424.Can Advanced SAS Programming Detect Fraud And Correct Data Errors In Public Library Utilization Systems?

Smart Data Validation and Fraud Detection in Public Library Utilization Systems Using Advanced SAS Techniques

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HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA STEP | SET | MERGE | PROC SORT | PROC APPEND | PROC TRANSPOSE | PROC DATASETS | INPUT | LENGTH | FORMAT | IF THEN ELSE | MDY | INTCK | INTNX | COALESCE | STRIP | TRIM | CAT | CATX | PROPCASE | UPCASE | LOWCASE | MACRO | MEND

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1. Introduction

Public libraries play an important role in the knowledge ecosystem of cities. They provide access to books, digital content, community learning spaces, and research materials. Modern libraries maintain extensive databases that track information such as the number of books available, member registrations, digital usage metrics, operating costs, and service performance scores.

However, like any large-scale information system, library datasets may contain data quality problems. These issues may arise due to:

• manual data entry mistakes
• system synchronization errors
• missing values
• duplicated records
• fraud in reporting library statistics
• incorrect date formats
• inconsistent capitalization or character formatting

If these issues are not detected and corrected, they can lead to incorrect decisions regarding funding allocation, staffing requirements, and service improvements.

SAS programming provides powerful tools to identify and correct these problems through:

• data step programming
• character and numeric functions
• date processing
• dataset merging
• macro automation
• fraud detection logic

In this project, we simulate a Public Library Management dataset that intentionally includes multiple errors. Then we use SAS programming techniques to detect and correct these issues.

The final goal is to demonstrate how advanced SAS techniques can help maintain accurate, reliable, and fraud-resistant library data systems.

2. Business Context

Cities operate multiple public libraries to support community education. Each library tracks key operational indicators such as:

• number of books available
• number of registered members
• digital service usage
• operational costs
• service quality scores
• membership fees

Government funding decisions are often based on these metrics.

However, if data is manipulated or entered incorrectly, it can cause:

• overfunding inefficient libraries
• underfunding high-performing libraries
• fraudulent reporting of library performance
• incorrect strategic planning

Therefore, maintaining clean, validated datasets is essential for library management.

SAS programming is widely used in analytics environments because it provides:

• strong data validation tools
• reliable data transformations
• automated quality checks
• fraud detection algorithms

This project demonstrates a simplified simulation of such a system.

3. Table of Contents

1. Introduction
2. Business Context
3. Raw Dataset Creation
4. Intentional Error Injection
5. Character Data Cleaning
6. Numeric Validation
7. Date Format Handling
8. Utilization Classification
9. Fraud Detection Macro
10. Dataset Merging
11. Dataset Appending
12. Dataset Transposing
13. Final Dataset Creation
14. Dataset Deletion
15. Analytical Insights
16. Project Summary
17. Conclusion

1. Creating Raw Library Dataset With Intentional Errors

data library_raw;

length Library_Name $25 City $20 Utilization_Class $15;

input Library_Name $ City $ Books_Count Members Digital_Usage 

      Operating_Cost Service_Score Percentage Fees Join_Date :ddmmyy10.;

format Join_Date ddmmyy10.;

datalines;

central_library hyderabad 50000 1500 900 200000 8.5 85 200 12/05/2020

city_library Hyderabad 45000 1400 800 190000 8.0 80 180 15/06/2020

knowledge_hub vizag 30000 900 500 150000 7.2 72 150 10/07/2021

digital_library chennai 40000 1100 700 175000 7.8 78 170 20/08/2021

reading_center Bangalore 35000 1000 600 165000 7.5 75 160 11/09/2021

community_library pune 20000 800 450 120000 6.8 68 120 05/10/2021

metro_library delhi 60000 1800 1200 250000 9.0 90 220 17/11/2022

urban_library kolkata 42000 1300 750 185000 8.1 81 190 22/12/2022

study_zone hyderabad -30000 900 600 160000 7.3 73 160 01/01/2023

smart_library bangalore 38000 1100 700 -170000 7.6 76 170 14/02/2023

city_readers chennai 0 850 550 150000 7.0 70 150 20/03/2023

digital_hub vizag 45000 . 900 200000 8.3 83 200 05/04/2023

;

run;

proc print data=library_raw;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date
1	central_library	hyderabad		50000	1500	900	200000	8.5	85	200	12/05/2020
2	city_library	Hyderabad		45000	1400	800	190000	8.0	80	180	15/06/2020
3	knowledge_hub	vizag		30000	900	500	150000	7.2	72	150	10/07/2021
4	digital_library	chennai		40000	1100	700	175000	7.8	78	170	20/08/2021
5	reading_center	Bangalore		35000	1000	600	165000	7.5	75	160	11/09/2021
6	community_library	pune		20000	800	450	120000	6.8	68	120	05/10/2021
7	metro_library	delhi		60000	1800	1200	250000	9.0	90	220	17/11/2022
8	urban_library	kolkata		42000	1300	750	185000	8.1	81	190	22/12/2022
9	study_zone	hyderabad		-30000	900	600	160000	7.3	73	160	01/01/2023
10	smart_library	bangalore		38000	1100	700	-170000	7.6	76	170	14/02/2023
11	city_readers	chennai		0	850	550	150000	7.0	70	150	20/03/2023
12	digital_hub	vizag		45000	.	900	200000	8.3	83	200	05/04/2023

Explanation

This DATA step creates the raw dataset representing multiple public libraries across different Indian cities.

The dataset contains the following variables:

Library_Name
City
Books_Count
Members
Digital_Usage
Operating_Cost
Service_Score
Percentage
Fees
Join_Date

However, the dataset intentionally contains multiple data errors.

Examples include:

·  negative values in Books_Count

·  negative operating cost

·  missing values

·  zero book counts

·  inconsistent capitalization in city names

These errors will later be corrected using SAS programming techniques.

Errors Introduced Intentionally

·  Negative Books_Count (-30000)

·  Negative Operating_Cost (-170000)

·  Missing Members value

·  Zero Books_Count

·  inconsistent city capitalization

·  potential fraud in digital usage

These are typical data quality problems in real systems.

Why This Code Is Important

This code establishes the foundation dataset used throughout the project.

It simulates real-world scenarios where raw data contains inaccuracies.

Without a raw dataset containing errors, we cannot demonstrate data cleaning and fraud detection logic.

Key Points

·  DATA step creates a new dataset

·  LENGTH statement defines character variable sizes

·  INPUT statement reads raw data

·  FORMAT applies date display format

·  DATALINES loads the data

·  Join_Date uses DDMMYY format

·  Dataset contains intentional errors

·  Missing values represented using "."

·  Negative values simulate data entry mistakes

·  Dataset acts as starting point for cleaning

2. Standardizing Character Variables

data library_clean_char;

set library_raw;

Library_Name = propcase(Library_Name);

City = propcase(strip(City));

run;

proc print data=library_clean_char;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date
1	Central_library	Hyderabad		50000	1500	900	200000	8.5	85	200	12/05/2020
2	City_library	Hyderabad		45000	1400	800	190000	8.0	80	180	15/06/2020
3	Knowledge_hub	Vizag		30000	900	500	150000	7.2	72	150	10/07/2021
4	Digital_library	Chennai		40000	1100	700	175000	7.8	78	170	20/08/2021
5	Reading_center	Bangalore		35000	1000	600	165000	7.5	75	160	11/09/2021
6	Community_library	Pune		20000	800	450	120000	6.8	68	120	05/10/2021
7	Metro_library	Delhi		60000	1800	1200	250000	9.0	90	220	17/11/2022
8	Urban_library	Kolkata		42000	1300	750	185000	8.1	81	190	22/12/2022
9	Study_zone	Hyderabad		-30000	900	600	160000	7.3	73	160	01/01/2023
10	Smart_library	Bangalore		38000	1100	700	-170000	7.6	76	170	14/02/2023
11	City_readers	Chennai		0	850	550	150000	7.0	70	150	20/03/2023
12	Digital_hub	Vizag		45000	.	900	200000	8.3	83	200	05/04/2023

Explanation

Character variables often contain inconsistencies such as:

·  extra spaces

·  mixed capitalization

·  inconsistent naming formats

STRIP

Removes leading and trailing spaces.

PROPCASE

Converts text to proper case.

Example:

hyderabad → Hyderabad

Why It Is Used

Clean character variables ensure consistent grouping and reporting.

For example:

"Hyderabad"
"hyderabad"

would otherwise be treated as two different cities.

Key Points

·  SET reads existing dataset

·  STRIP removes extra spaces

·  PROPCASE standardizes capitalization

·  Improves data consistency

·  Prevents grouping errors

·  Helps in reporting

·  Improves data quality

·  Supports merging operations

·  Removes formatting inconsistencies

·  Standardizes textual variables

3. Using Character Functions

data library_char_functions;

set library_clean_char;

City_Upper = upcase(City);

City_Lower = lowcase(City);

Library_Label = catx('-',Library_Name,City);

run;

proc print data=library_char_functions;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label
1	Central_library	Hyderabad		50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad
2	City_library	Hyderabad		45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad
3	Knowledge_hub	Vizag		30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag
4	Digital_library	Chennai		40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai
5	Reading_center	Bangalore		35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore
6	Community_library	Pune		20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune
7	Metro_library	Delhi		60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi
8	Urban_library	Kolkata		42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata
9	Study_zone	Hyderabad		-30000	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad
10	Smart_library	Bangalore		38000	1100	700	-170000	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore
11	City_readers	Chennai		0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai
12	Digital_hub	Vizag		45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag

Explanation

This program demonstrates various character manipulation functions.

UPCASE converts text to uppercase.

LOWCASE converts text to lowercase.

CATX concatenates values with a delimiter.

Example result:

Central_Library-Hyderabad

These functions help create new identifiers and standardized variables.

Key Points

·  UPCASE converts to uppercase

·  LOWCASE converts to lowercase

·  CATX concatenates with delimiter

·  Helps build unique identifiers

·  Useful in reporting systems

·  Prevents case-sensitive mismatches

·  Supports analytics grouping

·  Improves readability

·  Creates derived variables

·  Demonstrates character function usage

4. Numeric Error Detection

data library_numeric_check;

set library_char_functions;

if Books_Count < 0 then Books_Count = .;

if Operating_Cost < 0 then Operating_Cost = .;

run;

proc print data=library_numeric_check;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label
1	Central_library	Hyderabad		50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad
2	City_library	Hyderabad		45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad
3	Knowledge_hub	Vizag		30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag
4	Digital_library	Chennai		40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai
5	Reading_center	Bangalore		35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore
6	Community_library	Pune		20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune
7	Metro_library	Delhi		60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi
8	Urban_library	Kolkata		42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata
9	Study_zone	Hyderabad		.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad
10	Smart_library	Bangalore		38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore
11	City_readers	Chennai		0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai
12	Digital_hub	Vizag		45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag

Explanation

Numeric validation ensures that variables contain logical values.

Books count cannot be negative.

Operating cost cannot be negative.

If such values exist, they are converted to missing values.

Why It Is Used

This prevents invalid numeric values from affecting calculations.

For example:

negative costs would distort financial summaries.

Key Points

·  Numeric validation ensures logical values

·  Negative values replaced with missing

·  Improves data accuracy

·  Prevents calculation errors

·  Supports reliable analytics

·  Handles data entry mistakes

·  Ensures realistic business metrics

·  Protects financial reporting

·  Maintains dataset integrity

·  Prepares dataset for further processing

5. Creating Standardized Date Variables Using MDY Function

data library_dates;

set library_numeric_check;

Open_Date = mdy(1,1,2020);

Years_Active = intck('year', Open_Date, Join_Date);

format Open_Date date9.;

run;

proc print data=library_dates;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label	Open_Date	Years_Active
1	Central_library	Hyderabad		50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad	01JAN2020	0
2	City_library	Hyderabad		45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad	01JAN2020	0
3	Knowledge_hub	Vizag		30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag	01JAN2020	1
4	Digital_library	Chennai		40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai	01JAN2020	1
5	Reading_center	Bangalore		35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore	01JAN2020	1
6	Community_library	Pune		20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune	01JAN2020	1
7	Metro_library	Delhi		60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi	01JAN2020	2
8	Urban_library	Kolkata		42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata	01JAN2020	2
9	Study_zone	Hyderabad		.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad	01JAN2020	3
10	Smart_library	Bangalore		38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore	01JAN2020	3
11	City_readers	Chennai		0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai	01JAN2020	3
12	Digital_hub	Vizag		45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag	01JAN2020	3

Explanation

Dates are extremely important in operational datasets. Libraries track when members join, when services start, and how long systems have been active.

In this code we create a standard opening date for libraries and calculate how many years the library has been active.

Functions Used

MDY

Creates a SAS date value using:

MDY(month, day, year)

Example:

mdy(1,1,2020)

creates the date 01JAN2020.

INTCK

Calculates the number of intervals between two dates.

INTCK(interval,start_date,end_date)

Example:

INTCK('year', Open_Date, Join_Date)

This calculates the number of years between the library opening date and the join date.

Why This Code Is Important

Libraries often analyze service performance based on years of operation.

For example:

Older libraries may have:

·  higher membership

·  larger book collections

·  higher operating costs

This code enables those analyses.

Key Points

·  MDY creates SAS date values

·  Open_Date represents library start date

·  INTCK calculates time differences

·  Years_Active measures operational duration

·  FORMAT improves readability of dates

·  Date variables support trend analysis

·  Helps compare library performance over time

·  Enables lifecycle analytics

·  Important for operational reports

·  Date functions are widely used in SAS

6. Using INTNX For Future Planning Dates

data library_planning;

set library_dates;

Next_Audit_Date = intnx('year', Join_Date, 1, 'same');

format Next_Audit_Date date9.;

run;

proc print data=library_planning;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label	Open_Date	Years_Active	Next_Audit_Date
1	Central_library	Hyderabad		50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad	01JAN2020	0	12MAY2021
2	City_library	Hyderabad		45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad	01JAN2020	0	15JUN2021
3	Knowledge_hub	Vizag		30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag	01JAN2020	1	10JUL2022
4	Digital_library	Chennai		40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai	01JAN2020	1	20AUG2022
5	Reading_center	Bangalore		35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore	01JAN2020	1	11SEP2022
6	Community_library	Pune		20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune	01JAN2020	1	05OCT2022
7	Metro_library	Delhi		60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi	01JAN2020	2	17NOV2023
8	Urban_library	Kolkata		42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata	01JAN2020	2	22DEC2023
9	Study_zone	Hyderabad		.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad	01JAN2020	3	01JAN2024
10	Smart_library	Bangalore		38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore	01JAN2020	3	14FEB2024
11	City_readers	Chennai		0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai	01JAN2020	3	20MAR2024
12	Digital_hub	Vizag		45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag	01JAN2020	3	05APR2024

Explanation

Libraries conduct periodic audits of their operations and services.

This code calculates the next audit date, which is scheduled one year after the join date.

INTNX Function

INTNX moves a date forward or backward by a specified interval.

INTNX(interval,start_date,increment)

Example:

INTNX('year', Join_Date, 1)

This moves the date forward by one year.

Alignment Option

The option 'same' ensures that the same day and month are preserved.

Why This Code Is Used

Organizations need automated ways to schedule audits and inspections.

Using INTNX allows SAS to automatically generate future operational dates.

Key Points

·  INTNX shifts dates forward or backward

·  Used for scheduling future events

·  Helps plan library audits

·  Maintains consistent scheduling intervals

·  Prevents manual date calculations

·  Works with months, quarters, and years

·  Useful in financial forecasting

·  Important for operational planning

·  Reduces scheduling errors

·  Supports automated reporting systems

7. Utilization Classification

data library_utilization;

set library_planning;

Usage_Ratio = Digital_Usage / Members;

if Usage_Ratio >= 0.6 then Utilization_Class="High";

else if Usage_Ratio >=0.5 then Utilization_Class="Medium";

else Utilization_Class="Low";

run;

proc print data=library_utilization;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label	Open_Date	Years_Active	Next_Audit_Date	Usage_Ratio
1	Central_library	Hyderabad	High	50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad	01JAN2020	0	12MAY2021	0.60000
2	City_library	Hyderabad	Medium	45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad	01JAN2020	0	15JUN2021	0.57143
3	Knowledge_hub	Vizag	Medium	30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag	01JAN2020	1	10JUL2022	0.55556
4	Digital_library	Chennai	High	40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai	01JAN2020	1	20AUG2022	0.63636
5	Reading_center	Bangalore	High	35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore	01JAN2020	1	11SEP2022	0.60000
6	Community_library	Pune	Medium	20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune	01JAN2020	1	05OCT2022	0.56250
7	Metro_library	Delhi	High	60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi	01JAN2020	2	17NOV2023	0.66667
8	Urban_library	Kolkata	Medium	42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata	01JAN2020	2	22DEC2023	0.57692
9	Study_zone	Hyderabad	High	.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad	01JAN2020	3	01JAN2024	0.66667
10	Smart_library	Bangalore	High	38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore	01JAN2020	3	14FEB2024	0.63636
11	City_readers	Chennai	High	0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai	01JAN2020	3	20MAR2024	0.64706
12	Digital_hub	Vizag	Low	45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag	01JAN2020	3	05APR2024	.

Explanation

Library managers often measure how actively members use digital services.

We calculate a usage ratio.

Usage_Ratio = Digital_Usage / Members

This measures how many members actively use digital services.

Classification Logic

Ratio	Classification
>=0.6	High
>=0.5	Medium
<0.4	Low

Why This Is Important

This classification helps libraries understand how effectively digital resources are being used.

Libraries with low utilization may require:

·  digital awareness campaigns

·  training programs

·  improved infrastructure

Key Points

·  Usage_Ratio measures digital activity

·  IF-THEN logic creates classification

·  High utilization indicates strong digital engagement

·  Medium indicates moderate usage

·  Low indicates underutilization

·  Helps improve digital strategy

·  Supports performance benchmarking

·  Enables service optimization

·  Helps management decision-making

·  Improves operational efficiency

8. Fraud Detection Logic

data library_fraud_flag;

set library_utilization;

Fraud_Flag = 0;

if Digital_Usage > Members then Fraud_Flag = 1;

if Books_Count > 50000 then Fraud_Flag = 1;

if Percentage > 85 then Fraud_Flag = 1;

run;

proc print data=library_fraud_flag;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label	Open_Date	Years_Active	Next_Audit_Date	Usage_Ratio	Fraud_Flag
1	Central_library	Hyderabad	High	50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad	01JAN2020	0	12MAY2021	0.60000	0
2	City_library	Hyderabad	Medium	45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad	01JAN2020	0	15JUN2021	0.57143	0
3	Knowledge_hub	Vizag	Medium	30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag	01JAN2020	1	10JUL2022	0.55556	0
4	Digital_library	Chennai	High	40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai	01JAN2020	1	20AUG2022	0.63636	0
5	Reading_center	Bangalore	High	35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore	01JAN2020	1	11SEP2022	0.60000	0
6	Community_library	Pune	Medium	20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune	01JAN2020	1	05OCT2022	0.56250	0
7	Metro_library	Delhi	High	60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi	01JAN2020	2	17NOV2023	0.66667	1
8	Urban_library	Kolkata	Medium	42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata	01JAN2020	2	22DEC2023	0.57692	0
9	Study_zone	Hyderabad	High	.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad	01JAN2020	3	01JAN2024	0.66667	0
10	Smart_library	Bangalore	High	38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore	01JAN2020	3	14FEB2024	0.63636	0
11	City_readers	Chennai	High	0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai	01JAN2020	3	20MAR2024	0.64706	0
12	Digital_hub	Vizag	Low	45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag	01JAN2020	3	05APR2024	.	1

Explanation

Some libraries may manipulate statistics to obtain additional funding.

Examples include:

·  reporting more digital usage than members

·  inflating book counts

·  exaggerating service scores

This code identifies suspicious records.

Fraud Conditions

Condition 1

Digital_Usage > Members

This is logically impossible.

Condition 2

Books_Count > 50000

This may indicate inflated inventory numbers.

Condition 3

Percentage > 100

Fraud_Flag

0 = Normal record

1 = Suspicious record

Why This Code Is Important

Fraud detection is a critical part of modern data analytics systems.

Automated detection helps organizations:

·  prevent manipulation

·  ensure fair funding

·  maintain transparency

Key Points

·  Fraud_Flag identifies suspicious records

·  Logical validation ensures data integrity

·  Impossible values trigger flags

·  Helps auditors investigate anomalies

·  Prevents statistical manipulation

·  Supports regulatory compliance

·  Enables automated monitoring

·  Improves governance

·  Protects financial resources

·  Strengthens data reliability

9. Creating Fraud Detection Macro

%macro fraud_check(data=);

data fraud_result;

set &data;

length Fraud_Level $10.;

Fraud_Level="Normal";

if Fraud_Flag=1 then Fraud_Level="High Risk";

run;

proc print data=fraud_result;

run;

%mend;

%fraud_check(data=library_fraud_flag);

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label	Open_Date	Years_Active	Next_Audit_Date	Usage_Ratio	Fraud_Flag	Fraud_Level
1	Central_library	Hyderabad	High	50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad	01JAN2020	0	12MAY2021	0.60000	0	Normal
2	City_library	Hyderabad	Medium	45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad	01JAN2020	0	15JUN2021	0.57143	0	Normal
3	Knowledge_hub	Vizag	Medium	30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag	01JAN2020	1	10JUL2022	0.55556	0	Normal
4	Digital_library	Chennai	High	40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai	01JAN2020	1	20AUG2022	0.63636	0	Normal
5	Reading_center	Bangalore	High	35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore	01JAN2020	1	11SEP2022	0.60000	0	Normal
6	Community_library	Pune	Medium	20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune	01JAN2020	1	05OCT2022	0.56250	0	Normal
7	Metro_library	Delhi	High	60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi	01JAN2020	2	17NOV2023	0.66667	1	High Risk
8	Urban_library	Kolkata	Medium	42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata	01JAN2020	2	22DEC2023	0.57692	0	Normal
9	Study_zone	Hyderabad	High	.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad	01JAN2020	3	01JAN2024	0.66667	0	Normal
10	Smart_library	Bangalore	High	38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore	01JAN2020	3	14FEB2024	0.63636	0	Normal
11	City_readers	Chennai	High	0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai	01JAN2020	3	20MAR2024	0.64706	0	Normal
12	Digital_hub	Vizag	Low	45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag	01JAN2020	3	05APR2024	.	1	High Risk

Explanation

Macros allow SAS programmers to automate repetitive tasks.

Instead of writing fraud detection code repeatedly, we define a reusable macro.

Macro Components

%MACRO

Defines the macro.

&data

Macro variable representing input dataset.

%MEND

Ends the macro definition.

Why Macros Are Important

Macros are heavily used in enterprise SAS environments because they:

·  automate workflows

·  reduce code duplication

·  improve efficiency

Key Points

·  Macros automate SAS programs

·  %MACRO defines a reusable program

·  &data passes dataset names

·  %MEND ends macro definition

·  Reduces repetitive coding

·  Improves scalability

·  Used heavily in production systems

·  Enhances maintainability

·  Supports parameterized programs

·  Essential SAS programming skill

10. Creating Additional Library Dataset

data library_finance;

length Library_Name $25 Maintenance_Cost 8 Staff_Count 8;

input Library_Name $ Maintenance_Cost Staff_Count;

datalines;

Central_Library 50000 45

City_Library 45000 40

Knowledge_Hub 30000 30

Digital_Library 38000 35

Reading_Center 35000 32

Community_Library 20000 20

Metro_Library 60000 50

Urban_Library 42000 38

Study_Zone 31000 25

Smart_Library 39000 33

City_Readers 27000 22

Digital_Hub 41000 36

;

run;

proc print data=library_finance;

run;

OUTPUT:

Obs	Library_Name	Maintenance_Cost	Staff_Count
1	Central_Library	50000	45
2	City_Library	45000	40
3	Knowledge_Hub	30000	30
4	Digital_Library	38000	35
5	Reading_Center	35000	32
6	Community_Library	20000	20
7	Metro_Library	60000	50
8	Urban_Library	42000	38
9	Study_Zone	31000	25
10	Smart_Library	39000	33
11	City_Readers	27000	22
12	Digital_Hub	41000	36

Explanation

This code creates a second dataset representing financial information about libraries.

Variables include:

Library_Name
Maintenance_Cost
Staff_Count

This dataset simulates information coming from a financial management system.

In real organizations, operational data and financial data are usually stored in separate databases.

Why This Code Is Important

To perform meaningful analysis, data from multiple systems must be combined.

This dataset will later be merged with the operational library dataset.

Key Points

·  DATA step creates a new dataset

·  LENGTH defines variable attributes

·  INPUT reads financial data

·  DATALINES loads observations

·  Dataset represents financial metrics

·  Simulates external data source

·  Contains maintenance costs

·  Includes staff counts

·  Prepares dataset for merging

·  Demonstrates multi-source data environment

11. Combining Datasets Using MERGE

proc sort data=fraud_result;

by Library_Name;

run;

proc print data=fraud_result;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label	Open_Date	Years_Active	Next_Audit_Date	Usage_Ratio	Fraud_Flag	Fraud_Level
1	Central_library	Hyderabad	High	50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad	01JAN2020	0	12MAY2021	0.60000	0	Normal
2	City_library	Hyderabad	Medium	45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad	01JAN2020	0	15JUN2021	0.57143	0	Normal
3	City_readers	Chennai	High	0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai	01JAN2020	3	20MAR2024	0.64706	0	Normal
4	Community_library	Pune	Medium	20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune	01JAN2020	1	05OCT2022	0.56250	0	Normal
5	Digital_hub	Vizag	Low	45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag	01JAN2020	3	05APR2024	.	1	High Risk
6	Digital_library	Chennai	High	40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai	01JAN2020	1	20AUG2022	0.63636	0	Normal
7	Knowledge_hub	Vizag	Medium	30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag	01JAN2020	1	10JUL2022	0.55556	0	Normal
8	Metro_library	Delhi	High	60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi	01JAN2020	2	17NOV2023	0.66667	1	High Risk
9	Reading_center	Bangalore	High	35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore	01JAN2020	1	11SEP2022	0.60000	0	Normal
10	Smart_library	Bangalore	High	38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore	01JAN2020	3	14FEB2024	0.63636	0	Normal
11	Study_zone	Hyderabad	High	.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad	01JAN2020	3	01JAN2024	0.66667	0	Normal
12	Urban_library	Kolkata	Medium	42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata	01JAN2020	2	22DEC2023	0.57692	0	Normal

proc sort data=library_finance;

by Library_Name;

run;

proc print data=library_finance;

run;

OUTPUT:

Obs	Library_Name	Maintenance_Cost	Staff_Count
1	Central_Library	50000	45
2	City_Library	45000	40
3	City_Readers	27000	22
4	Community_Library	20000	20
5	Digital_Hub	41000	36
6	Digital_Library	38000	35
7	Knowledge_Hub	30000	30
8	Metro_Library	60000	50
9	Reading_Center	35000	32
10	Smart_Library	39000	33
11	Study_Zone	31000	25
12	Urban_Library	42000	38

data library_merged;

merge fraud_result library_finance;

by Library_Name;

run;

proc print data=library_merged;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label	Open_Date	Years_Active	Next_Audit_Date	Usage_Ratio	Fraud_Flag	Fraud_Level	Maintenance_Cost	Staff_Count
1	Central_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		50000	45
2	Central_library	Hyderabad	High	50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad	01JAN2020	0	12MAY2021	0.60000	0	Normal	.	.
3	City_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		45000	40
4	City_Readers			.	.	.	.	.	.	.	.				.	.	.	.	.		27000	22
5	City_library	Hyderabad	Medium	45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad	01JAN2020	0	15JUN2021	0.57143	0	Normal	.	.
6	City_readers	Chennai	High	0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai	01JAN2020	3	20MAR2024	0.64706	0	Normal	.	.
7	Community_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		20000	20
8	Community_library	Pune	Medium	20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune	01JAN2020	1	05OCT2022	0.56250	0	Normal	.	.
9	Digital_Hub			.	.	.	.	.	.	.	.				.	.	.	.	.		41000	36
10	Digital_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		38000	35
11	Digital_hub	Vizag	Low	45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag	01JAN2020	3	05APR2024	.	1	High Risk	.	.
12	Digital_library	Chennai	High	40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai	01JAN2020	1	20AUG2022	0.63636	0	Normal	.	.
13	Knowledge_Hub			.	.	.	.	.	.	.	.				.	.	.	.	.		30000	30
14	Knowledge_hub	Vizag	Medium	30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag	01JAN2020	1	10JUL2022	0.55556	0	Normal	.	.
15	Metro_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		60000	50
16	Metro_library	Delhi	High	60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi	01JAN2020	2	17NOV2023	0.66667	1	High Risk	.	.
17	Reading_Center			.	.	.	.	.	.	.	.				.	.	.	.	.		35000	32
18	Reading_center	Bangalore	High	35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore	01JAN2020	1	11SEP2022	0.60000	0	Normal	.	.
19	Smart_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		39000	33
20	Smart_library	Bangalore	High	38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore	01JAN2020	3	14FEB2024	0.63636	0	Normal	.	.
21	Study_Zone			.	.	.	.	.	.	.	.				.	.	.	.	.		31000	25
22	Study_zone	Hyderabad	High	.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad	01JAN2020	3	01JAN2024	0.66667	0	Normal	.	.
23	Urban_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		42000	38
24	Urban_library	Kolkata	Medium	42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata	01JAN2020	2	22DEC2023	0.57692	0	Normal	.	.

Explanation

The MERGE statement combines datasets based on a common variable.

Here the common variable is:

Library_Name

Before merging, both datasets must be sorted by the BY variable.

Why Sorting Is Required

SAS requires datasets to be sorted before performing a merge using BY statements.

If sorting is skipped, SAS may produce incorrect results.

Result

The merged dataset now contains:

Operational Data
+
Financial Data

This allows analysts to study relationships between library usage and financial resources.

Key Points

·  PROC SORT organizes datasets

·  BY statement defines merge key

·  MERGE combines datasets

·  Sorting ensures correct matching

·  Enables multi-source integration

·  Adds financial variables

·  Creates richer dataset

·  Supports advanced analysis

·  Common technique in data engineering

·  Critical skill for SAS programmers

12. Using SET Statement

data library_copy;

 set library_merged;

run;

proc print data=library_copy;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label	Open_Date	Years_Active	Next_Audit_Date	Usage_Ratio	Fraud_Flag	Fraud_Level	Maintenance_Cost	Staff_Count
1	Central_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		50000	45
2	Central_library	Hyderabad	High	50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad	01JAN2020	0	12MAY2021	0.60000	0	Normal	.	.
3	City_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		45000	40
4	City_Readers			.	.	.	.	.	.	.	.				.	.	.	.	.		27000	22
5	City_library	Hyderabad	Medium	45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad	01JAN2020	0	15JUN2021	0.57143	0	Normal	.	.
6	City_readers	Chennai	High	0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai	01JAN2020	3	20MAR2024	0.64706	0	Normal	.	.
7	Community_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		20000	20
8	Community_library	Pune	Medium	20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune	01JAN2020	1	05OCT2022	0.56250	0	Normal	.	.
9	Digital_Hub			.	.	.	.	.	.	.	.				.	.	.	.	.		41000	36
10	Digital_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		38000	35
11	Digital_hub	Vizag	Low	45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag	01JAN2020	3	05APR2024	.	1	High Risk	.	.
12	Digital_library	Chennai	High	40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai	01JAN2020	1	20AUG2022	0.63636	0	Normal	.	.
13	Knowledge_Hub			.	.	.	.	.	.	.	.				.	.	.	.	.		30000	30
14	Knowledge_hub	Vizag	Medium	30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag	01JAN2020	1	10JUL2022	0.55556	0	Normal	.	.
15	Metro_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		60000	50
16	Metro_library	Delhi	High	60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi	01JAN2020	2	17NOV2023	0.66667	1	High Risk	.	.
17	Reading_Center			.	.	.	.	.	.	.	.				.	.	.	.	.		35000	32
18	Reading_center	Bangalore	High	35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore	01JAN2020	1	11SEP2022	0.60000	0	Normal	.	.
19	Smart_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		39000	33
20	Smart_library	Bangalore	High	38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore	01JAN2020	3	14FEB2024	0.63636	0	Normal	.	.
21	Study_Zone			.	.	.	.	.	.	.	.				.	.	.	.	.		31000	25
22	Study_zone	Hyderabad	High	.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad	01JAN2020	3	01JAN2024	0.66667	0	Normal	.	.
23	Urban_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		42000	38
24	Urban_library	Kolkata	Medium	42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata	01JAN2020	2	22DEC2023	0.57692	0	Normal	.	.

Explanation

The SET statement reads observations from an existing dataset and creates a new dataset.

Here we create a copy of the merged dataset.

Why This Is Useful

Sometimes analysts want to:

·  preserve the original dataset

·  create working copies for transformations

The SET statement allows safe data manipulation.

Key Points

·  SET reads existing datasets

·  Creates new dataset copies

·  Prevents modification of original data

·  Useful for data transformations

·  Supports sequential data processing

·  Allows derived variable creation

·  Enables data pipeline design

·  Maintains data safety

·  Commonly used in SAS workflows

·  Fundamental SAS concept

13. Appending New Records

data new_library;

length Library_Name $25 City $20 Utilization_Class $15;

input Library_Name $ City $ Books_Count Members Digital_Usage 

      Operating_Cost Service_Score Percentage Fees Join_Date :ddmmyy10.;

datalines;

Future_Library Hyderabad 30000 1000 600 150000 7.5 75 150 01/05/2023

;

run;

proc print data=new_library;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date
1	Future_Library	Hyderabad		30000	1000	600	150000	7.5	75	150	23131

proc append base=library_copy 

            data=new_library;

run;

proc print data=library_copy;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label	Open_Date	Years_Active	Next_Audit_Date	Usage_Ratio	Fraud_Flag	Fraud_Level	Maintenance_Cost	Staff_Count
1	Central_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		50000	45
2	Central_library	Hyderabad	High	50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad	01JAN2020	0	12MAY2021	0.60000	0	Normal	.	.
3	City_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		45000	40
4	City_Readers			.	.	.	.	.	.	.	.				.	.	.	.	.		27000	22
5	City_library	Hyderabad	Medium	45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad	01JAN2020	0	15JUN2021	0.57143	0	Normal	.	.
6	City_readers	Chennai	High	0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai	01JAN2020	3	20MAR2024	0.64706	0	Normal	.	.
7	Community_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		20000	20
8	Community_library	Pune	Medium	20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune	01JAN2020	1	05OCT2022	0.56250	0	Normal	.	.
9	Digital_Hub			.	.	.	.	.	.	.	.				.	.	.	.	.		41000	36
10	Digital_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		38000	35
11	Digital_hub	Vizag	Low	45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag	01JAN2020	3	05APR2024	.	1	High Risk	.	.
12	Digital_library	Chennai	High	40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai	01JAN2020	1	20AUG2022	0.63636	0	Normal	.	.
13	Knowledge_Hub			.	.	.	.	.	.	.	.				.	.	.	.	.		30000	30
14	Knowledge_hub	Vizag	Medium	30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag	01JAN2020	1	10JUL2022	0.55556	0	Normal	.	.
15	Metro_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		60000	50
16	Metro_library	Delhi	High	60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi	01JAN2020	2	17NOV2023	0.66667	1	High Risk	.	.
17	Reading_Center			.	.	.	.	.	.	.	.				.	.	.	.	.		35000	32
18	Reading_center	Bangalore	High	35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore	01JAN2020	1	11SEP2022	0.60000	0	Normal	.	.
19	Smart_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		39000	33
20	Smart_library	Bangalore	High	38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore	01JAN2020	3	14FEB2024	0.63636	0	Normal	.	.
21	Study_Zone			.	.	.	.	.	.	.	.				.	.	.	.	.		31000	25
22	Study_zone	Hyderabad	High	.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad	01JAN2020	3	01JAN2024	0.66667	0	Normal	.	.
23	Urban_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		42000	38
24	Urban_library	Kolkata	Medium	42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata	01JAN2020	2	22DEC2023	0.57692	0	Normal	.	.
25	Future_Library	Hyderabad		30000	1000	600	150000	7.5	75	150	01/05/2023				.	.	.	.	.		.	.

 Explanation

Sometimes new data arrives after a dataset is already created.

The PROC APPEND procedure allows new records to be added to an existing dataset.

Instead of rebuilding the entire dataset, SAS simply adds the new observations.

Why APPEND Is Important

In production systems, datasets grow continuously.

APPEND provides a fast and efficient way to add new records.

Key Points

·  PROC APPEND adds records to dataset

·  BASE specifies existing dataset

·  DATA specifies new dataset

·  Faster than rebuilding dataset

·  Efficient for large datasets

·  Used in ETL pipelines

·  Supports incremental data loading

·  Prevents unnecessary recomputation

·  Widely used in production systems

·  Important SAS procedure

14. Using COALESCE Function

data library_coalesce;

set library_copy;

Members_Final = coalesce(Members,0);

run;

proc print data=library_coalesce;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label	Open_Date	Years_Active	Next_Audit_Date	Usage_Ratio	Fraud_Flag	Fraud_Level	Maintenance_Cost	Staff_Count	Members_Final
1	Central_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		50000	45	0
2	Central_library	Hyderabad	High	50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad	01JAN2020	0	12MAY2021	0.60000	0	Normal	.	.	1500
3	City_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		45000	40	0
4	City_Readers			.	.	.	.	.	.	.	.				.	.	.	.	.		27000	22	0
5	City_library	Hyderabad	Medium	45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad	01JAN2020	0	15JUN2021	0.57143	0	Normal	.	.	1400
6	City_readers	Chennai	High	0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai	01JAN2020	3	20MAR2024	0.64706	0	Normal	.	.	850
7	Community_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		20000	20	0
8	Community_library	Pune	Medium	20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune	01JAN2020	1	05OCT2022	0.56250	0	Normal	.	.	800
9	Digital_Hub			.	.	.	.	.	.	.	.				.	.	.	.	.		41000	36	0
10	Digital_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		38000	35	0
11	Digital_hub	Vizag	Low	45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag	01JAN2020	3	05APR2024	.	1	High Risk	.	.	0
12	Digital_library	Chennai	High	40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai	01JAN2020	1	20AUG2022	0.63636	0	Normal	.	.	1100
13	Knowledge_Hub			.	.	.	.	.	.	.	.				.	.	.	.	.		30000	30	0
14	Knowledge_hub	Vizag	Medium	30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag	01JAN2020	1	10JUL2022	0.55556	0	Normal	.	.	900
15	Metro_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		60000	50	0
16	Metro_library	Delhi	High	60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi	01JAN2020	2	17NOV2023	0.66667	1	High Risk	.	.	1800
17	Reading_Center			.	.	.	.	.	.	.	.				.	.	.	.	.		35000	32	0
18	Reading_center	Bangalore	High	35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore	01JAN2020	1	11SEP2022	0.60000	0	Normal	.	.	1000
19	Smart_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		39000	33	0
20	Smart_library	Bangalore	High	38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore	01JAN2020	3	14FEB2024	0.63636	0	Normal	.	.	1100
21	Study_Zone			.	.	.	.	.	.	.	.				.	.	.	.	.		31000	25	0
22	Study_zone	Hyderabad	High	.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad	01JAN2020	3	01JAN2024	0.66667	0	Normal	.	.	900
23	Urban_Library			.	.	.	.	.	.	.	.				.	.	.	.	.		42000	38	0
24	Urban_library	Kolkata	Medium	42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata	01JAN2020	2	22DEC2023	0.57692	0	Normal	.	.	1300
25	Future_Library	Hyderabad		30000	1000	600	150000	7.5	75	150	01/05/2023				.	.	.	.	.		.	.	1000

Explanation

The COALESCE function replaces missing values with the first non-missing value.

In this example:

If Members is missing, it will be replaced with 0.

Why This Is Important

Missing values can cause problems in calculations.

For example:

Average membership calculations may become incorrect.

Replacing missing values ensures stable analysis results.

Key Points

·  COALESCE handles missing values

·  Returns first non-missing value

·  Improves dataset completeness

·  Prevents calculation errors

·  Useful in large datasets

·  Helps data validation

·  Supports reliable analytics

·  Reduces null value problems

·  Commonly used numeric function

·  Essential for data cleaning

15. Transposing Dataset

proc transpose data=library_coalesce out=library_transpose;

var Books_Count Members Digital_Usage;

run;

proc print data=library_transpose;

run;

OUTPUT:

Obs	_NAME_	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18	COL19	COL20	COL21	COL22	COL23	COL24	COL25
1	Books_Count	.	50000	.	.	45000	0	.	20000	.	.	45000	40000	.	30000	.	60000	.	35000	.	38000	.	.	.	42000	30000
2	Members	.	1500	.	.	1400	850	.	800	.	.	.	1100	.	900	.	1800	.	1000	.	1100	.	900	.	1300	1000
3	Digital_Usage	.	900	.	.	800	550	.	450	.	.	900	700	.	500	.	1200	.	600	.	700	.	600	.	750	600

Explanation

Sometimes datasets must be restructured for reporting or visualization.

PROC TRANSPOSE converts rows into columns.

Example transformation:

Variable	Value
Books_Count
Members
Digital_Usage

becomes:

| Column1 | Column2 | Column3 |

Why This Is Important

Transposing data helps when creating:

·  reports

·  dashboards

·  statistical summaries

Key Points

·  PROC TRANSPOSE restructures datasets

·  Converts rows to columns

·  Useful for reports

·  Helps data visualization

·  Simplifies analysis

·  Supports pivot-style datasets

·  Enables flexible formatting

·  Used in reporting systems

·  Useful for summary tables

·  Important SAS procedure

16. Deleting Temporary Datasets

proc datasets library=work;

delete library_copy library_transpose new_library;

run;

quit;

LOG:

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

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

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

Explanation

During data processing, many temporary datasets are created.

These datasets consume memory and storage.

PROC DATASETS allows programmers to delete unnecessary datasets.

Why This Is Important

Efficient memory management is essential in large data environments.

Deleting unused datasets keeps the SAS workspace clean and efficient.

Key Points

·  PROC DATASETS manages datasets

·  DELETE removes datasets

·  WORK library stores temporary datasets

·  Helps memory management

·  Keeps environment clean

·  Improves processing efficiency

·  Reduces clutter

·  Important in production workflows

·  Prevents storage overflow

·  Essential data housekeeping step

17. Complete Corrected SAS Program

data library_final;

set library_raw;

Library_Name = propcase(strip(Library_Name));

City = propcase(strip(City));

City_Upper = upcase(City);

City_Lower = lowcase(City);

Library_Label = catx('-',Library_Name,City);

if Books_Count < 0 then Books_Count = .;

if Operating_Cost < 0 then Operating_Cost = .;

Members_Final = coalesce(Members,0);

Open_Date = mdy(1,1,2020);

Years_Active = intck('year', Open_Date, Join_Date);

Next_Audit_Date = intnx('year', Join_Date, 1, 'same');

Usage_Ratio = Digital_Usage / Members_Final;

if Usage_Ratio >=0.6 then Utilization_Class="High";

else if Usage_Ratio >=0.5 then Utilization_Class="Medium";

else Utilization_Class="Low";

Fraud_Flag = 0;

if Digital_Usage > Members_Final then Fraud_Flag=1;

if Books_Count > 50000 then Fraud_Flag=1;

if Percentage > 85 then Fraud_Flag=1;

format Open_Date Next_Audit_Date date9.;

run;

proc print data=library_final;

run;

OUTPUT:

Obs	Library_Name	City	Utilization_Class	Books_Count	Members	Digital_Usage	Operating_Cost	Service_Score	Percentage	Fees	Join_Date	City_Upper	City_Lower	Library_Label	Members_Final	Open_Date	Years_Active	Next_Audit_Date	Usage_Ratio	Fraud_Flag
1	Central_library	Hyderabad	High	50000	1500	900	200000	8.5	85	200	12/05/2020	HYDERABAD	hyderabad	Central_library-Hyderabad	1500	01JAN2020	0	12MAY2021	0.60000	0
2	City_library	Hyderabad	Medium	45000	1400	800	190000	8.0	80	180	15/06/2020	HYDERABAD	hyderabad	City_library-Hyderabad	1400	01JAN2020	0	15JUN2021	0.57143	0
3	Knowledge_hub	Vizag	Medium	30000	900	500	150000	7.2	72	150	10/07/2021	VIZAG	vizag	Knowledge_hub-Vizag	900	01JAN2020	1	10JUL2022	0.55556	0
4	Digital_library	Chennai	High	40000	1100	700	175000	7.8	78	170	20/08/2021	CHENNAI	chennai	Digital_library-Chennai	1100	01JAN2020	1	20AUG2022	0.63636	0
5	Reading_center	Bangalore	High	35000	1000	600	165000	7.5	75	160	11/09/2021	BANGALORE	bangalore	Reading_center-Bangalore	1000	01JAN2020	1	11SEP2022	0.60000	0
6	Community_library	Pune	Medium	20000	800	450	120000	6.8	68	120	05/10/2021	PUNE	pune	Community_library-Pune	800	01JAN2020	1	05OCT2022	0.56250	0
7	Metro_library	Delhi	High	60000	1800	1200	250000	9.0	90	220	17/11/2022	DELHI	delhi	Metro_library-Delhi	1800	01JAN2020	2	17NOV2023	0.66667	1
8	Urban_library	Kolkata	Medium	42000	1300	750	185000	8.1	81	190	22/12/2022	KOLKATA	kolkata	Urban_library-Kolkata	1300	01JAN2020	2	22DEC2023	0.57692	0
9	Study_zone	Hyderabad	High	.	900	600	160000	7.3	73	160	01/01/2023	HYDERABAD	hyderabad	Study_zone-Hyderabad	900	01JAN2020	3	01JAN2024	0.66667	0
10	Smart_library	Bangalore	High	38000	1100	700	.	7.6	76	170	14/02/2023	BANGALORE	bangalore	Smart_library-Bangalore	1100	01JAN2020	3	14FEB2024	0.63636	0
11	City_readers	Chennai	High	0	850	550	150000	7.0	70	150	20/03/2023	CHENNAI	chennai	City_readers-Chennai	850	01JAN2020	3	20MAR2024	0.64706	0
12	Digital_hub	Vizag	Low	45000	.	900	200000	8.3	83	200	05/04/2023	VIZAG	vizag	Digital_hub-Vizag	0	01JAN2020	3	05APR2024	.	1

Intentional Errors Introduced In The Raw Dataset

When the raw dataset library_raw was created earlier, several errors were intentionally inserted to simulate real-world data problems.

Below are the errors.

Error 1 — Negative Books Count

Example:

Study_Zone  -30000

Problem:

A library cannot have a negative number of books.

Reason for error:

Manual data entry mistake or corrupted system record.

Correction logic:

if Books_Count < 0 then Books_Count = .;

This replaces invalid values with missing values.

Error 2 — Negative Operating Cost

Example:

Smart_Library  Operating_Cost = -170000

Problem:

Operating costs cannot be negative.

Possible reason:

Financial system import error.

Correction:

if Operating_Cost < 0 then Operating_Cost = .;

Error 3 — Missing Members Value

Example:

Digital_Hub Members = .

Problem:

Membership information missing.

Correction using COALESCE:

Members_Final = coalesce(Members,0);

This replaces missing values with 0.

Error 4 — Zero Books Count

Example:

City_Readers Books_Count = 0

Problem:

Libraries normally contain at least some books.

Possible reason:

Data import failure.

Correction logic may flag such records for manual verification.

Error 5 — Inconsistent City Capitalization

Examples:

hyderabad

Hyderabad

HYDERABAD

Problem:

Inconsistent text formatting.

Correction:

City = propcase(strip(City));

This standardizes city names.

Error 6 — Fraudulent Usage Numbers

Example condition:

Digital_Usage > Members

Problem:

Digital usage cannot exceed total membership.

Correction logic:

Flag suspicious records using Fraud_Flag variable.

Final Dataset Utilization

The library_final dataset can be used for several analyses.

Examples:

Library Performance Analysis

Compare service scores across cities.

Cost Efficiency Analysis

Evaluate operating costs relative to membership size.

Digital Engagement Analysis

Measure digital usage levels.

Fraud Detection

Identify suspicious records.

Strategic Planning

Plan future library expansion based on utilization patterns.

Explanation

This code performs complete data cleaning and fraud detection.

The main steps include:

·  Character standardization

·  Numeric error correction

·  Missing value handling

·  Date variable creation

·  Utilization classification

·  Fraud detection

This integrated program represents a real-world SAS data processing pipeline.

Why This Final Code Is Important

In real analytics environments, SAS programmers must combine multiple tasks in a single workflow:

·  data cleaning

·  validation

·  transformation

·  fraud detection

This final program demonstrates how those steps can be integrated into one process.

18.20 Key Points About This Project

·  The project simulates a public library data management system.

·  A raw dataset with intentional errors was created.

·  Errors included negative values and missing data.

·  Character variables were standardized using PROPCASE and STRIP.

·  Text case conversions were performed using UPCASE and LOWCASE.

·  New identifiers were created using CATX.

·  Numeric validation removed invalid values.

·  Missing membership values were handled using COALESCE.

·  Date variables were generated using MDY.

·  Library age was calculated using INTCK.

·  Future audit dates were scheduled using INTNX.

·  Digital service usage ratios were calculated.

·  Libraries were classified into utilization categories.

·  Fraud detection rules were implemented.

·  Suspicious records were flagged automatically.

·  Multiple datasets were combined using MERGE.

·  Additional records were added using PROC APPEND.

·  Data structure was reshaped using PROC TRANSPOSE.

·  Temporary datasets were removed using PROC DATASETS DELETE.

·  The final dataset supports advanced library analytics.

Project Summary

This project demonstrates how SAS programming can be used to manage and validate operational data in public library systems.

The project began with a raw dataset containing several intentional errors to simulate real-world data quality problems. These included negative numeric values, missing membership counts, inconsistent capitalization, and suspicious digital usage statistics.

Through a series of SAS programs, the dataset was cleaned and transformed using a combination of:

·  character functions

·  numeric validation rules

·  date functions

·  dataset merging

·  macros

·  fraud detection logic

The final dataset provides a reliable foundation for analyzing library operations, evaluating digital engagement, monitoring operational costs, and identifying suspicious reporting patterns.

This type of data pipeline is common in modern analytics environments where data quality and governance are critical for accurate decision-making.

Conclusion

Accurate data is essential for the effective management of public services such as libraries. Poor data quality can lead to incorrect funding decisions, inefficient resource allocation, and misleading performance evaluations.

This project illustrated how advanced SAS programming techniques can be applied to detect and correct errors in operational datasets. By combining data cleaning techniques, date processing functions, character manipulation functions, and automated fraud detection rules, SAS programmers can transform unreliable raw data into a trustworthy analytical dataset.

The approach demonstrated in this project mirrors real-world analytics workflows used by organizations to maintain data integrity and transparency. With proper validation processes and automated monitoring systems, institutions can ensure that their operational data remains accurate, reliable, and suitable for strategic decision-making.

SAS INTERVIEW QUESTIONS

1. What is a Cartesian join?

Answer:
A Cartesian join occurs when every row of one table joins with every row of another table.

2. What is DISTINCT used for?

Answer:
DISTINCT removes duplicate rows from query results in Sql.

3. What is the difference between UNION and OUTER UNION?

Answer:
UNION removes duplicates, while OUTER UNION keeps all rows including duplicates.

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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 PUBLIC LIBRARY data.

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

1.Can SAS Programming Reveal Hidden Popularity Trends in Video Games Using DATA Step, PROC SQL, Ranking Analytics, and Automated Popularity Grading Macros?

2.Can SAS Programming Reveal Shopping Behavior and Spending Patterns of Families in Hyderabad Malls Using Data Step Processing, SQL Analytics, and Automated Summary Macros?

3.How Do Advanced SAS Procedures and Data Visualization Techniques Reveal Hidden Patterns Among Different Types of People Across India?

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