427.Can SAS’s Precision and R’s Flexibility Together Eliminate Hidden Errors in Orbital Debris Data Analytics?

Advanced Data Validation and Transformation Techniques Across SAS and R

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Statements Used in Both SAS and R (Equivalent Mapping Format)

DATA CREATION — DATA STEP | LENGTH | INPUT | DATALINES → data.frame() | tibble()

DATA READING / MANIPULATION — SET → <- (assignment) | mutate()

DATA JOINING — MERGE → merge() | left_join()

DATA SORTING — PROC SORT → arrange() | order()

DATA DISPLAY — PROC PRINT → print()

DATA STRUCTURE VIEW — PROC CONTENTS → str() | summary()

FREQUENCY ANALYSIS — PROC FREQ → table() | count()

DATA RESHAPING — PROC TRANSPOSE → pivot_longer() | pivot_wider()

DATA APPENDING — PROC APPEND → rbind() | bind_rows()

DATA DELETION — PROC DATASETS DELETE → rm()

AUTOMATION — MACRO | %MACRO | %MEND → function()

NUMERIC FUNCTIONS — ROUND | LOG | ABS → round() | log() | abs()

CHARACTER FUNCTIONS — STRIP | TRIM | CAT | CATX | UPCASE | LOWCASE | PROPCASE → trimws() | paste() | paste0() | toupper() | tolower() | tools::toTitleCase()

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Introduction

Orbital debris—commonly known as space junk—is one of the most critical challenges in modern space operations. With thousands of satellites and millions of debris fragments orbiting Earth, tracking and managing this data is essential for collision avoidance, mission safety, and regulatory compliance.

In real-world analytics environments (especially aerospace and defense domains), datasets are rarely clean. They contain:

• Missing values
• Incorrect formats
• Logical inconsistencies
• Fraudulent or manipulated entries

This project demonstrates how Advanced SAS Programming can:

• Create a complex orbital debris dataset
• Introduce intentional data errors
• Detect and correct those errors
• Apply transformations using SAS procedures, functions, macros
• Replicate the same logic in R programming

Table of Contents

1. Business Context
2. Raw Dataset Creation (With Errors)
3. Error Identification
4. Data Cleaning Using SAS
5. Date Handling (MDY, INTCK, INTNX)
6. Character Functions
7. Numeric Functions
8. Dataset Operations (SET, MERGE, APPEND)
9. PROC TRANSPOSE
10. Fraud Detection Macros
11. Utilization Classification
12. PROC DATASETS DELETE
13. Final Clean Dataset
14. R Code Equivalent
15. SAS VS R
16. 10 Key Points
17. Summary
18. Conclusion

Business Context

Organizations like space agencies and satellite operators rely on accurate orbital data to:

• Prevent satellite collisions
• Estimate risk probabilities
• Optimize tracking resource allocation
• Detect anomalies or fraudulent data entries

A small error (e.g., wrong velocity) can lead to catastrophic mission failure.

1. Raw Dataset Creation (With Intentional Errors)

SAS Code

data orbital_debris_raw;

    input Object_ID $ Orbit_Altitude Velocity_km_s Collision_Risk $ 

          Object_Size Tracking_Agency $ Fees Utilization $ Launch_Date $10.;

    datalines;

OBJ001 400 7.8 HIGH 2 NASA 5000 HIGH 01-01-2023

OBJ002 . 8.1 MEDIUM 3 ISRO 4500 MEDIUM 15-02-2023

OBJ003 550 -7.5 LOW 1 ESA 3000 LOW 20-03-2023

OBJ004 700 8.5 HIGH . NASA 6000 HIGH 05-04-2023

OBJ005 800 abc MEDIUM 5 JAXA 5500 MEDIUM 10-05-2023

OBJ006 650 7.9 LOW 2 ISRO . LOW 15-06-2023

;

run;

LOG:

77 OBJ005 800 abc MEDIUM 5 JAXA 5500 MEDIUM 10-05-2023

Object_ID=OBJ005 Orbit_Altitude=800 Velocity_km_s=. Collision_Risk=MEDIUM Object_Size=5 Tracking_Agency=JAXA Fees=5500

Utilization=MEDIUM Launch_Date=10-05-2023 _ERROR_=1 _N_=5

NOTE: The data set WORK.ORBITAL_DEBRIS_RAW has 6 observations and 9 variables.

Explanation

This dataset contains intentional errors:

·  Missing values (.)

·  Invalid numeric value (abc)

·  Negative velocity

·  Missing object size

·  Missing fees

Why This Step is Important

·  Simulates real-world messy data

·  Helps in testing validation logic

R Code

orbital_debris_raw <- data.frame(

  Object_ID = c("OBJ001","OBJ002","OBJ003","OBJ004","OBJ005","OBJ006"),

  Orbit_Altitude = c(400, NA, 550, 700, 800, 650),

  Velocity_km_s = c("7.8","8.1","-7.5","8.5","abc","7.9"),

  Collision_Risk = c("HIGH"."MEDIUM","LOW","HIGH","MEDIUM","LOW"),

  Object_Size = c(2,3,1,NA,5,2),

  Trackint_Agency = c("NASA","ISRO","ESA","NASA","JAXA","ISRO"),

  Fees = c(5000,4500,3000,6000,5500,NA),

  Utilization = c("HIGH","MEDIUM","LOW","HIGH","MEDIUM","LOW"),

  Launch_Date = c("01-01-2023","15-02-2023","20-03-2023",

                  "05-042023","10-05-2023","15-06-2023"),

  stringAsFactors = FALSE 

)

CONSOLE:

> orbital_debris_raw <- data.frame(
+   Object_ID = c("OBJ001","OBJ002","OBJ003","OBJ004","OBJ005","OBJ006"),
+   Orbit_Altitude = c(400, NA, 550, 700, 800, 650),
+   Velocity_km_s = c("7.8","8.1","-7.5","8.5","abc","7.9"),
+   Collision_Risk = c("HIGH","MEDIUM","LOW","HIGH","MEDIUM","LOW"),
+   Object_Size = c(2,3,1,NA,5,2),
+   Tracking_Agency = c("NASA","ISRO","ESA","NASA","JAXA","ISRO"),
+   Fees = c(5000,4500,3000,6000,5500,NA),
+   Utilization = c("HIGH","MEDIUM","LOW","HIGH","MEDIUM","LOW"),
+   Launch_Date = c("01-01-2023","15-02-2023","20-03-2023",
+                   "05-04-2023","10-05-2023","15-06-2023"),
+   stringsAsFactors = FALSE
+ )

Explanation
·   SAS uses INPUT+ DATALINES
·   R uses data.frame( )
·  Velocity intentionally stored as character (to simulate error like abc)

2. Identifying Errors

SAS Code

proc print data=orbital_debris_raw;

run;

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date
1	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023
2	OBJ002	.	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023
3	OBJ003	550	-7.5	LOW	1	ESA	3000	LOW	20-03-2023
4	OBJ004	700	8.5	HIGH	.	NASA	6000	HIGH	05-04-2023
5	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023
6	OBJ006	650	7.9	LOW	2	ISRO	.	LOW	15-06-2023

Errors Identified

·  Missing Orbit_Altitude

·  Negative Velocity

·  Character value in numeric field

·  Missing Object_Size

·  Missing Fees

R Code

print(orbital_debris_raw)

R OUTPUT:

Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Trackint_Agency	Fees	Utilization	Launch_Date	stringAsFactors
OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	FALSE
OBJ002		8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	FALSE
OBJ003	550	-7.5	LOW	1	ESA	3000	LOW	20-03-2023	FALSE
OBJ004	700	8.5	HIGH		NASA	6000	HIGH	05-04-2023	FALSE
OBJ005	800	abc	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	FALSE
OBJ006	650	7.9	LOW	2	ISRO		LOW	15-06-2023	FALSE

3. Data Cleaning and Correction

SAS Code

data orbital_debris_clean;

    set orbital_debris_raw;

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

    if Orbit_Altitude = . then Orbit_Altitude = 500;

    if Object_Size = . then Object_Size = 2;

    if Fees = . then Fees = 4000;

    Velocity_km_s = input(Velocity_km_s, best12.);

run;

proc print data=orbital_debris_clean;

run;

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date
1	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023
2	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023
3	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023
4	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023
5	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023
6	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023

Explanation

·  ABS( ) corrects negative values

·  Default values assigned to missing fields

·  INPUT( ) converts character to numeric

Why Used

·  Ensures consistency

·  Prevents downstream errors

R Code

library(dplyr)

orbital_debris_clean <- orbital_debris_raw %>%

  mutate(

    Velocity_km_s = as.numeric(Velocity_km_s),

    Velocity_km_s = ifelse(Velocity_km_s < 0 , abs(Velocity_km_s),Velocity_km_s),

    Orbit_Altitude = ifelse(is.na(Orbit_Altitude),500,Orbit_Altitude),

    Object_Size = ifelse(is.na(Object_Size),2,Object_Size),

    Fees = ifelse(is.na(Fees),4000,Fees)

  )

print(orbital_debris_clean)

Explanation

·  input( ) → as.numeric( )

·  if → ifelse( )

·  Handles invalid values (abc → NA)

4. Date Handling (MDY, INTCK, INTNX)

SAS Code

data orbital_dates;

    set orbital_debris_clean;

    Launch_Date_SAS = input(Launch_Date, ddmmyy10.);

    format Launch_Date_SAS date9.;

    Today = today();

    Age_Days = intck('day', Launch_Date_SAS, Today);

    Next_Check = intnx('month', Launch_Date_SAS, 6);

    format Next_Check date9.;

run;

proc print data=orbital_dates;

run;

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date	Launch_Date_SAS	Today	Age_Days	Next_Check
1	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023
2	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023
3	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023
4	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023
5	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023
6	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023

Explanation

·  INPUT ( ) converts character date

·  INTCK ( ) calculates difference

·  INTNX ( ) calculates future date

Why Used

·  Time-based analysis is critical in orbital tracking

R Code

orbital_dates <- orbital_debris_clean %>%

  mutate(

    Launch_Date_SAS = dmy(Launch_Date),

    Today = Sys.Date(),

    Age_Days = as.numeric(Today - Launch_Date_SAS),

    Next_Check = Launch_Date_SAS %m+% months(6) 

  )

print(orbital_dates)

5. Character Functions

SAS Code

data char_functions;

    set orbital_dates;

    Agency_Clean = propcase(strip(Tracking_Agency));

    Risk_Upper = upcase(Collision_Risk);

    Risk_Lower = lowcase(Collision_Risk);

    Combined = catx('-', Object_ID, Agency_Clean);

run;

proc print data=char_functions;

run;

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date	Launch_Date_SAS	Today	Age_Days	Next_Check	Agency_Clean	Risk_Upper	Risk_Lower	Combined
1	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa
2	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro
3	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa
4	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa
5	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa
6	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro

Functions Used

·  STRIP ( ) removes spaces

·  PROPCASE ( ) formats text

·  CATX ( ) concatenates

6. Numeric Functions

SAS Code

data numeric_functions;

    set char_functions;

    Risk_Score = round(Velocity_km_s * Object_Size, 0.1);

    Log_Fees = log(Fees);

run;

proc print data=numeric_functions;

run;

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date	Launch_Date_SAS	Today	Age_Days	Next_Check	Agency_Clean	Risk_Upper	Risk_Lower	Combined	Risk_Score	Log_Fees
1	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719
2	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183
3	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637
4	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951
5	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250
6	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405

Explanation

·  Calculates derived metrics

·  Useful for risk modeling

7. SET Operation

SAS Code

data combined_data;

    set numeric_functions;

run;

proc print data=combined_data;

run;

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date	Launch_Date_SAS	Today	Age_Days	Next_Check	Agency_Clean	Risk_Upper	Risk_Lower	Combined	Risk_Score	Log_Fees
1	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719
2	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183
3	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637
4	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951
5	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250
6	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405

Why Used

·  Combines datasets sequentially

8. SORT & MERGE Operation

SAS Code

data agency_info;

    input Tracking_Agency $ Country $;

    datalines;

NASA USA

ISRO INDIA

ESA EUROPE

JAXA JAPAN

;

run;

proc print data=agency_info;

run;

OUTPUT:

Obs	Tracking_Agency	Country
1	NASA	USA
2	ISRO	INDIA
3	ESA	EUROPE
4	JAXA	JAPAN

proc sort data=combined_data;by Tracking_Agency;run;

proc print data=combined_data;

run;

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date	Launch_Date_SAS	Today	Age_Days	Next_Check	Agency_Clean	Risk_Upper	Risk_Lower	Combined	Risk_Score	Log_Fees
1	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637
2	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183
3	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405
4	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250
5	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719
6	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951

proc sort data=agency_info;by Tracking_Agency;run;

proc print data=agency_info;

run;

OUTPUT:

Obs	Tracking_Agency	Country
1	ESA	EUROPE
2	ISRO	INDIA
3	JAXA	JAPAN
4	NASA	USA

data merged_data;

    merge combined_data 

          agency_info;

    by Tracking_Agency;

run;

proc print data=merged_data;

run;

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date	Launch_Date_SAS	Today	Age_Days	Next_Check	Agency_Clean	Risk_Upper	Risk_Lower	Combined	Risk_Score	Log_Fees	Country
1	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637	EUROPE
2	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183	INDIA
3	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405	INDIA
4	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250	JAPAN
5	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719	USA
6	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951	USA

Explanation

·  Joins datasets

·  Adds metadata

9. APPEND Operation

SAS Code

proc append base=merged_data 

            data=combined_data;

run;

proc print data=merged_data;

run;

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date	Launch_Date_SAS	Today	Age_Days	Next_Check	Agency_Clean	Risk_Upper	Risk_Lower	Combined	Risk_Score	Log_Fees	Country
1	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637	EUROPE
2	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183	INDIA
3	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405	INDIA
4	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250	JAPAN
5	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719	USA
6	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951	USA
7	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637
8	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183
9	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405
10	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250
11	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719
12	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951

Why Used

·  Adds records efficiently

10. PROC TRANSPOSE

SAS Code

proc transpose data=merged_data out=transposed_data;

    by Object_ID NotSorted;

    var Velocity_km_s Object_Size Fees;

run;

proc print data=transposed_data;

run;

OUTPUT:

Obs	Object_ID	_NAME_	COL1
1	OBJ003	Velocity_km_s	7.5
2	OBJ003	Object_Size	1.0
3	OBJ003	Fees	3000.0
4	OBJ002	Velocity_km_s	8.1
5	OBJ002	Object_Size	3.0
6	OBJ002	Fees	4500.0
7	OBJ006	Velocity_km_s	7.9
8	OBJ006	Object_Size	2.0
9	OBJ006	Fees	4000.0
10	OBJ005	Velocity_km_s	.
11	OBJ005	Object_Size	5.0
12	OBJ005	Fees	5500.0
13	OBJ001	Velocity_km_s	7.8
14	OBJ001	Object_Size	2.0
15	OBJ001	Fees	5000.0
16	OBJ004	Velocity_km_s	8.5
17	OBJ004	Object_Size	2.0
18	OBJ004	Fees	6000.0
19	OBJ003	Velocity_km_s	7.5
20	OBJ003	Object_Size	1.0
21	OBJ003	Fees	3000.0
22	OBJ002	Velocity_km_s	8.1
23	OBJ002	Object_Size	3.0
24	OBJ002	Fees	4500.0
25	OBJ006	Velocity_km_s	7.9
26	OBJ006	Object_Size	2.0
27	OBJ006	Fees	4000.0
28	OBJ005	Velocity_km_s	.
29	OBJ005	Object_Size	5.0
30	OBJ005	Fees	5500.0
31	OBJ001	Velocity_km_s	7.8
32	OBJ001	Object_Size	2.0
33	OBJ001	Fees	5000.0
34	OBJ004	Velocity_km_s	8.5
35	OBJ004	Object_Size	2.0
36	OBJ004	Fees	6000.0

Explanation

·  Converts rows → columns

11. Fraud Detection Macro

SAS Code

%macro fraud_check(data=);

data fraud_flag;

    set &data;

    if Fees > 7000 or Velocity_km_s > 7 then Fraud_Flag = 1;

    else Fraud_Flag = 0;

run;

proc print data=fraud_flag;

run;

%mend;

%fraud_check(data=merged_data);

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date	Launch_Date_SAS	Today	Age_Days	Next_Check	Agency_Clean	Risk_Upper	Risk_Lower	Combined	Risk_Score	Log_Fees	Country	Fraud_Flag
1	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637	EUROPE	1
2	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183	INDIA	1
3	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405	INDIA	1
4	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250	JAPAN	0
5	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719	USA	1
6	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951	USA	1
7	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637		1
8	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183		1
9	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405		1
10	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250		0
11	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719		1
12	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951		1

Explanation

·  Flags abnormal values

·  Automates validation

12. Utilization Classification

SAS Code

data utilization_class;

    set fraud_flag;

    if Utilization = 'HIGH' then Util_Class = 3;

    else if Utilization = 'MEDIUM' then Util_Class = 2;

    else Util_Class = 1;

run;

proc print data=utilization_class;

run;

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date	Launch_Date_SAS	Today	Age_Days	Next_Check	Agency_Clean	Risk_Upper	Risk_Lower	Combined	Risk_Score	Log_Fees	Country	Fraud_Flag	Util_Class
1	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637	EUROPE	1	1
2	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183	INDIA	1	2
3	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405	INDIA	1	1
4	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250	JAPAN	0	2
5	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719	USA	1	3
6	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951	USA	1	3
7	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637		1	1
8	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183		1	2
9	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405		1	1
10	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250		0	2
11	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719		1	3
12	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951		1	3

13. PROC DATASETS DELETE

SAS Code

proc datasets library=work;

    delete orbital_debris_raw;

quit;

LOG:

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

Why Used

·  Cleans temporary datasets

14. Final Corrected Dataset

SAS Code

data final_dataset;

    set utilization_class;

run;

proc print data=final_dataset;

run;

OUTPUT:

Obs	Object_ID	Orbit_Altitude	Velocity_km_s	Collision_Risk	Object_Size	Tracking_Agency	Fees	Utilization	Launch_Date	Launch_Date_SAS	Today	Age_Days	Next_Check	Agency_Clean	Risk_Upper	Risk_Lower	Combined	Risk_Score	Log_Fees	Country	Fraud_Flag	Util_Class
1	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637	EUROPE	1	1
2	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183	INDIA	1	2
3	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405	INDIA	1	1
4	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250	JAPAN	0	2
5	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719	USA	1	3
6	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951	USA	1	3
7	OBJ003	550	7.5	LOW	1	ESA	3000	LOW	20-03-2023	20MAR2023	24185	1096	01SEP2023	Esa	LOW	low	OBJ003-Esa	7.5	8.00637		1	1
8	OBJ002	500	8.1	MEDIUM	3	ISRO	4500	MEDIUM	15-02-2023	15FEB2023	24185	1129	01AUG2023	Isro	MEDIUM	medium	OBJ002-Isro	24.3	8.41183		1	2
9	OBJ006	650	7.9	LOW	2	ISRO	4000	LOW	15-06-2023	15JUN2023	24185	1009	01DEC2023	Isro	LOW	low	OBJ006-Isro	15.8	8.29405		1	1
10	OBJ005	800	.	MEDIUM	5	JAXA	5500	MEDIUM	10-05-2023	10MAY2023	24185	1045	01NOV2023	Jaxa	MEDIUM	medium	OBJ005-Jaxa	.	8.61250		0	2
11	OBJ001	400	7.8	HIGH	2	NASA	5000	HIGH	01-01-2023	01JAN2023	24185	1174	01JUL2023	Nasa	HIGH	high	OBJ001-Nasa	15.6	8.51719		1	3
12	OBJ004	700	8.5	HIGH	2	NASA	6000	HIGH	05-04-2023	05APR2023	24185	1080	01OCT2023	Nasa	HIGH	high	OBJ004-Nasa	17.0	8.69951		1	3

15. R Code

library(dplyr)

orbital_data <- data.frame(

  Object_ID = c("OBJ001","OBJ002","OBJ003","OBJ004","OBJ005","OBJ006"),

  Orbit_Altitude = c(400, NA, 550, 700, 800, 650),

  Velocity_km_s = c(7.8, 8.1, -7.5, 8.5, NA, 7.9),

  Fees = c(5000,4500,3000,6000,5500,NA)

)

orbital_data <- orbital_data %>%

  mutate(

    Velocity_km_s = abs(Velocity_km_s),

    Orbit_Altitude = ifelse(is.na(Orbit_Altitude), 500, Orbit_Altitude),

    Fees = ifelse(is.na(Fees), 4000, Fees),

    Risk_Score = round(Velocity_km_s * 2,1)

  )

print(orbital_data)

R OUTPUT:

Object_ID           Orbit_Altitude   Velocity_km_s   Fees      Risk_Score

OBJ001               400                           7.8                   5000      15.6

OBJ002               500                           8.1                  4500       16.2

OBJ003               550                           7.5                   3000      15

OBJ004               700                           8.5                   6000      17

OBJ005               800                                                    5500   

OBJ006              650                            7.9                   4000     15.8

SAS vs R — Direct Comparison

1. Data Input Handling

·  SAS uses INPUT + DATALINES (structured, fixed schema)

·  R uses data.frame( ) (flexible, dynamic typing)

2. Error Behavior

·  SAS logs errors (_ERROR_=1) but continues execution

·  R silently converts invalid values to NA with warnings

3. Type Conversion

·  SAS: INPUT( ) function explicitly converts character → numeric

·  R: as.numeric( ) performs conversion but introduces NA automatically

4. Missing Value Handling

·  SAS uses . for numeric missing

·  R uses NA (more explicit and consistent across types)

5. Conditional Logic

·  SAS uses IF-THEN statements

·  R uses ifelse( ) or case_when( )

6. String Processing

·  SAS: STRIP, PROPCASE, UPCASE

·  R: trimws(), tools::toTitleCase(), toupper()

7.  Date Handling

·  SAS: INPUT, INTCK, INTNX (very powerful for intervals)

·  R: as.Date( ), arithmetic operations (simpler but less domain-specific)

8. Dataset Operations

·  SAS: SET, MERGE, PROC APPEND

·  R: dplyr, merge( ), rbind( ) (more flexible but requires packages)

9. Macro vs Functional Programming

·  SAS Macros automate workflows (%macro)

·  R uses functions and pipelines (dplyr, %>%)

10. Debugging & Auditability

·  SAS provides detailed logs (industry-grade validation)

·  R provides warnings but less structured audit trails

10 Key Points

1.     We created an orbital debris dataset with real-world variables like altitude, velocity, and risk.

2.     Intentional errors (like “abc” in velocity) were introduced to simulate real data issues.

3.     SAS detected errors using log messages and _ERROR_ variable.

4.     R handled the same error by converting invalid values into NA.

5.     Data cleaning was performed by replacing missing and incorrect values.

6.     Date variables were transformed and analyzed using SAS and R functions.

7.     Character and numeric transformations were applied for standardization.

8.     Dataset operations like merge, append, and transpose were executed.

9.     Fraud detection logic was implemented using SAS macros and R conditions.

10.  Final dataset was cleaned, validated, and ready for analysis.

Summary

In this project, we worked with orbital debris data that had multiple errors, just like real-world datasets. SAS handled the errors in a very structured way by showing detailed logs and continuing execution without stopping the process. This makes SAS very reliable in industries like clinical trials and aerospace where data validation is critical.

On the other hand, R provided a more flexible and modern approach. It handled errors quietly by converting invalid values into NA, which makes it easier for quick data analysis but slightly less controlled compared to SAS.

Overall, SAS is strong in data governance, validation, and audit trails, while R is powerful in flexibility, speed, and modern data manipulation. Both tools complement each other, and understanding both gives a strong advantage in real-world data projects.

Conclusion

This project clearly demonstrates that Advanced SAS Programming is extremely powerful in handling real-world data challenges. Through structured steps, we:

·  Created a dataset with intentional errors

·  Detected inconsistencies

·  Applied correction logic

·  Used advanced functions and macros

·  Ensured data integrity

·  Replicated logic in R

Key Takeaways

·  SAS excels in data cleaning and validation

·  Macros automate complex logic

·  Functions ensure precision

·  Procedures enable structured analysis

SAS INTERVIEW QUESTIONS

1. How do FIRST. and LAST. work?

Answer:

FIRST. and LAST. are temporary variables automatically created by SAS when using a BY statement after sorting data.

• FIRST.variable = 1 for the first observation of each BY group
• LAST.variable = 1 for the last observation of each BY group

Example: Used for group totals or identifying first/last records in a group.

2. Difference between CALL SYMPUT and CALL SYMPUTX?

Answer:

Feature	CALL SYMPUT	CALL SYMPUTX
Spaces	Keeps leading/trailing spaces	Removes spaces automatically
Type conversion	May cause numeric-to-character notes	Automatically converts values
Usage	Older method	Recommended method

Short: CALL SYMPUTX is an improved version of CALL SYMPUT.

3. Difference between INPUT and PUT functions?

Answer:

Function	Purpose
INPUT	Converts     character → numeric
PUT	Converts     numeric → character

Example:

• INPUT(char_var,8.) → character to numeric
• PUT(num_var,8.) → numeric to character.

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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 DIGITAL ADVERTISING 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

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