	delhi	EQ010	EQ010-Turbine
11	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	Ravi	HYDERABAD	hyderabad	EQ011	EQ011-Pump
12	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	Kiran	CHENNAI	chennai	EQ012	EQ012-Motor
13	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	Suresh	MUMBAI	mumbai	EQ013	EQ013-Compressor
14	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	Ramesh	PUNE	pune	EQ014	EQ014-Pump
15	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	Ajay	DELHI	delhi	EQ015	EQ015-Turbine

6. Numeric Functions

data Num;

set equipment_raw;

Cost_per_Hour = Maintenance_Cost / Usage_Hours;

Rounded_Cost = round(Maintenance_Cost);

Log_Cost = log(Maintenance_Cost);

run;

proc print data=Num;

run;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date	Cost_per_Hour	Rounded_Cost	Log_Cost
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	4.1667	5000	8.51719
2	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	13.3333	12000	9.39266
3	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	5.3333	8000	8.98720
4	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	10.0000	20000	9.90349
5	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025	6.0000	3000	8.00637
6	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025	-0.7143	-500	.
7	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	2.2727	2500	7.82405
8	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	9.3750	15000	9.61581
9	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025	.	6000	8.69951
10	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	6.9231	9000	9.10498
11	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	5.5000	5500	8.61250
12	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	6.7857	9500	9.15905
13	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	12.3529	21000	9.95228
14	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	5.2941	4500	8.41183
15	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	5.3333	3200	8.07091

7. PROC TRANSPOSE

proc transpose data=equipment_raw out=maintenance_transpose;

var Usage_Hours Maintenance_Cost Downtime_Hours;

run;

proc print data=maintenance_transpose;

run;

OUTPUT:

Obs	_NAME_	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15
1	Usage_Hours	1200	900	1500	2000	500	700	1100	1600	.	1300	1000	1400	1700	850	600
2	Maintenance_Cost	5000	12000	8000	20000	3000	-500	2500	15000	6000	9000	5500	9500	21000	4500	3200
3	Downtime_Hours	10	30	15	45	5	8	0	40	12	22	11	20	60	9	6

8. Dataset Append

proc append base=equipment_raw

            data=utilization_class force;

run;

proc print data=equipment_raw;

run;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025
2	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025
3	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025
4	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025
5	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025
6	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025
7	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025
8	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025
9	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025
10	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025
11	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025
12	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025
13	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026
14	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026
15	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026
16	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025
17	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025
18	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025
19	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025
20	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025
21	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025
22	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025
23	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025
24	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025
25	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025
26	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025
27	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025
28	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026
29	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026
30	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026

9. Dataset Merge

proc sort data=equipment_raw;by Equipment_ID;run;

proc print data=equipment_raw;

run;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025
2	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025
3	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025
4	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025
5	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025
6	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025
7	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025
8	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025
9	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025
10	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025
11	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025
12	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025
13	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025
14	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025
15	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025
16	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025
17	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025
18	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025
19	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025
20	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025
21	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025
22	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025
23	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025
24	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025
25	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026
26	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026
27	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026
28	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026
29	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026
30	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026

proc sort data=fraud_flag;by Equipment_ID;run;

proc print data=fraud_flag;

run;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date	Fraud_Flag
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	NO
2	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	NO
3	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	NO
4	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	NO
5	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025	NO
6	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025	NO
7	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	NO
8	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	NO
9	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025	NO
10	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	NO
11	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	NO
12	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	NO
13	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	YES
14	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	NO
15	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	NO

data final_maintenance;

merge equipment_raw

      fraud_flag;

by Equipment_ID;

run;

proc print data=final_maintenance;

run;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date	Fraud_Flag
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	NO
2	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	NO
3	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	NO
4	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	NO
5	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	NO
6	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	NO
7	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	NO
8	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	NO
9	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025	NO
10	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	101	200	22MAY2025	NO
11	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025	NO
12	EQ006	Pump	Hyderabad	Ravi	700	2	-500	8	88	26	85	150	15JUN2025	NO
13	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	NO
14	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	NO
15	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	NO
16	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	NO
17	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025	NO
18	EQ009	Pump	Pune	Ramesh	.	3	6000	12	70	20	88	300	19SEP2025	NO
19	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	NO
20	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	NO
21	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	NO
22	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	NO
23	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	NO
24	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	NO
25	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	YES
26	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	YES
27	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	NO
28	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	NO
29	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	NO
30	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	NO

10. PROC DATASETS Delete

proc datasets library=work;

delete maintenance_transpose;

quit;

LOG:

NOTE: Deleting WORK.MAINTENANCE_TRANSPOSE (memtype=DATA).

11. Corrected Dataset Code (Full Program)

data equipment_clean;

set equipment_raw;

if Percentage>100 then Percentage=100;

if Maintenance_Cost<0 then Maintenance_Cost=abs(Maintenance_Cost);

if Usage_Hours=. then Usage_Hours=1000;

if Maintenance_Cost>20000 then Fraud_Flag="YES";

else Fraud_Flag="NO";

Cost_per_Hour = Maintenance_Cost / Usage_Hours;

Next_Service_Date = intnx('year',Maintenance_Date,1,'same');

format Next_Service_Date date9.;

run;

proc print data=equipment_clean;

run;

OUTPUT:

Obs	Equipment_ID	Equipment_Type	Location	Technician	Usage_Hours	Failure_Count	Maintenance_Cost	Downtime_Hours	Condition_Index	Remaining_Life	Percentage	Fees	Maintenance_Date	Fraud_Flag	Cost_per_Hour	Next_Service_Date
1	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	NO	4.1667	12JAN2026
2	EQ001	Pump	Hyderabad	Ravi	1200	2	5000	10	85	24	90	250	12JAN2025	NO	4.1667	12JAN2026
3	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	NO	13.3333	14FEB2026
4	EQ002	Motor	Chennai	Kiran	900	5	12000	30	60	18	85	500	14FEB2025	NO	13.3333	14FEB2026
5	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	NO	5.3333	21MAR2026
6	EQ003	Compressor	Mumbai	Suresh	1500	3	8000	15	75	20	88	350	21MAR2025	NO	5.3333	21MAR2026
7	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	NO	10.0000	11APR2026
8	EQ004	Pump	Pune	Ramesh	2000	7	20000	45	40	10	95	800	11APR2025	NO	10.0000	11APR2026
9	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	100	200	22MAY2025	NO	6.0000	22MAY2026
10	EQ005	Turbine	Delhi	Ajay	500	1	3000	5	92	30	100	200	22MAY2025	NO	6.0000	22MAY2026
11	EQ006	Pump	Hyderabad	Ravi	700	2	500	8	88	26	85	150	15JUN2025	NO	0.7143	15JUN2026
12	EQ006	Pump	Hyderabad	Ravi	700	2	500	8	88	26	85	150	15JUN2025	NO	0.7143	15JUN2026
13	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	NO	2.2727	18JUL2026
14	EQ007	Motor	Chennai	Kiran	1100	0	2500	0	95	35	80	100	18JUL2025	NO	2.2727	18JUL2026
15	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	NO	9.3750	25AUG2026
16	EQ008	Compressor	Mumbai	Suresh	1600	6	15000	40	55	15	92	650	25AUG2025	NO	9.3750	25AUG2026
17	EQ009	Pump	Pune	Ramesh	1000	3	6000	12	70	20	88	300	19SEP2025	NO	6.0000	19SEP2026
18	EQ009	Pump	Pune	Ramesh	1000	3	6000	12	70	20	88	300	19SEP2025	NO	6.0000	19SEP2026
19	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	NO	6.9231	02OCT2026
20	EQ010	Turbine	Delhi	Ajay	1300	4	9000	22	65	18	89	400	02OCT2025	NO	6.9231	02OCT2026
21	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	NO	5.5000	17NOV2026
22	EQ011	Pump	Hyderabad	Ravi	1000	2	5500	11	80	23	87	250	17NOV2025	NO	5.5000	17NOV2026
23	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	NO	6.7857	09DEC2026
24	EQ012	Motor	Chennai	Kiran	1400	3	9500	20	72	19	84	450	09DEC2025	NO	6.7857	09DEC2026
25	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	YES	12.3529	01JAN2027
26	EQ013	Compressor	Mumbai	Suresh	1700	8	21000	60	35	8	99	900	01JAN2026	YES	12.3529	01JAN2027
27	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	NO	5.2941	14FEB2027
28	EQ014	Pump	Pune	Ramesh	850	2	4500	9	82	24	86	230	14FEB2026	NO	5.2941	14FEB2027
29	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	NO	5.3333	05MAR2027
30	EQ015	Turbine	Delhi	Ajay	600	1	3200	6	90	28	83	180	05MAR2026	NO	5.3333	05MAR2027

20 Key Points About This Project

1.     The project begins by designing a realistic industrial equipment maintenance dataset.

2.     It includes operational variables such as Usage Hours, Failure Count, and Downtime.

3.     Financial metrics such as Maintenance Cost and Service Fees are incorporated.

4.     Equipment health is represented using Condition Index and Remaining Life.

5.     The dataset intentionally introduces multiple data quality errors.

6.     Examples include negative maintenance cost and missing usage hours.

7.     Invalid percentages greater than 100% were deliberately inserted.

8.     SAS DATA step programming was used to build the dataset.

9.     Character cleaning functions such as STRIP and TRIM were applied.

10.  Formatting improvements were performed using PROPCASE, UPCASE, and LOWCASE.

11.  Numeric functions were used to derive metrics like cost per hour.

12.  Date calculations were performed using MDY, INTCK, and INTNX.

13.  A fraud detection macro was implemented to identify abnormal maintenance costs.

14.  Equipment utilization was categorized as LOW, MEDIUM, or HIGH.

15.  PROC TRANSPOSE was used to restructure maintenance variables.

16.  Multiple datasets were combined using SET, MERGE, and APPEND.

17.  Incorrect values were corrected using conditional logic in DATA steps.

18.  Temporary datasets were cleaned using PROC DATASETS DELETE.

19.  The final dataset supports maintenance analytics and fraud detection.

20.  The completed project demonstrates advanced SAS data engineering techniques used in real industrial analytics environments.

Project Summary

This project demonstrates how SAS programming can be used to manage and analyze industrial equipment maintenance data while detecting and correcting data quality issues. A simulated dataset was created containing variables such as Equipment_ID, Usage_Hours, Failure_Count, Maintenance_Cost, Downtime_Hours, Condition_Index, Remaining_Life, Percentage, Fees, and Maintenance_Date. Several intentional errors were introduced into the dataset, including negative maintenance costs, missing usage hours, invalid percentages greater than 100, and suspiciously high maintenance expenses. These errors represent common real-world data issues encountered in industrial maintenance analytics.

Using SAS DATA step programming, macros, character functions, numeric functions, and date functions such as MDY, INTCK, and INTNX, the dataset was cleaned and transformed. Fraud detection logic was implemented to flag abnormal maintenance costs, and utilization classification was created to categorize equipment usage levels. Additional dataset manipulation techniques such as SET, MERGE, APPEND, and PROC TRANSPOSE were applied. The final cleaned dataset provides reliable maintenance insights for operational decision-making and predictive equipment management.

Conclusion

This project demonstrates a complete industrial equipment maintenance analytics workflow using SAS programming. The program begins with the creation of a raw dataset that intentionally contains multiple data errors, such as missing values, invalid percentages, negative maintenance costs, and suspiciously high repair expenses.

Through the use of SAS DATA steps, macros, dataset manipulation techniques, and built-in functions, the program systematically detects these issues, flags fraudulent transactions, and corrects invalid data.

Key SAS techniques include:

·  Dataset creation using DATA step

·  Fraud detection using macros

·  Data cleaning and validation

·  Date calculations using MDY, INTCK, and INTNX

·  Character manipulation using STRIP, TRIM, CATX, PROPCASE

·  Numeric analysis using mathematical functions

·  Dataset transformation using PROC TRANSPOSE

·  Dataset merging and appending

·  Dataset management using PROC DATASETS

The final cleaned dataset provides reliable information for industrial maintenance decision-making, enabling organizations to reduce downtime, detect fraudulent maintenance activities, and improve equipment life cycle management.

SAS INTERVIEW QUESTIONS

1. Explain ARRAY processing in SAS?

An ARRAY in SAS is used to process multiple variables together using a single loop instead of writing repetitive code.

Example use:

·  Apply calculations to many variables

·  Data cleaning across multiple columns

Example:

array scores score1-score5;

do i=1 to 5;

   scores{i}=scores{i}+10;

end;

Short: ARRAY allows efficient processing of multiple variables using loops.

 2. What is the purpose of DO UNTIL and DO WHILE?

Both are looping statements used to repeat a block of code.

Statement	Condition Check
DO WHILE	Condition checked before execution
DO UNTIL	Condition checked after execution

Short:

 ·  DO WHILE runs while condition is true.

 ·  DO UNTIL runs until condition becomes true.

3. How do you remove duplicates in SAS?

Duplicates are usually removed using PROC SORT with NODUPKEY.

Example:

proc sort data=dataset nodupkey;

by id;

run;

Short: NODUPKEY removes observations with duplicate values of the BY variable.

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

About the Author:

SAS Learning Hub is a data analytics and SAS programming platform focused on clinical, financial, and real-world data analysis. The content is created by professionals with academic training in Pharmaceutics and hands-on experience in Base SAS, PROC SQL, Macros, SDTM, and ADaM, providing practical and industry-relevant SAS learning resources.

Disclaimer:

The datasets and analysis in this article are created for educational and demonstration purposes only. They do not represent EQUIPMENT MAINTENANCE data.

Our Mission:

This blog provides industry-focused SAS programming tutorials and analytics projects covering finance, healthcare, and technology.

This project is suitable for:

·  Students learning SAS

·  Data analysts building portfolios

·  Professionals preparing for SAS interviews

·  Bloggers writing about analytics 

·  Clinical SAS Programmer

·  Research Data Analyst

·  Regulatory Data Validator

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Follow Us On : 

 

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

--->Follow our blog for more SAS-based analytics projects and industry data models.

---> Support Us By Following Our Blog..

To deepen your understanding of SAS analytics, please refer to our other data science and industry-focused projects listed below:

1.Which Country Truly Dominates the Olympics? – A Complete SAS Medal Efficiency Analytics Project

2.Which Airports Are Really the Busiest? – An End-to-End SAS Airport Traffic Analytics Project

3.Can Data Predict Election Outcomes? – A Complete SAS Voting Analytics Project

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

About Us | Contact | Privacy Policy