264.TEMPLES OF INDIA DATASET ANALYSIS USING PROC PRINT | PROC CONTENTS | PROC SORT | PROC FREQ | PROC MEANS | PROC SQL | PROC TABULATE | PROC REPORT | PROC TRANSPOSE | PROC FORMAT | PROC RANK | PROC SGPLOT | MACROS

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229.HOLLYWOOD MUSIC DIRECTORS ANALYSIS USING PROC FORMAT | PROC PRINT | PROC CONTENTS | PROC SORT | PROC FREQ | PROC MEANS | PROC SQL | PROC REPORT | PROC SGPLOT IN SAS

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HOLLYWOOD MUSIC DIRECTORS ANALYSIS USING PROC FORMAT | PROC PRINT | PROC CONTENTS | PROC SORT | PROC FREQ | PROC MEANS | PROC SQL | PROC REPORT | PROC SGPLOT IN SAS

/*Creating a dataset Hollywood  Music  Directors */

1 . Session‑wide OPTIONS

options nocenter             /* left‑aligned output for blog screenshots  */

        nodate               /* remove date stamp from results window     */

        fullstimer           /* CPU/real‑time stats for performance geeks */

        yearcutoff=1920      /* allow birth or debut years <1920 */

        errors=3             /* stop after 3 data errors   */

        dkricond=error       /* force SQL UPDATE or MODIFY integrity */

        validvarname=upcase; /* ensure column names are analytic‑ready    */


2 .Custom FORMAT Table

proc format;

   value oscfmt

      0        = 'NONE'

      1        = '1 WIN'

      2 - 3    = 'MULTI‑WINNER'

      4 - high = 'LEGEND';

run;


3 .DATA Step – Building the Core Dataset

data work.musicdirs;

   length DirID 8 Name $40 Country $20 PrimaryGenre $15 OscarWins 8 NotableWork $60

          DebutYear 8 ActiveYrs 8 AvgIMDb 4.1 TwitterM 8;


   infile datalines4 dlm='|' dsd;

   input DirID Name Country PrimaryGenre OscarWins NotableWork :$60. DebutYear ActiveYrs

         AvgIMDb TwitterM;


   /* Derived columns */

   CareerStage = catt(put(intck('YEAR', mdy(1,1,DebutYear), today()), 2.), ' Yrs');

   Name_UP     = upcase(Name);

   EraGroup    = cats(floor(DebutYear/10)*10, 's');

   WinLabel    = put(OscarWins, oscfmt.);

   USUBJID     = cats('MD-', put(DirID, z3.));


   label  CareerStage = "Total Career Length (yrs)"

          Name_UP     = "Upper-Case Name"

          EraGroup    = "Decade of Debut"

          WinLabel    = "Oscar Prestige Bucket";

datalines4;

001|Hans Zimmer|Germany|Epic|2|Inception|1984|40|8.9|1.3

002|John Williams|USA|Orchestral|5|Star Wars|1958|66|8.8|0.4

003|A.R. Rahman|India|World Fusion|2|Slumdog Millionaire|1992|33|8.1|27

004|Alan Menken|USA|Disney Musicals|2|The Little Mermaid|1979|45|8.0|0.15

005|Danny Elfman|USA|Gothic Pop|0|Batman|1980|44|7.7|0.6

006|Ennio Morricone|Italy|Spaghetti Western|1|The Good, the Bad…|1961|59|8.5|0.02

007|Howard Shore|Canada|Symphonic|3|The Lord of the Rings|1979|45|8.9|0.12

008|Thomas Newman|USA|Ambient|0|American Beauty|1984|40|8.3|0.08

009|Michael Giacchino|USA|Eclectic|1|Up|1994|31|8.4|0.5

010|James Horner|USA|Romantic|2|Titanic|1979|37|8.9|0.07

011|Alexandre Desplat|France|Lyric Modern|2|The Shape of Water|1986|39|8.1|0.09

012|Hildur Guðnadóttir|Iceland|Atmospheric|1|Joker|2006|19|8.2|0.4

013|Lorne Balfe|Scotland|Hybrid|0|Mission Impossible: Fallout|2002|23|7.6|0.23

014|Pinar Toprak|Turkey|Action-SciFi|0|Captain Marvel|2004|21|7.2|0.17

015|Justin Hurwitz|USA|Jazz-Infused|2|La La Land|2009|16|8.3|0.25

016|Ramin Djawadi|Germany|Epic TV|0|Game of Thrones|2004|21|9.3|0.85

017|Nicholas Britell|USA|Neo-Classical|0|Succession|2012|13|8.8|0.18

018|Rachel Portman|UK|Rom-Drama|1|Emma|1986|39|7.9|0.05

019|Gustavo Santaolalla|Argentina|Latin Fusion|2|Brokeback Mountain|1978|47|7.8|0.1

020|Mark Mothersbaugh|USA|Alt-Rock|0|The Royal Tenenbaums|1981|44|7.5|0.22

;;;;

run;

proc print;run;

Ouput:

Obs DIRID NAME COUNTRY PRIMARYGENRE OSCARWINS NOTABLEWORK DEBUTYEAR ACTIVEYRS AVGIMDB TWITTERM CAREERSTAGE NAME_UP ERAGROUP WINLABEL USUBJID
1 1 Hans Zimmer Germany Epic 2 Inception 1984 40 8.89999 1.30 31 Yrs HANS ZIMMER 1980s MULTI-WINNER MD-001
2 2 John Williams USA Orchestral 5 Star Wars 1958 66 8.80000 0.40 57 Yrs JOHN WILLIAMS 1950s LEGEND MD-002
3 3 A.R. Rahman India World Fusion 2 Slumdog Millionaire 1992 33 8.10000 27.00 23 Yrs A.R. RAHMAN 1990s MULTI-WINNER MD-003
4 4 Alan Menken USA Disney Musicals 2 The Little Mermaid 1979 45 8.00000 0.15 36 Yrs ALAN MENKEN 1970s MULTI-WINNER MD-004
5 5 Danny Elfman USA Gothic Pop 0 Batman 1980 44 7.70000 0.60 35 Yrs DANNY ELFMAN 1980s NONE MD-005
6 6 Ennio Morricone Italy Spaghetti Weste 1 The Good, the Bad… 1961 59 8.50000 0.02 54 Yrs ENNIO MORRICONE 1960s 1 WIN MD-006
7 7 Howard Shore Canada Symphonic 3 The Lord of the Rings 1979 45 8.89999 0.12 36 Yrs HOWARD SHORE 1970s MULTI-WINNER MD-007
8 8 Thomas Newman USA Ambient 0 American Beauty 1984 40 8.30000 0.08 31 Yrs THOMAS NEWMAN 1980s NONE MD-008
9 9 Michael Giacchino USA Eclectic 1 Up 1994 31 8.39999 0.50 21 Yrs MICHAEL GIACCHINO 1990s 1 WIN MD-009
10 10 James Horner USA Romantic 2 Titanic 1979 37 8.89999 0.07 36 Yrs JAMES HORNER 1970s MULTI-WINNER MD-010
11 11 Alexandre Desplat France Lyric Modern 2 The Shape of Water 1986 39 8.10000 0.09 29 Yrs ALEXANDRE DESPLAT 1980s MULTI-WINNER MD-011
12 12 Hildur Guðnadóttir Iceland Atmospheric 1 Joker 2006 19 8.20000 0.40 9 Yrs HILDUR GUÐNADÓTTIR 2000s 1 WIN MD-012
13 13 Lorne Balfe Scotland Hybrid 0 Mission Impossible: Fallout 2002 23 7.60000 0.23 13 Yrs LORNE BALFE 2000s NONE MD-013
14 14 Pinar Toprak Turkey Action-SciFi 0 Captain Marvel 2004 21 7.20000 0.17 11 Yrs PINAR TOPRAK 2000s NONE MD-014
15 15 Justin Hurwitz USA Jazz-Infused 2 La La Land 2009 16 8.30000 0.25 6 Yrs JUSTIN HURWITZ 2000s MULTI-WINNER MD-015
16 16 Ramin Djawadi Germany Epic TV 0 Game of Thrones 2004 21 9.30000 0.85 11 Yrs RAMIN DJAWADI 2000s NONE MD-016
17 17 Nicholas Britell USA Neo-Classical 0 Succession 2012 13 8.80000 0.18 3 Yrs NICHOLAS BRITELL 2010s NONE MD-017
18 18 Rachel Portman UK Rom-Drama 1 Emma 1986 39 7.90000 0.05 29 Yrs RACHEL PORTMAN 1980s 1 WIN MD-018
19 19 Gustavo Santaolalla Argentina Latin Fusion 2 Brokeback Mountain 1978 47 7.80000 0.10 37 Yrs GUSTAVO SANTAOLALLA 1970s MULTI-WINNER MD-019
20 20 Mark Mothersbaugh USA Alt-Rock 0 The Royal Tenenbaums 1981 44 7.50000 0.22 34 Yrs MARK MOTHERSBAUGH 1980s NONE MD-020


4 .First Peek – PROC PRINT & PROC CONTENTS

title "Snapshot of the First 10 Hollywood Music Directors";

proc print data=work.musicdirs(obs=10) label;

   format OscarWins oscfmt.;

run;

Ouput:

Snapshot of the First 10 Hollywood Music Directors

Obs DIRID NAME COUNTRY PRIMARYGENRE OSCARWINS NOTABLEWORK DEBUTYEAR ACTIVEYRS AVGIMDB TWITTERM Total Career
Length (yrs)		 Upper-Case Name Decade of
Debut		 Oscar Prestige Bucket USUBJID
1 1 Hans Zimmer Germany Epic MULTI-WINNER Inception 1984 40 8.89999 1.30 31 Yrs HANS ZIMMER 1980s MULTI-WINNER MD-001
2 2 John Williams USA Orchestral LEGEND Star Wars 1958 66 8.80000 0.40 57 Yrs JOHN WILLIAMS 1950s LEGEND MD-002
3 3 A.R. Rahman India World Fusion MULTI-WINNER Slumdog Millionaire 1992 33 8.10000 27.00 23 Yrs A.R. RAHMAN 1990s MULTI-WINNER MD-003
4 4 Alan Menken USA Disney Musicals MULTI-WINNER The Little Mermaid 1979 45 8.00000 0.15 36 Yrs ALAN MENKEN 1970s MULTI-WINNER MD-004
5 5 Danny Elfman USA Gothic Pop NONE Batman 1980 44 7.70000 0.60 35 Yrs DANNY ELFMAN 1980s NONE MD-005
6 6 Ennio Morricone Italy Spaghetti Weste 1 WIN The Good, the Bad… 1961 59 8.50000 0.02 54 Yrs ENNIO MORRICONE 1960s 1 WIN MD-006
7 7 Howard Shore Canada Symphonic MULTI-WINNER The Lord of the Rings 1979 45 8.89999 0.12 36 Yrs HOWARD SHORE 1970s MULTI-WINNER MD-007
8 8 Thomas Newman USA Ambient NONE American Beauty 1984 40 8.30000 0.08 31 Yrs THOMAS NEWMAN 1980s NONE MD-008
9 9 Michael Giacchino USA Eclectic 1 WIN Up 1994 31 8.39999 0.50 21 Yrs MICHAEL GIACCHINO 1990s 1 WIN MD-009
10 10 James Horner USA Romantic MULTI-WINNER Titanic 1979 37 8.89999 0.07 36 Yrs JAMES HORNER 1970s MULTI-WINNER MD-010


title "Data Dictionary – MUSICDIRS";

proc contents data=work.musicdirs position;

run;

title;

Ouput:

Data Dictionary – MUSICDIRS

The CONTENTS Procedure

Data Set Name WORK.MUSICDIRS Observations 20
Member Type DATA Variables 15
Engine V9 Indexes 0
Created 14/09/2015 00:39:15 Observation Length 832
Last Modified 14/09/2015 00:39:15 Deleted Observations 0
Protection   Compressed NO
Data Set Type   Sorted NO
Label      
Data Representation WINDOWS_64    
Encoding wlatin1 Western (Windows)    

Engine/Host Dependent Information
Data Set Page Size 69632
Number of Data Set Pages 1
First Data Page 1
Max Obs per Page 83
Obs in First Data Page 20
Number of Data Set Repairs 0
ExtendObsCounter YES
Filename C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD16108_DESKTOP-QFAA4KV_\musicdirs.sas7bdat
Release Created 9.0401M2
Host Created X64_8HOME

Alphabetic List of Variables and Attributes
# Variable Type Len Label
8 ACTIVEYRS Num 8  
9 AVGIMDB Num 4  
11 CAREERSTAGE Char 200 Total Career Length (yrs)
3 COUNTRY Char 20  
7 DEBUTYEAR Num 8  
1 DIRID Num 8  
13 ERAGROUP Char 200 Decade of Debut
2 NAME Char 40  
12 NAME_UP Char 40 Upper-Case Name
6 NOTABLEWORK Char 60  
5 OSCARWINS Num 8  
4 PRIMARYGENRE Char 15  
10 TWITTERM Num 8  
15 USUBJID Char 200  
14 WINLABEL Char 12 Oscar Prestige Bucket

Variables in Creation Order
# Variable Type Len Label
1 DIRID Num 8  
2 NAME Char 40  
3 COUNTRY Char 20  
4 PRIMARYGENRE Char 15  
5 OSCARWINS Num 8  
6 NOTABLEWORK Char 60  
7 DEBUTYEAR Num 8  
8 ACTIVEYRS Num 8  
9 AVGIMDB Num 4  
10 TWITTERM Num 8  
11 CAREERSTAGE Char 200 Total Career Length (yrs)
12 NAME_UP Char 40 Upper-Case Name
13 ERAGROUP Char 200 Decade of Debut
14 WINLABEL Char 12 Oscar Prestige Bucket
15 USUBJID Char 200  

5.PROC SORT + Aggregational Statistics

proc sort data=work.musicdirs out=top_scores ;

   by descending AvgIMDb;

run;


title "Top 5 by Average IMDb Score";

proc print data=top_scores(obs=5);

   var Name Country NotableWork AvgIMDb OscarWins;

   format OscarWins WinLabel.;

run;

Ouput:

Top 5 by Average IMDb Score

Obs NAME COUNTRY NOTABLEWORK AVGIMDB OSCARWINS
1 Ramin Djawadi Germany Game of Thrones 9.30000 NONE
2 Hans Zimmer Germany Inception 8.89999 MULTI-WINNER
3 Howard Shore Canada The Lord of the Rings 8.89999 MULTI-WINNER
4 James Horner USA Titanic 8.89999 MULTI-WINNER
5 John Williams USA Star Wars 8.80000 LEGEND


6 .Ad‑hoc Explorations with PROC MEANS & FREQ

proc means data=work.musicdirs mean maxdec=2 nway noprint;

   class Country;

   var AvgIMDb ActiveYrs TwitterM;

   output out=country_stats mean= / autoname;

run;

Ouput:

Top 5 by Average IMDb Score

Obs COUNTRY _TYPE_ _FREQ_ AVGIMDB_MEAN ACTIVEYRS_MEAN TWITTERM_MEAN
1 Argentina 1 1 7.80000 47.0000 0.1000
2 Canada 1 1 8.89999 45.0000 0.1200
3 France 1 1 8.10000 39.0000 0.0900
4 Germany 1 2 9.09999 30.5000 1.0750
5 Iceland 1 1 8.20000 19.0000 0.4000
6 India 1 1 8.10000 33.0000 27.0000
7 Italy 1 1 8.50000 59.0000 0.0200
8 Scotland 1 1 7.60000 23.0000 0.2300
9 Turkey 1 1 7.20000 21.0000 0.1700
10 UK 1 1 7.90000 39.0000 0.0500
11 USA 1 9 8.30000 37.3333 0.2722


proc freq data=work.musicdirs order=freq;

   tables EraGroup*WinLabel / nopercent norow nocol;

run;

Ouput:

Top 5 by Average IMDb Score

The FREQ Procedure

Frequency
Table of ERAGROUP by WINLABEL
ERAGROUP(Decade of Debut) WINLABEL(Oscar Prestige Bucket)
MULTI-WINNER NONE 1 WIN LEGEND Total
1980s
2
3
1
0
6
2000s
1
3
1
0
5
1970s
4
0
0
0
4
1990s
1
0
1
0
2
1950s
0
0
0
1
1
1960s
0
0
1
0
1
2010s
0
1
0
0
1
Total
8
7
4
1
20


7 .PROC SQL Mastery

proc sql;

   create table genre_summary as

   select PrimaryGenre,

          count(*) as N_Directors,

          mean(AvgIMDb) as MeanIMDb format=5.2,

          sum(TwitterM) as TotalTwtr format=6.1,

          sum(case when OscarWins>0 then 1 else 0 end) as Awarded

   from work.musicdirs

   group by PrimaryGenre

   having calculated N_Directors >= 1

   order by calculated MeanIMDb desc;

quit;

proc print;run;

Ouput:

Obs PRIMARYGENRE N_DIRECTORS MEANIMDB TOTALTWTR AWARDED
1 Epic TV 1 9.30 0.9 0
2 Symphonic 1 8.90 0.1 1
3 Epic 1 8.90 1.3 1
4 Romantic 1 8.90 0.1 1
5 Neo-Classical 1 8.80 0.2 0
6 Orchestral 1 8.80 0.4 1
7 Spaghetti Weste 1 8.50 0.0 1
8 Eclectic 1 8.40 0.5 1
9 Ambient 1 8.30 0.1 0
10 Jazz-Infused 1 8.30 0.3 1
11 Atmospheric 1 8.20 0.4 1
12 Lyric Modern 1 8.10 0.1 1
13 World Fusion 1 8.10 27.0 1
14 Disney Musicals 1 8.00 0.2 1
15 Rom-Drama 1 7.90 0.1 1
16 Latin Fusion 1 7.80 0.1 1
17 Gothic Pop 1 7.70 0.6 0
18 Hybrid 1 7.60 0.2 0
19 Alt-Rock 1 7.50 0.2 0
20 Action-SciFi 1 7.20 0.2 0


8. Reusable Macros – Country Sheets & Genre Posters

8.1 Generate one HTML report per country

%macro country_sheet(ctry);

   %let _uc=%upcase(&ctry);

   ods html file="&sysuserhome.\musicdir_&_uc..html" style=pearl;


   title "Music Directors from &ctry";

   proc report data=work.musicdirs nowd;

      where upcase(Country)="&_uc";

      column Name PrimaryGenre OscarWins WinLabel AvgIMDb CareerStage;

      define WinLabel / style(column)=[foreground=darkblue];

   run;

   ods html close;

%mend;


%country_sheet(USA)

Ouput:

Music Directors from USA

NAME PRIMARYGENRE OSCARWINS Oscar Prestige Bucket AVGIMDB Total Career Length (yrs)
John Williams Orchestral 5 LEGEND 8.7999954 57 Yrs
Alan Menken Disney Musicals 2 MULTI-WINNER 8 36 Yrs
Danny Elfman Gothic Pop 0 NONE 7.6999969 35 Yrs
Thomas Newman Ambient 0 NONE 8.2999954 31 Yrs
Michael Giacchino Eclectic 1 1 WIN 8.3999939 21 Yrs
James Horner Romantic 2 MULTI-WINNER 8.8999939 36 Yrs
Justin Hurwitz Jazz-Infused 2 MULTI-WINNER 8.2999954 6 Yrs
Nicholas Britell Neo-Classical 0 NONE 8.7999954 3 Yrs
Mark Mothersbaugh Alt-Rock 0 NONE 7.5 34 Yrs

%country_sheet(UK)

Ouput:

Music Directors from UK

NAME PRIMARYGENRE OSCARWINS Oscar Prestige Bucket AVGIMDB Total Career Length (yrs)
Rachel Portman Rom-Drama 1 1 WIN 7.8999977 29 Yrs


%country_sheet(Germany)

Ouput:

Music Directors from Germany

NAME PRIMARYGENRE OSCARWINS Oscar Prestige Bucket AVGIMDB Total Career Length (yrs)
Hans Zimmer Epic 2 MULTI-WINNER 8.8999939 31 Yrs
Ramin Djawadi Epic TV 0 NONE 9.2999954 11 Yrs

8.2  Rapid‑fire bar chart macro

%macro bar_genre;

   proc sgplot data=genre_summary;

      vbar PrimaryGenre / response=N_Directors;

      yaxis label="Number of Directors";

      xaxis label="Primary Genre";

      title "Distribution of Genres Among Hollywood Music Directors";

   run;

%mend;

%bar_genre

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           2.46 seconds

      user cpu time       0.15 seconds

      system cpu time     0.18 seconds

      memory              6359.31k

      OS Memory           19316.00k

      Timestamp           14/09/2015 12:48:33 AM

      Step Count                        37  Switch Count  0


NOTE: Listing image output written to SGPlot.png.

NOTE: There were 20 observations read from the data set WORK.GENRE_SUMMARY.





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