Mastering Data Cleaning with a Global Magazine Dataset Using SAS & R

From Global Magazine Rankings to Enterprise-Ready Intelligence: Cleaning the Best Magazines in the World Dataset Using SAS and R

1.Business Crisis Scenario

A global publishing company prepared an executive dashboard ranking the Best Magazines in the World based on circulation, subscription revenue, readership, and editorial category. During quarterly reporting, executives discovered duplicate magazine IDs, invalid launch dates, inconsistent country names, missing circulation values, malformed publisher emails, and negative subscription prices. These issues produced incorrect rankings, misleading AI predictions, inaccurate revenue forecasts, and unreliable dashboards.

In regulated industries such as clinical research, similar problems can delay regulatory submissions and compromise SDTM or ADaM deliverables. Therefore, enterprise data cleaning is not simply cosmetic it is the foundation of trustworthy analytics.

2.Raw Dataset

Variable	Description
Magazine_ID	Unique identifier
Magazine_Name	Magazine title
Country	Publishing country
Category	Editorial category
Launch_Date	Publication start date
Circulation	Copies distributed
Subscription_Price	Annual subscription
Publisher_Email	Publisher contact
Region	Business region

Intentional errors include:

• Duplicate Magazine_ID
• Missing launch dates
• Negative subscription prices
• Invalid circulation values
• Mixed uppercase/lowercase
• NULL strings
• Leading/trailing spaces
• Invalid emails
• Invalid regions
• Incorrect category labels

SAS Raw Dataset

data magazine_raw;

length Magazine_Name $40 Country $20 Category $20 Publisher_Email $50 Region $15;

informat Launch_Date date9.;

format Launch_Date date9.;

infile datalines dlm=',' dsd truncover;

input Magazine_ID Magazine_Name :$40. Country :$20. Category :$20.

Launch_Date :date9. Circulation $ Subscription_Price Publisher_Email :$50.

Region :$15.;

datalines;

101,Time,USA,News,15JAN1923,4500000,120,time@magazine.com,NA

101,time,usa,news,.,4500000,-120,time.com,North

102,National Geographic,USA,Science,01JAN1888,6000000,150,natgeo@magazine.com,NA

103, Forbes ,USA,Business,15SEP1917,2500000,110,forbes@magazine.com,NA

104,The Economist,UK,Finance,01SEP1843,1800000,180,economist@email,EU

105,Vogue,France,Fashion,17DEC1892,3200000,140,vogue@magazine.com,Europe

106,Nature,UK,Science,04NOV1869,2500000,200,nature@magazine.com,EU

107,WIRED,USA,Technology,01MAR1993,1700000,130,wired@magazine.com,NA

108,Reader's Digest,Canada,Lifestyle,05FEB1922,-900000,90,reader@magazine.com,North America

109,People,USA,Entertainment,20MAR1974,3500000,95,people@magazine.com,NA

110,Scientific American,USA,SCIENCE,28AUG1845,1500000,160,sciam@magazine.com,na

111,Rolling Stone,USA,Music,09NOV1967,1400000,115,rollingstone.com,NA

112,The New Yorker,USA,Culture,21FEB1925,1250000,170,newyorker@magazine.com,NA

113,Fortune,USA,Business,15FEB1930,NULL,150,fortune@magazine.com,NA

114,Bloomberg Businessweek,USA,Business,01SEP1929,900000,175,bloomberg@magazine.com,

115,Discover,USA,Science,.,850000,100,discover@magazine.com,NA

116,The Atlantic, USA ,Politics,01NOV1857,650000,145,atlantic@magazine.com,North

117,Harvard Business Review,USA,BUSINESS,01JAN1922,500000,220,hbr@magazine,NA

118,Better Homes and Gardens,USA,Lifestyle,01JAN1924,700000,80,bhg@magazine.com,NA

119,Sports Illustrated,USA,Sports,16AUG1954,1300000,-99,si@magazine.com,NA

120,The Week,UK,News,10APR1995,750000,105,theweek@magazine.com,EU

121,NULL,India,Education,15JAN2000,450000,70,education@magazine.com,APAC

;

run;

proc print data=magazine_raw;

run;

OUTPUT:

Obs	Magazine_Name	Country	Category	Publisher_Email	Region	Launch_Date	Magazine_ID	Circulation	Subscription_Price
1	Time	USA	News	time@magazine.com	NA	15JAN1923	101	4500000	120
2	time	usa	news	time.com	North	.	101	4500000	-120
3	National Geographic	USA	Science	natgeo@magazine.com	NA	01JAN1888	102	6000000	150
4	Forbes	USA	Business	forbes@magazine.com	NA	15SEP1917	103	2500000	110
5	The Economist	UK	Finance	economist@email	EU	01SEP1843	104	1800000	180
6	Vogue	France	Fashion	vogue@magazine.com	Europe	17DEC1892	105	3200000	140
7	Nature	UK	Science	nature@magazine.com	EU	04NOV1869	106	2500000	200
8	WIRED	USA	Technology	wired@magazine.com	NA	01MAR1993	107	1700000	130
9	Reader's Digest	Canada	Lifestyle	reader@magazine.com	North America	05FEB1922	108	-900000	90
10	People	USA	Entertainment	people@magazine.com	NA	20MAR1974	109	3500000	95
11	Scientific American	USA	SCIENCE	sciam@magazine.com	na	28AUG1845	110	1500000	160
12	Rolling Stone	USA	Music	rollingstone.com	NA	09NOV1967	111	1400000	115
13	The New Yorker	USA	Culture	newyorker@magazine.com	NA	21FEB1925	112	1250000	170
14	Fortune	USA	Business	fortune@magazine.com	NA	15FEB1930	113	NULL	150
15	Bloomberg Businessweek	USA	Business	bloomberg@magazine.com		01SEP1929	114	900000	175
16	Discover	USA	Science	discover@magazine.com	NA	.	115	850000	100
17	The Atlantic	USA	Politics	atlantic@magazine.com	North	01NOV1857	116	650000	145
18	Harvard Business Review	USA	BUSINESS	hbr@magazine	NA	01JAN1922	117	500000	220
19	Better Homes and Gardens	USA	Lifestyle	bhg@magazine.com	NA	01JAN1924	118	700000	80
20	Sports Illustrated	USA	Sports	si@magazine.com	NA	16AUG1954	119	1300000	-99
21	The Week	UK	News	theweek@magazine.com	EU	10APR1995	120	750000	105
22	NULL	India	Education	education@magazine.com	APAC	15JAN2000	121	450000	70

Intentional Errors Included

Issue	Observation
Duplicate Magazine_ID	101
Mixed Case	time, usa, SCIENCE, BUSINESS
Leading/Trailing Spaces	Forbes, USA
Negative Circulation	-900000
Negative Subscription Price	-120, -99
Missing Launch_Date	101,115
NULL String	Magazine_Name, Circulation
Invalid Email	time.com, economist@email, hbr@magazine
Blank Region	Magazine_ID=114
Different Region Labels	NA, North, North America, na
Invalid Category Case	SCIENCE, BUSINESS

Explanation

SAS uses LENGTH before assignments to prevent character truncation. If placed later, SAS permanently fixes shorter lengths, potentially losing information. INFILE, INPUT, and FORMAT standardize variable reading.

Equivalent Raw Dataset in R

library(tibble)

magazine_raw <- tibble(

  Magazine_ID = c(101,101,102,103,104,105,106,107,108,109,110,

                  111,112,113,114,115,116,117,118,119,120,121),

  Magazine_Name = c("Time","time","National Geographic"," Forbes ",

    "The Economist","Vogue","Nature","WIRED","Reader's Digest",

    "People","Scientific American","Rolling Stone","The New Yorker",

    "Fortune","Bloomberg Businessweek","Discover","The Atlantic",

    "Harvard Business Review","Better Homes and Gardens",

    "Sports Illustrated","The Week","NULL"),

  Country = c("USA","usa","USA","USA","UK","France","UK","USA",

              "Canada","USA","USA","USA","USA","USA","USA",

              "USA"," USA ","USA","USA","USA","UK","India"),

  Category = c("News","news","Science","Business","Finance",

               "Fashion","Science","Technology","Lifestyle",

               "Entertainment","SCIENCE","Music","Culture",

               "Business","Business","Science","Politics",

               "BUSINESS","Lifestyle","Sports","News","Education"),

  Launch_Date = c("15JAN1923",NA,"01JAN1888","15SEP1917","01SEP1843",

                  "17DEC1892","04NOV1869","01MAR1993","05FEB1922",

                  "20MAR1974","28AUG1845","09NOV1967","21FEB1925",

                  "15FEB1930","01SEP1929",NA,"01NOV1857","01JAN1922",

                  "01JAN1924","16AUG1954","10APR1995","15JAN2000"),

  Circulation = c(4500000,4500000,6000000,2500000,1800000,3200000,

                  2500000,1700000,-900000,3500000,1500000,1400000,

                  1250000,NA,900000,850000,650000,500000,700000,1300000,

                  750000,450000),

  Subscription_Price = c(120,-120,150,110,180,140,200,130,90,95,160,115,

                         170,150,175,100,145,220,80,-99,105,70),

  Publisher_Email = c("time@magazine.com","time.com","natgeo@magazine.com",

    "forbes@magazine.com","economist@email","vogue@magazine.com","nature@magazine.com",

 "wired@magazine.com","reader@magazine.com","people@magazine.com","sciam@magazine.com",

 "rollingstone.com","newyorker@magazine.com","fortune@magazine.com","bloomberg@magazine.com",    "discover@magazine.com","atlantic@magazine.com","hbr@magazine","bhg@magazine.com",

    "si@magazine.com","theweek@magazine.com","education@magazine.com"),

  Region = c("NA","North","NA","NA","EU","Europe","EU","NA",

             "North America","NA","na","NA","NA","NA","",

             "NA","North","NA","NA","NA","EU","APAC")

)

OUTPUT:

Magazine_ID	Magazine_Name	Country	Category	Launch_Date	Circulation	Subscription_Price	Publisher_Email	Region
101	Time	USA	News	15JAN1923	4500000	120	time@magazine.com		NA
101	time	usa	news		4500000	-120	time.com		North
102	National Geographic	USA	Science	01JAN1888	6000000	150	natgeo@magazine.com		NA
103	Forbes	USA	Business	15SEP1917	2500000	110	forbes@magazine.com		NA
104	The Economist	UK	Finance	01SEP1843	1800000	180	economist@email		EU
105	Vogue	France	Fashion	17DEC1892	3200000	140	vogue@magazine.com		Europe
106	Nature	UK	Science	04NOV1869	2500000	200	nature@magazine.com		EU
107	WIRED	USA	Technology	01MAR1993	1700000	130	wired@magazine.com		NA
108	Reader's Digest	Canada	Lifestyle	05FEB1922	-900000	90	reader@magazine.com		North America
109	People	USA	Entertainment	20MAR1974	3500000	95	people@magazine.com		NA
110	Scientific American	USA	SCIENCE	28AUG1845	1500000	160	sciam@magazine.com		na
111	Rolling Stone	USA	Music	09NOV1967	1400000	115	rollingstone.com		NA
112	The New Yorker	USA	Culture	21FEB1925	1250000	170	newyorker@magazine.com		NA
113	Fortune	USA	Business	15FEB1930		150	fortune@magazine.com		NA
114	Bloomberg Businessweek	USA	Business	01SEP1929	900000	175	bloomberg@magazine.com
115	Discover	USA	Science		850000	100	discover@magazine.com		NA
116	The Atlantic	USA	Politics	01NOV1857	650000	145	atlantic@magazine.com		North
117	Harvard Business Review	USA	BUSINESS	01JAN1922	500000	220	hbr@magazine		NA
118	Better Homes and Gardens	USA	Lifestyle	01JAN1924	700000	80	bhg@magazine.com		NA
119	Sports Illustrated	USA	Sports	16AUG1954	1300000	-99	si@magazine.com		NA
120	The Week	UK	News	10APR1995	750000	105	theweek@magazine.com		EU
121	NULL	India	Education	15JAN2000	450000	70	education@magazine.com		APAC

Dataset Summary

• Observations: 22
• Variables: 9
• Business Domain: Global Publishing Industry
• Purpose: Demonstrate enterprise-grade data cleaning using SAS DATA Step, PROC SQL, and R tidyverse.
• Intentional Data Quality Problems: Duplicate IDs, missing dates, negative numeric values, mixed text case, whitespace corruption, invalid emails, inconsistent regions, NULL strings, missing numeric values, and inconsistent category labels.

Explanation

R stores character vectors dynamically and does not truncate strings like SAS. Packages such as dplyr, stringr, and lubridate simplify cleaning while maintaining readable pipelines.

3. SAS Enterprise Cleaning Workflow

A typical production workflow combines DATA Step programming with PROC SQL.

data magazine_clean;

set magazine_raw;

Magazine_Name=propcase(strip(Magazine_Name));

Country=upcase(strip(Country));

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

if missing(Category) then Category="General";

Publisher_Email=tranwrd(Publisher_Email," ","");

if Circulation="NULL" then Circ_Num=.;

else Circ_Num=input(Circulation,best12.);

drop Circulation;

rename Circ_Num=Circulation;

run;

proc print data=magazine_clean;

run;

OUTPUT:

Obs	Magazine_Name	Country	Category	Publisher_Email	Region	Launch_Date	Magazine_ID	Subscription_Price	Circulation
1	Time	USA	News	time@magazine.com	NA	15JAN1923	101	120	4500000
2	Time	USA	news	time.com	North	.	101	120	4500000
3	National Geographic	USA	Science	natgeo@magazine.com	NA	01JAN1888	102	150	6000000
4	Forbes	USA	Business	forbes@magazine.com	NA	15SEP1917	103	110	2500000
5	The Economist	UK	Finance	economist@email	EU	01SEP1843	104	180	1800000
6	Vogue	FRANCE	Fashion	vogue@magazine.com	Europe	17DEC1892	105	140	3200000
7	Nature	UK	Science	nature@magazine.com	EU	04NOV1869	106	200	2500000
8	Wired	USA	Technology	wired@magazine.com	NA	01MAR1993	107	130	1700000
9	Reader's Digest	CANADA	Lifestyle	reader@magazine.com	North America	05FEB1922	108	90	-900000
10	People	USA	Entertainment	people@magazine.com	NA	20MAR1974	109	95	3500000
11	Scientific American	USA	SCIENCE	sciam@magazine.com	na	28AUG1845	110	160	1500000
12	Rolling Stone	USA	Music	rollingstone.com	NA	09NOV1967	111	115	1400000
13	The New Yorker	USA	Culture	newyorker@magazine.com	NA	21FEB1925	112	170	1250000
14	Fortune	USA	Business	fortune@magazine.com	NA	15FEB1930	113	150	.
15	Bloomberg Businessweek	USA	Business	bloomberg@magazine.com		01SEP1929	114	175	900000
16	Discover	USA	Science	discover@magazine.com	NA	.	115	100	850000
17	The Atlantic	USA	Politics	atlantic@magazine.com	North	01NOV1857	116	145	650000
18	Harvard Business Review	USA	BUSINESS	hbr@magazine	NA	01JAN1922	117	220	500000
19	Better Homes And Gardens	USA	Lifestyle	bhg@magazine.com	NA	01JAN1924	118	80	700000
20	Sports Illustrated	USA	Sports	si@magazine.com	NA	16AUG1954	119	99	1300000
21	The Week	UK	News	theweek@magazine.com	EU	10APR1995	120	105	750000
22	Null	INDIA	Education	education@magazine.com	APAC	15JAN2000	121	70	450000

Additional techniques Can Also include:

• IF-THEN/ELSE
• SELECT-WHEN
• DO loops
• ARRAYS
• RETAIN
• FIRST./LAST.
• COMPRESS
• CATX
• COALESCEC
• VERIFY
• FIND
• INDEX
• INPUT/PUT conversions
• INTNX
• INTCK

PROC FORMAT standardizes business categories, PROC SORT NODUPKEY removes duplicates, PROC REPORT generates executive reports, PROC TRANSPOSE reshapes data, while reusable SAS macros automate repetitive validation.

PROC SQL Example

proc sql;

create table summary as

select Country,

count(*) as Total_Magazines,

avg(Subscription_Price) as AvgPrice

from magazine_clean

group by Country;

quit;

proc print data=summary;

run;

OUTPUT:

Obs	Country	Total_Magazines	AvgPrice
1	CANADA	1	90.000
2	FRANCE	1	140.000
3	INDIA	1	70.000
4	UK	3	161.667
5	USA	16	133.688

Explanation

PROC SQL simplifies summarization and joins without requiring multiple DATA steps. DATA Step is generally faster for row-wise transformations, while PROC SQL excels in relational operations.

4. Modern R Cleaning Workflow

Equivalent R pipeline:

library(dplyr)

library(stringr)

magazine_clean <-

  magazine_raw %>%

  mutate(

    Magazine_Name = str_trim(str_to_title(Magazine_Name)),

    Country = str_to_upper(str_trim(Country)),

    Category = str_to_title(str_trim(Category)),

    Subscription_Price = abs(Subscription_Price),

    Publisher_Email = str_remove_all(Publisher_Email, " ")

  )

OUTPUT:

Magazine_ID	Magazine_Name	Country	Category	Launch_Date	Circulation	Subscription_Price	Publisher_Email	Region
101	Time	USA	News	15JAN1923	4500000	120	time@magazine.com	NA
101	Time	USA	News		4500000	120	time.com	North
102	National Geographic	USA	Science	01JAN1888	6000000	150	natgeo@magazine.com	NA
103	Forbes	USA	Business	15SEP1917	2500000	110	forbes@magazine.com	NA
104	The Economist	UK	Finance	01SEP1843	1800000	180	economist@email	EU
105	Vogue	FRANCE	Fashion	17DEC1892	3200000	140	vogue@magazine.com	Europe
106	Nature	UK	Science	04NOV1869	2500000	200	nature@magazine.com	EU
107	Wired	USA	Technology	01MAR1993	1700000	130	wired@magazine.com	NA
108	Reader's Digest	CANADA	Lifestyle	05FEB1922	-900000	90	reader@magazine.com	North America
109	People	USA	Entertainment	20MAR1974	3500000	95	people@magazine.com	NA
110	Scientific American	USA	Science	28AUG1845	1500000	160	sciam@magazine.com	na
111	Rolling Stone	USA	Music	09NOV1967	1400000	115	rollingstone.com	NA
112	The New Yorker	USA	Culture	21FEB1925	1250000	170	newyorker@magazine.com	NA
113	Fortune	USA	Business	15FEB1930		150	fortune@magazine.com	NA
114	Bloomberg Businessweek	USA	Business	01SEP1929	900000	175	bloomberg@magazine.com
115	Discover	USA	Science		850000	100	discover@magazine.com	NA
116	The Atlantic	USA	Politics	01NOV1857	650000	145	atlantic@magazine.com	North
117	Harvard Business Review	USA	Business	01JAN1922	500000	220	hbr@magazine	NA
118	Better Homes And Gardens	USA	Lifestyle	01JAN1924	700000	80	bhg@magazine.com	NA
119	Sports Illustrated	USA	Sports	16AUG1954	1300000	99	si@magazine.com	NA
120	The Week	UK	News	10APR1995	750000	105	theweek@magazine.com	EU
121	Null	INDIA	Education	15JAN2000	450000	70	education@magazine.com	APAC

R projects commonly use:

• mutate()
• case_when()
• across()
• replace_na()
• filter()
• arrange()
• summarise()
• distinct()
• separate()
• unite()
• if_else()
• str_replace_all()
• grepl()
• parse_date_time()
• coalesce()

Compared with SAS DATA Step, R emphasizes chained transformations, while SAS provides stronger production auditability and controlled metadata management.

5.Validation & Compliance

Clinical programming requires every transformation to be traceable. Regulatory agencies expect reproducible workflows supported by audit trails and independent Quality Control (QC).

Key enterprise considerations:

• SDTM datasets require standardized variables.
• ADaM datasets depend on validated derivations.
• Independent QC confirms programming accuracy.
• Metadata documents variable definitions.
• Audit trails record every transformation.
• Missing numeric values in SAS are considered smaller than valid numbers, affecting sorting and statistical summaries if handled incorrectly.

Ignoring these principles may produce incorrect treatment populations, inaccurate efficacy analyses, or misleading safety reports.

6.Business Logic

Business rules transform operational records into analytical intelligence. Missing values may be imputed using predefined standards, unrealistic values corrected through validation rules, and text variables normalized to improve consistency. For example, a patient age of 250 years is impossible and should be flagged rather than analyzed. Likewise, a magazine subscription price of −120 should become 120 after validation. Country values such as "usa", "USA ", and "Usa" should all become "USA" to prevent duplicate categories. Missing publication dates may be imputed using approved business rules, while malformed emails should be corrected or excluded from communication workflows. These transformations improve dashboard accuracy, statistical reliability, AI model performance, and executive reporting.

7.Twenty Enterprise Best Practices

1. Validate source systems.
2. Define metadata first.
3. Standardize naming conventions.
4. Remove duplicates early.
5. Normalize text.
6. Validate dates.
7. Check impossible values.
8. Preserve audit trails.
9. Use reusable macros.
10. Separate raw and clean datasets.
11. Never overwrite source data.
12. Perform independent QC.
13. Standardize missing values.
14. Document transformations.
15. Automate validation.
16. Test production code.
17. Review logs daily.
18. Maintain data lineage.
19. Version control programs.
20. Deploy only validated datasets.

8.Twenty One-Line Insights

• Dirty data creates expensive business mistakes.
• Clean inputs produce reliable outputs.
• Validation exceeds visual inspection.
• Metadata improves governance.
• Standardization increases reproducibility.
• Audit trails build trust.
• Macros reduce repetitive coding.
• PROC SQL simplifies joins.
• DATA Step excels at row processing.
• R encourages readable pipelines.
• Missing values require careful handling.
• Dates drive analytics.
• Consistency improves dashboards.
• Automation reduces manual errors.
• Defensive programming saves time.
• Duplicate records distort statistics.
• QC protects regulatory submissions.
• Documentation accelerates maintenance.
• Reliable data supports AI.
• Quality data enables confident decisions.

9.SAS vs R Comparison

Feature	SAS	R
Auditability	Excellent	Good
Regulatory Acceptance	Excellent	Growing
Flexibility	High	Very High
Scalability	Excellent	High
Visualization	Good	Excellent
Open Source	No	Yes
Clinical Standards	Industry Leader	Supportive

Validation Checklist

• Duplicate IDs removed
• Missing values reviewed
• Dates standardized
• Text normalized
• Numeric ranges validated
• Categories standardized
• Emails verified
• QC completed
• Metadata updated
• Reports validated

10. Summary, Conclusion & Interview Questions

Summary

SAS and R complement each other remarkably well. SAS remains the preferred platform for regulated enterprise environments because of its strong auditability, metadata management, reproducibility, and production stability. DATA Step programming efficiently handles row-wise transformations, while PROC SQL simplifies joins and summarizations. R excels in exploratory analysis, visualization, and flexible data manipulation using the tidyverse ecosystem. Together, these technologies transform inconsistent operational data into reliable analytical intelligence that supports business reporting, AI models, and regulatory submissions. Successful data engineering is less about writing complex code and more about designing repeatable, validated workflows that consistently deliver trustworthy information.

Conclusion

Data cleaning is one of the most valuable activities in modern analytics. Whether working with clinical trial records, banking transactions, insurance claims, retail sales, or publishing data such as the Best Magazines in the World dataset, poor-quality data directly affects dashboards, machine learning models, executive decisions, and regulatory compliance. Enterprise programmers should never treat cleaning as an afterthought. Instead, they should build structured pipelines that validate source data, standardize formats, remove duplicates, document transformations, and verify outputs through independent QC. SAS provides exceptional governance, traceability, and production reliability, while R offers unmatched flexibility and modern analytical capabilities. Organizations that combine both technologies establish scalable, reproducible, and trustworthy analytics ecosystems capable of supporting strategic business intelligence and high-quality decision-making.

Interview Questions

1. How would you remove duplicate magazine records?
Use PROC SORT NODUPKEY or distinct() in R while validating business keys.

2. Why should negative subscription prices be corrected?
Negative values distort financial summaries and downstream analytical models.

3. DATA Step or PROC SQL which would you choose?
DATA Step for sequential row processing; PROC SQL for joins, aggregation, and relational queries.

4. Why are missing numeric values dangerous in SAS?
They sort lower than valid numbers and can influence comparisons, filtering, and statistical analyses if not handled deliberately.

5. Why use both SAS and R in enterprise analytics?
SAS provides validated, audit-ready production workflows, while R delivers flexible data wrangling, advanced visualization, and rapid analytical development, creating a robust end-to-end data engineering solution.

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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.

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The datasets and analysis in this article are created for educational and demonstration purposes only. Here we learn about MAGAZINE DATA.

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