282.100+ HISTORIC BUSINESSES WORLDWIDE DATASET | PROC CONTENTS | PROC PRINT | PROC FREQ | PROC MEANS | PROC SORT | PROC REPORT| PROC SQL |MACRO SHOW_TOP_INDUSTRIES | MACRO COUNTRY_REPORT

100+ HISTORIC BUSINESSES WORLDWIDE DATASET | PROC CONTENTS | PROC PRINT | PROC FREQ | PROC MEANS | PROC SORT | PROC REPORT| PROC SQL |MACRO SHOW_TOP_INDUSTRIES | MACRO COUNTRY_REPORT

 /*CREATING A DATASET OF 100+ HISTORIC BUSINESSES */

1.Macro lists for generating plausible names, countries, industries and founders 

%let prefixes = Abernathy Burling Cortes Hanover Silkwood Tudor Imperial Royal Eastern

                Atlantic Pioneer Union Continental Heritage OldTown Windsor Blackwell 

                Greenfield Lancaster Brighton Franklin Sterling Mayfair Cambridge;

%let suffixes = %str(Trading Co) %str(Mercantile) %str(& Co.) %str(Foundry Works) 

                %str(Emporium) %str(& Sons) %str(& Partners) %str(Consortium) 

                %str(Guild) %str(Company) %str(& Company) %str(Manufactories) 

                %str(Shipping House) %str(Warehouses) %str(Baker & Co.) %str(Brewer & Co.);

%let countries = United_Kingdom United_States France Germany Italy Spain India China Japan

                 Netherlands Portugal Sweden Norway Denmark Belgium Switzerland Austria 

                 Turkey Russia Brazil Mexico Australia Canada South_Africa Egypt Morocco 

                 Saudi_Arabia UAE Singapore Hong_Kong Indonesia Malaysia Vietnam Thailand;

%let industries = Textiles Shipping Brewing Banking Metalworking Ceramics Agriculture

                  SpiceTrading Printing Publishing Glassmaking Carpentry Shipbuilding 

                  SilkTrade TeaTrade CoffeeTrade Insurance Retailing Mining LeatherTanning 

                  Jewelry Watchmaking Porcelain;

%let founders = Thomas_Abbott William_Burling Jose_Cortes Hans_Hanover Li_Silkwood Raj_Tudor

                Alessandro_Imperial Maria_Royal Omar_Eastern John_Atlantic Pierre_Pioneer 

                Ana_Union Karl_Continental Isabel_Heritage Suresh_OldTown Henry_Windsor 

                Paul_Blackwell Grace_Greenfield Marcus_Lancaster Sophia_Brighton 

                Noah_Franklin Emily_Sterling;

2.Count words in macro lists 

%let npref = %sysfunc(countw(&prefixes));

%let nsuf  = %sysfunc(countw(&suffixes));

%let nctry = %sysfunc(countw(&countries));

%let nind  = %sysfunc(countw(&industries));

%let nfound= %sysfunc(countw(&founders));

options nocenter;

data old_businesses;

  length Business_ID 8 Name $60 Country $30 Founded_Year 8 

         Industry $30 Founder $30 Status $8;

  call streaminit(12345);

  do Business_ID = 1 to 110;

    /* Pick prefix, suffix, country, industry, founder deterministically for coverage */

    pref = scan("&prefixes", mod(Business_ID-1,&npref)+1, ' ');

    suf  = scan("&suffixes", mod(Business_ID-1,&nsuf)+1, ' ');

    Name = catx(' ', pref, suf);

    Country = tranwrd(scan("&countries", mod(Business_ID-1,&nctry)+1, ' '), '_', ' ');

    Industry = scan("&industries", mod(Business_ID-1,&nind)+1, ' ');

    Founder  = tranwrd(scan("&founders", mod(Business_ID-1,&nfound)+1, ' '), '_', ' ');

    /* Older businesses: random year between 1600 and 1950, with 10% modern */

    if rand('uniform') < 0.10 then 

      Founded_Year = 1951 + floor(rand('uniform')*70); 

    else 

      Founded_Year = 1600 + floor(rand('uniform')*351);

    /* Status: many old firms defunct, some still active */

    if Founded_Year < 1850 then do;

      Status = ifc(rand('uniform')<0.5,'Defunct','Active');

    end;

    else do;

      Status = ifc(rand('uniform')<0.7,'Active','Defunct');

    end;

    output;

  end;

  drop pref suf;

run;

proc print data=old_businesses(obs=20);

  title "Sample of Historic Businesses";

run;

Output:

Sample of Historic Businesses

Obs	Business_ID	Name	Country	Founded_Year	Industry	Founder	Status
1	1	Abernathy Trading	United Kingdom	1948	Textiles	Thomas Abbott	Active
2	2	Burling Co	United States	1889	Shipping	William Burling	Active
3	3	Cortes Mercantile	France	1734	Brewing	Jose Cortes	Defunct
4	4	Hanover &	Germany	1781	Banking	Hans Hanover	Active
5	5	Silkwood Co.	Italy	1927	Metalworking	Li Silkwood	Active
6	6	Tudor Foundry	Spain	1635	Ceramics	Raj Tudor	Active
7	7	Imperial Works	India	1895	Agriculture	Alessandro Imperial	Defunct
8	8	Royal Emporium	China	1632	SpiceTrading	Maria Royal	Active
9	9	Eastern &	Japan	1845	Printing	Omar Eastern	Active
10	10	Atlantic Sons	Netherlands	1930	Publishing	John Atlantic	Active
11	11	Pioneer &	Portugal	1863	Glassmaking	Pierre Pioneer	Defunct
12	12	Union Partners	Sweden	1767	Carpentry	Ana Union	Active
13	13	Continental Consortium	Norway	1895	Shipbuilding	Karl Continental	Defunct
14	14	Heritage Guild	Denmark	1885	SilkTrade	Isabel Heritage	Active
15	15	OldTown Company	Belgium	1738	TeaTrade	Suresh OldTown	Active
16	16	Windsor &	Switzerland	1850	CoffeeTrade	Henry Windsor	Active
17	17	Blackwell Company	Austria	1724	Insurance	Paul Blackwell	Defunct
18	18	Greenfield Manufactories	Turkey	1798	Retailing	Grace Greenfield	Defunct
19	19	Lancaster Shipping	Russia	1989	Mining	Marcus Lancaster	Defunct
20	20	Brighton House	Brazil	1952	LeatherTanning	Sophia Brighton	Defunct

3.PROC CONTENTS - 

purpose: show dataset structure and variable attributes in one listing 

proc contents data=old_businesses position;

run; 

Output:

The CONTENTS Procedure

Data Set Name	WORK.OLD_BUSINESSES	Observations	110
Member Type	DATA	Variables	7
Engine	V9	Indexes	0
Created	09/07/2025 18:22:48	Observation Length	176
Last Modified	09/07/2025 18:22:48	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	SOLARIS_X86_64, LINUX_X86_64, ALPHA_TRU64, LINUX_IA64
Encoding	utf-8 Unicode (UTF-8)

Engine/Host Dependent Information
Data Set Page Size	131072
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	743
Obs in First Data Page	110
Number of Data Set Repairs	0
Filename	/saswork/SAS_work662C0001A968_odaws02-apse1-2.oda.sas.com/SAS_work88120001A968_odaws02-apse1-2.oda.sas.com/old_businesses.sas7bdat
Release Created	9.0401M8
Host Created	Linux
Inode Number	1186018
Access Permission	rw-r--r--
Owner Name	u63247146
File Size	256KB
File Size (bytes)	262144

Alphabetic List of Variables and Attributes
#	Variable	Type	Len
1	Business_ID	Num	8
3	Country	Char	30
4	Founded_Year	Num	8
6	Founder	Char	30
5	Industry	Char	30
2	Name	Char	60
7	Status	Char	8

Variables in Creation Order
#	Variable	Type	Len
1	Business_ID	Num	8
2	Name	Char	60
3	Country	Char	30
4	Founded_Year	Num	8
5	Industry	Char	30
6	Founder	Char	30
7	Status	Char	8

4.PROC PRINT - 

purpose: print first 15 observations for a quick visual check 

proc print data=old_businesses(obs=15) label;

  title 'First 15 Historic Businesses - Quick Look';

run;  

Output:

First 15 Historic Businesses - Quick Look

Obs	Business_ID	Name	Country	Founded_Year	Industry	Founder	Status
1	1	Abernathy Trading	United Kingdom	1948	Textiles	Thomas Abbott	Active
2	2	Burling Co	United States	1889	Shipping	William Burling	Active
3	3	Cortes Mercantile	France	1734	Brewing	Jose Cortes	Defunct
4	4	Hanover &	Germany	1781	Banking	Hans Hanover	Active
5	5	Silkwood Co.	Italy	1927	Metalworking	Li Silkwood	Active
6	6	Tudor Foundry	Spain	1635	Ceramics	Raj Tudor	Active
7	7	Imperial Works	India	1895	Agriculture	Alessandro Imperial	Defunct
8	8	Royal Emporium	China	1632	SpiceTrading	Maria Royal	Active
9	9	Eastern &	Japan	1845	Printing	Omar Eastern	Active
10	10	Atlantic Sons	Netherlands	1930	Publishing	John Atlantic	Active
11	11	Pioneer &	Portugal	1863	Glassmaking	Pierre Pioneer	Defunct
12	12	Union Partners	Sweden	1767	Carpentry	Ana Union	Active
13	13	Continental Consortium	Norway	1895	Shipbuilding	Karl Continental	Defunct
14	14	Heritage Guild	Denmark	1885	SilkTrade	Isabel Heritage	Active
15	15	OldTown Company	Belgium	1738	TeaTrade	Suresh OldTown	Active

5.PROC FREQ - 

purpose: frequency distribution for categorical variables 

proc freq data=old_businesses;

  tables Country Industry Status / nocum nopercent;

run; 

Output:

The FREQ Procedure

Country	Frequency
Australia	3
Austria	3
Belgium	3
Brazil	3
Canada	3
China	4
Denmark	3
Egypt	3
France	4
Germany	4
Hong Kong	3
India	4
Indonesia	3
Italy	4
Japan	3
Malaysia	3
Mexico	3
Morocco	3
Netherlands	3
Norway	3
Portugal	3
Russia	3
Saudi Arabia	3
Singapore	3
South Africa	3
Spain	4
Sweden	3
Switzerland	3
Thailand	3
Turkey	3
UAE	3
United Kingdom	4
United States	4
Vietnam	3

Industry	Frequency
Agriculture	5
Banking	5
Brewing	5
Carpentry	5
Ceramics	5
CoffeeTrade	5
Glassmaking	5
Insurance	5
Jewelry	4
LeatherTanning	4
Metalworking	5
Mining	4
Porcelain	4
Printing	5
Publishing	5
Retailing	5
Shipbuilding	5
Shipping	5
SilkTrade	5
SpiceTrading	5
TeaTrade	5
Textiles	5
Watchmaking	4

Status	Frequency
Active	68
Defunct	42

6.PROC MEANS - 

purpose: basic numeric summary of Founded_Year 

proc means data=old_businesses min mean median max std n;

  var Founded_Year;

run; 

Output:

The MEANS Procedure

Analysis Variable : Founded_Year
Minimum	Mean	Median	Maximum	Std Dev	N
1601.00	1809.61	1824.00	2012.00	105.9860728	110

7.PROC SORT + PROC REPORT - 

purpose: sorted grouped report showing counts by country and earliest founded year 

proc sort data=old_businesses out=obs_sorted; by Country Founded_Year; 

run;

proc print data=obs_sorted(obs=20);

run;

Output:

Obs	Business_ID	Name	Country	Founded_Year	Industry	Founder	Status
1	90	Greenfield Foundry	Australia	1668	Jewelry	William Burling	Active
2	22	Sterling Trading	Australia	1840	Watchmaking	Emily Sterling	Defunct
3	56	Royal Guild	Australia	1887	Publishing	Ana Union	Defunct
4	17	Blackwell Company	Austria	1724	Insurance	Paul Blackwell	Defunct
5	85	Continental Trading	Austria	1772	CoffeeTrade	Marcus Lancaster	Active
6	51	Cortes &	Austria	1887	Metalworking	Alessandro Imperial	Active
7	15	OldTown Company	Belgium	1738	TeaTrade	Suresh OldTown	Active
8	49	Abernathy Works	Belgium	1757	Brewing	Li Silkwood	Defunct
9	83	Pioneer House	Belgium	1803	SilkTrade	Paul Blackwell	Defunct
10	88	Windsor &	Brazil	1635	Mining	Emily Sterling	Active
11	54	Tudor Partners	Brazil	1862	SpiceTrading	John Atlantic	Defunct
12	20	Brighton House	Brazil	1952	LeatherTanning	Sophia Brighton	Defunct
13	91	Lancaster Works	Canada	1674	Watchmaking	Jose Cortes	Active
14	23	Mayfair Co	Canada	1680	Porcelain	Thomas Abbott	Defunct
15	57	Eastern Company	Canada	1864	Glassmaking	Karl Continental	Active
16	8	Royal Emporium	China	1632	SpiceTrading	Maria Royal	Active
17	110	Heritage Co.	China	1739	Retailing	Emily Sterling	Active
18	42	Greenfield Warehouses	China	1881	Mining	Sophia Brighton	Active
19	76	Hanover Consortium	China	1936	Agriculture	John Atlantic	Active
20	14	Heritage Guild	Denmark	1885	SilkTrade	Isabel Heritage	Active

proc report data=obs_sorted nowd;

  column Country n Founded_Year=MinYear Founded_Year=MaxYear;

  define Country / group;

  define n / 'Count';

  define MinYear / min 'Earliest Founded Year';

  define MaxYear / max 'Latest Founded Year';

run;

Output:

Country	Count	Earliest Founded Year	Latest Founded Year
Australia	3	1668	1887
Austria	3	1724	1887
Belgium	3	1738	1803
Brazil	3	1635	1952
Canada	3	1674	1864
China	4	1632	1936
Denmark	3	1885	1953
Egypt	3	1656	1983
France	4	1734	1951
Germany	4	1749	1962
Hong Kong	3	1601	1886
India	4	1787	1895
Indonesia	3	1659	1937
Italy	4	1768	1927
Japan	3	1642	1920
Malaysia	3	1703	1898
Mexico	3	1672	1844
Morocco	3	1701	1837
Netherlands	3	1700	1930
Norway	3	1726	1895
Portugal	3	1722	1863
Russia	3	1663	1989
Saudi Arabia	3	1639	1699
Singapore	3	1805	2012
South Africa	3	1841	1889
Spain	4	1621	1707
Sweden	3	1767	1849
Switzerland	3	1811	1850
Thailand	3	1900	1901
Turkey	3	1694	1798
UAE	3	1714	1940
United Kingdom	4	1745	1979
United States	4	1660	1956
Vietnam	3	1692	1983

8.PROC SQL - 

purpose: create summary tables and an example join / aggregation 

proc sql;

  create table industry_summary as

  select Industry,

         count(*) as N_Businesses,

         min(Founded_Year) as Earliest format=4.,

         max(Founded_Year) as Latest format=4.

  from old_businesses

  group by Industry

  order by N_Businesses desc;

quit; 

proc print data=industry_summary(obs=20);run;

Output:

Obs	Industry	N_Businesses	Earliest	Latest
1	CoffeeTrade	5	1621	1876
2	SpiceTrading	5	1632	1920
3	TeaTrade	5	1699	1863
4	Printing	5	1692	1898
5	Shipping	5	1770	1953
6	Ceramics	5	1635	1938
7	Textiles	5	1740	1983
8	Metalworking	5	1699	2012
9	Brewing	5	1639	1950
10	Glassmaking	5	1745	1900
11	Publishing	5	1722	1983
12	Shipbuilding	5	1656	1951
13	Carpentry	5	1726	1896
14	SilkTrade	5	1701	1962
15	Agriculture	5	1676	1936
16	Banking	5	1650	1940
17	Insurance	5	1694	1805
18	Retailing	5	1601	1850
19	Porcelain	4	1680	1979
20	Mining	4	1635	1989

9.Macro to print top N industries with a custom header 

%macro show_top_industries(n=5);

  %put NOTE: Showing top &n industries by business count;

  proc print data=industry_summary(obs=&n) noobs; title "Top &n Industries"; run;

%mend show_top_industries;

%show_top_industries(n=8) 

Output:

Top 8 Industries

Industry	N_Businesses	Earliest	Latest
CoffeeTrade	5	1621	1876
SpiceTrading	5	1632	1920
TeaTrade	5	1699	1863
Printing	5	1692	1898
Shipping	5	1770	1953
Ceramics	5	1635	1938
Textiles	5	1740	1983
Metalworking	5	1699	2012

10.Macro to create a country-specific dataset and basic stats 

%macro country_report(country=United States);

  %let cname = %sysfunc(tranwrd(&country, %str( ), _));

  proc sql;

    create table &cname._biz as

    select * from old_businesses

    where Country = "%sysfunc(tranwrd(&country,_ , ))";

  quit;

  proc means data=&cname._biz min mean median max n;

    var Founded_Year;

    title "Summary for &country";

  run;

%mend country_report;

%country_report(country=United States) 

Output:

Summary for United States

The MEANS Procedure

Analysis Variable : Founded_Year
Minimum	Mean	Median	Maximum	N
1660.00	1810.00	1812.00	1956.00	4

11.Validation / QC: check for duplicates on Name + Founded_Year 

proc sort data=old_businesses nodupkey dupout=dups; 

 by Name Founded_Year;

run; 

proc print data=dups; 

 title 'Duplicate Name-Founded_Year combinations (if any)';

run; 

Log:

NOTE: 0 observations with duplicate key values were deleted.

NOTE: No observations in data set WORK.DUPS.

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