226.GLOBAL EPILEPSY TREATMENT DATA ANALYSIS USING PROC OPTIONS | PROC FORMAT | PROC PRINT | PROC CONTENTS | PROC FREQ | PROC MEANS | PROC SGPLOT | PROC SQL | PROC EXPORT | PROC COMPARE

GLOBAL EPILEPSY TREATMENT DATA ANALYSIS USING PROC OPTIONS | PROC FORMAT | PROC PRINT | PROC CONTENTS | PROC FREQ | PROC MEANS | PROC SGPLOT | PROC SQL | PROC EXPORT | PROC COMPARE

 /*Worldwide epilepsy‑treatment dataset*/

1.Global program‑level OPTIONS (good style)

options nocenter nodate nonumber ls=132 ps=60

        varlenchk=NOWARN         /* ignore length “shrinking” */

        fmtsearch=(work);        /* look‑up custom formats    */

2.Creating the 25‑row dataset

data work.epil_world;

   length PatientID $8  Country $20 Region $15  EpilepsyType $25

          Sex $1 PrimaryDrug $15 SeizureControl $12 TreatmentRegimen $10 ;

   format DiagnosisDate TreatmentStart TreatmentEnd date9. ;

   infile datalines dsd truncover;

   input PatientID Country Region EpilepsyType $

         Age Sex $

         DiagnosisDate :date9.

         TreatmentStart :date9.

         TreatmentEnd   :date9.

         PrimaryDrug $ SeizureControl $ ;

   /*—— DERIVE LENGTH‑OF‑TREATMENT ——*/

   DurationMonths = intck('month', TreatmentStart, TreatmentEnd );

   /*—— DERIVE REGIMEN TYPE ——*/

   TreatmentRegimen = ifc(DurationMonths>24, 'Poly', 'Mono');

   label DurationMonths = 'Treatment Duration (Months)';

datalines;

P001,India,Asia,Focal,24,M,05JAN2022,15JAN2022,15JUL2024,Levetiracetam,Controlled

P002,USA,NorthAmerica,Generalized,32,F,20MAR2021,10APR2021,10JUN2023,Valproate,Partial

P003,Brazil,SouthAmerica,Focal_to_Bilateral,45,M,12DEC2019,01JAN2020,01JAN2024,Carbamazepine,Controlled

P004,UK,Europe,Generalized,29,F,15FEB2020,20FEB2020,20FEB2022,Topiramate,Controlled

P005,Japan,Asia,Focal,38,M,01AUG2018,15AUG2018,15FEB2025,Lacosamide,Uncontrolled

P006,Nigeria,Africa,Unknown,27,F,10OCT2022,20OCT2022,20APR2024,Phenytoin,Partial

P007,Germany,Europe,Focal,51,M,05MAY2017,20MAY2017,20MAY2020,Carbamazepine,Controlled

P008,Canada,NorthAmerica,Generalized,33,F,02JAN2023,05JAN2023,05JUL2024,Valproate,Partial

P009,Australia,Oceania,Focal,41,M,22JUL2020,01AUG2020,01FEB2023,Levetiracetam,Controlled

P010,India,Asia,Focal_to_Bilateral,16,F,12SEP2023,15SEP2023,15MAR2025,Oxcarbazepine,Uncontrolled

P011,France,Europe,Generalized,60,M,20APR2016,01MAY2016,01MAY2024,Valproate,Controlled

P012,Brazil,SouthAmerica,Focal,34,F,15NOV2020,20NOV2020,20NOV2023,Lacosamide,Partial

P013,USA,NorthAmerica,Unknown,22,M,01JAN2024,15JAN2024,15JUN2024,Levetiracetam,Uncontrolled

P014,Japan,Asia,Generalized,57,F,10JUN2019,20JUN2019,20DEC2023,Topiramate,Controlled

P015,India,Asia,Focal,30,M,05JUL2021,10JUL2021,10JAN2025,Carbamazepine,Partial

P016,Kenya,Africa,Focal,28,F,22FEB2022,01MAR2022,01MAR2024,Lacosamide,Controlled

P017,UK,Europe,Unknown,46,M,11NOV2020,20NOV2020,20NOV2023,Phenytoin,Partial

P018,USA,NorthAmerica,Focal_to_Bilateral,37,F,01APR2022,10APR2022,10OCT2024,Valproate,Controlled

P019,Australia,Oceania,Generalized,19,M,01JAN2023,05JAN2023,05JAN2025,Levetiracetam,Controlled

P020,India,Asia,Focal,52,F,15DEC2018,01JAN2019,01JAN2024,Carbamazepine,Uncontrolled

P021,Germany,Europe,Focal,48,M,20MAY2020,25MAY2020,25NOV2023,Lacosamide,Partial

P022,Canada,NorthAmerica,Generalized,31,F,02FEB2021,10FEB2021,10AUG2023,Topiramate,Controlled

P023,Nigeria,Africa,Focal_to_Bilateral,43,M,08AUG2019,15AUG2019,15FEB2023,Valproate,Partial

P024,Brazil,SouthAmerica,Generalized,26,F,10DEC2022,20DEC2022,20JUN2024,Phenytoin,Controlled

P025,Japan,Asia,Unknown,55,M,01SEP2020,10SEP2020,10MAR2023,Levetiracetam,Uncontrolled

;

run;

proc print;run;

Output:

Obs	PatientID	Country	Region	EpilepsyType	Sex	PrimaryDrug	SeizureControl	TreatmentRegimen	DiagnosisDate	TreatmentStart	TreatmentEnd	Age	DurationMonths
1	P001	India	Asia	Focal	M	Levetiracetam	Controlled	Poly	05JAN2022	15JAN2022	15JUL2024	24	30
2	P002	USA	NorthAmerica	Generalized	F	Valproate	Partial	Poly	20MAR2021	10APR2021	10JUN2023	32	26
3	P003	Brazil	SouthAmerica	Focal_to_Bilateral	M	Carbamazepine	Controlled	Poly	12DEC2019	01JAN2020	01JAN2024	45	48
4	P004	UK	Europe	Generalized	F	Topiramate	Controlled	Mono	15FEB2020	20FEB2020	20FEB2022	29	24
5	P005	Japan	Asia	Focal	M	Lacosamide	Uncontrolled	Poly	01AUG2018	15AUG2018	15FEB2025	38	78
6	P006	Nigeria	Africa	Unknown	F	Phenytoin	Partial	Mono	10OCT2022	20OCT2022	20APR2024	27	18
7	P007	Germany	Europe	Focal	M	Carbamazepine	Controlled	Poly	05MAY2017	20MAY2017	20MAY2020	51	36
8	P008	Canada	NorthAmerica	Generalized	F	Valproate	Partial	Mono	02JAN2023	05JAN2023	05JUL2024	33	18
9	P009	Australia	Oceania	Focal	M	Levetiracetam	Controlled	Poly	22JUL2020	01AUG2020	01FEB2023	41	30
10	P010	India	Asia	Focal_to_Bilateral	F	Oxcarbazepine	Uncontrolled	Mono	12SEP2023	15SEP2023	15MAR2025	16	18
11	P011	France	Europe	Generalized	M	Valproate	Controlled	Poly	20APR2016	01MAY2016	01MAY2024	60	96
12	P012	Brazil	SouthAmerica	Focal	F	Lacosamide	Partial	Poly	15NOV2020	20NOV2020	20NOV2023	34	36
13	P013	USA	NorthAmerica	Unknown	M	Levetiracetam	Uncontrolled	Mono	01JAN2024	15JAN2024	15JUN2024	22	5
14	P014	Japan	Asia	Generalized	F	Topiramate	Controlled	Poly	10JUN2019	20JUN2019	20DEC2023	57	54
15	P015	India	Asia	Focal	M	Carbamazepine	Partial	Poly	05JUL2021	10JUL2021	10JAN2025	30	42
16	P016	Kenya	Africa	Focal	F	Lacosamide	Controlled	Mono	22FEB2022	01MAR2022	01MAR2024	28	24
17	P017	UK	Europe	Unknown	M	Phenytoin	Partial	Poly	11NOV2020	20NOV2020	20NOV2023	46	36
18	P018	USA	NorthAmerica	Focal_to_Bilateral	F	Valproate	Controlled	Poly	01APR2022	10APR2022	10OCT2024	37	30
19	P019	Australia	Oceania	Generalized	M	Levetiracetam	Controlled	Mono	01JAN2023	05JAN2023	05JAN2025	19	24
20	P020	India	Asia	Focal	F	Carbamazepine	Uncontrolled	Poly	15DEC2018	01JAN2019	01JAN2024	52	60
21	P021	Germany	Europe	Focal	M	Lacosamide	Partial	Poly	20MAY2020	25MAY2020	25NOV2023	48	42
22	P022	Canada	NorthAmerica	Generalized	F	Topiramate	Controlled	Poly	02FEB2021	10FEB2021	10AUG2023	31	30
23	P023	Nigeria	Africa	Focal_to_Bilateral	M	Valproate	Partial	Poly	08AUG2019	15AUG2019	15FEB2023	43	42
24	P024	Brazil	SouthAmerica	Generalized	F	Phenytoin	Controlled	Mono	10DEC2022	20DEC2022	20JUN2024	26	18
25	P025	Japan	Asia	Unknown	M	Levetiracetam	Uncontrolled	Poly	01SEP2020	10SEP2020	10MAR2023	55	30

3.First‑look exploration

3.1 What’s inside? (PROC CONTENTS)

proc contents data=work.epil_world varnum;

   title "Structure of EPIL_W (Worldwide Epilepsy Study)";

run;

Output:

Structure of EPIL_WORLD (Worldwide Epilepsy Study)

The CONTENTS Procedure

Data Set Name	WORK.EPIL_WORLD	Observations	25
Member Type	DATA	Variables	13
Engine	V9	Indexes	0
Created	14/09/2015 00:06:14	Observation Length	152
Last Modified	14/09/2015 00:06:14	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	65536
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	430
Obs in First Data Page	25
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD10416_DESKTOP-QFAA4KV_\epil_world.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Variables in Creation Order
#	Variable	Type	Len	Format	Label
1	PatientID	Char	8
2	Country	Char	20
3	Region	Char	15
4	EpilepsyType	Char	25
5	Sex	Char	1
6	PrimaryDrug	Char	15
7	SeizureControl	Char	12
8	TreatmentRegimen	Char	10
9	DiagnosisDate	Num	8	DATE9.
10	TreatmentStart	Num	8	DATE9.
11	TreatmentEnd	Num	8	DATE9.
12	Age	Num	8
13	DurationMonths	Num	8		Treatment Duration (Months)

3.2 Raw peek (PROC PRINT with WHERE)

proc print data=work.epil_world(obs=10);

   title "First 10 Records – Sanity Check";

   where Country in ('India','USA');

run;

Output:

First 10 Records – Sanity Check

Obs	PatientID	Country	Region	EpilepsyType	Sex	PrimaryDrug	SeizureControl	TreatmentRegimen	DiagnosisDate	TreatmentStart	TreatmentEnd	Age	DurationMonths
1	P001	India	Asia	Focal	M	Levetiracetam	Controlled	Poly	05JAN2022	15JAN2022	15JUL2024	24	30
2	P002	USA	NorthAmerica	Generalized	F	Valproate	Partial	Poly	20MAR2021	10APR2021	10JUN2023	32	26
10	P010	India	Asia	Focal_to_Bilateral	F	Oxcarbazepine	Uncontrolled	Mono	12SEP2023	15SEP2023	15MAR2025	16	18
13	P013	USA	NorthAmerica	Unknown	M	Levetiracetam	Uncontrolled	Mono	01JAN2024	15JAN2024	15JUN2024	22	5
15	P015	India	Asia	Focal	M	Carbamazepine	Partial	Poly	05JUL2021	10JUL2021	10JAN2025	30	42
18	P018	USA	NorthAmerica	Focal_to_Bilateral	F	Valproate	Controlled	Poly	01APR2022	10APR2022	10OCT2024	37	30
20	P020	India	Asia	Focal	F	Carbamazepine	Uncontrolled	Poly	15DEC2018	01JAN2019	01JAN2024	52	60

4.Descriptive statistics

4.1 Demographics by epilepsy type (PROC FREQ)

proc freq data=work.epil_world order=freq;

   table EpilepsyType*Sex / nocol nopercent nocum;

   title "Sex Distribution by Epilepsy Classification";

run;

Output:

Sex Distribution by Epilepsy Classification

The FREQ Procedure

Frequency Row Pct

Table of EpilepsyType by Sex EpilepsyType Sex M F Total Focal 6 66.67 3 33.33 9   Generalized 2 25.00 6 75.00 8   Focal_to_Bilateral 2 50.00 2 50.00 4   Unknown 3 75.00 1 25.00 4   Total 13 12 25

4.2 Treatment length distribution (PROC MEANS)

proc means data=work.epil_world n mean median min max maxdec=1;

   class EpilepsyType;

   var DurationMonths Age;

   title "Duration & Age, Broken Down by Epilepsy Class";

run;

Output:

Duration & Age, Broken Down by Epilepsy Class

The MEANS Procedure

EpilepsyType	N Obs	Variable	Label	N	Mean	Median	Minimum	Maximum
Focal	9	DurationMonths Age	Treatment Duration (Months)	9 9	42.0 38.4	36.0 38.0	24.0 24.0	78.0 52.0
Focal_to_Bilateral	4	DurationMonths Age	Treatment Duration (Months)	4 4	34.5 35.3	36.0 40.0	18.0 16.0	48.0 45.0
Generalized	8	DurationMonths Age	Treatment Duration (Months)	8 8	36.3 35.9	25.0 31.5	18.0 19.0	96.0 60.0
Unknown	4	DurationMonths Age	Treatment Duration (Months)	4 4	22.3 37.5	24.0 36.5	5.0 22.0	36.0 55.0

4.3 Quick plot (PROC SGPLOT with summary function)

proc sgplot data=work.epil_world;

   vbox DurationMonths / category=Region;

   title "Boxplot: Months on Treatment by Global Region";

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           3.32 seconds

      cpu time            0.56 seconds

NOTE: Listing image output written to SGPlot1.png.

NOTE: There were 25 observations read from the data set WORK.EPIL_WORLD.

5.Custom formats to declutter outputs

proc format;

   value $reg 'NorthAmerica' = 'N. America'

              'SouthAmerica' = 'S. America'

              'Oceania'      = 'Oce'

              other          = 'OTHER';

run;

proc freq data=work.epil_world;

   format Region $reg.;

   tables Region*SeizureControl / norow nocol nopercent;

   title "Outcome vs Region Using User Format";

run;

Output:

Outcome vs Region Using User Format

The FREQ Procedure

Frequency

Table of Region by SeizureControl Region SeizureControl Controlled Partial Uncontrolled Total OTHER 6 5 4 15 N. America 2 2 1 5 Oce 2 0 0 2 S. America 2 1 0 3 Total 12 8 5 25

6.Analytic SQL snippets

proc sql;

   title "Mean Treatment Duration by Country & Control Status";

   select Country,

          SeizureControl,

          count(*)               as N_Patients,

          mean(DurationMonths)   as Avg_Months format=5.1

   from   work.epil_world

   group  by Country, SeizureControl

   having calculated Avg_Months > 18

   order  by Avg_Months desc;

quit;

Output:

Mean Treatment Duration by Country & Control Status

Country	SeizureControl	N_Patients	Avg_Months
France	Controlled	1	96.0
Japan	Uncontrolled	2	54.0
Japan	Controlled	1	54.0
Germany	Partial	1	42.0
India	Partial	1	42.0
India	Uncontrolled	2	39.0
UK	Partial	1	36.0
Germany	Controlled	1	36.0
Brazil	Partial	1	36.0
Brazil	Controlled	2	33.0
Nigeria	Partial	2	30.0
Canada	Controlled	1	30.0
India	Controlled	1	30.0
USA	Controlled	1	30.0
Australia	Controlled	2	27.0
USA	Partial	1	26.0
UK	Controlled	1	24.0
Kenya	Controlled	1	24.0

7.Reusable macro: %SummaryBy

%macro SummaryBy(data=, class=, statvar=, outfile=);

   %local i n var;

   %let n=%sysfunc(countw(&class));

   %do i=1 %to &n;

      %let var=%scan(&class,&i);

      title "===&var Summary===";

      proc means data=&data n mean median maxdec=1;

         class &var;

         var &statvar;

      run;

      proc freq data=&data;

         tables &var / nocum nopercent;

      run;

      /*—— optional export ——*/

      %if %length(&outfile) %then %do;

         proc export data=&data(keep=&var &statvar)

             outfile="&outfile._&var..csv"

             dbms=csv replace;

         run;

      %end;

   %end;

%mend SummaryBy;

%SummaryBy(data=work.epil_world,

           class=Region EpilepsyType TreatmentRegimen,

           statvar=DurationMonths,

           outfile=%sysfunc(pathname(work))/epil_out);

Output:

===Region Summary===

The MEANS Procedure

Analysis Variable : DurationMonths Treatment Duration (Months)
Region	N Obs	N	Mean	Median
Africa	3	3	28.0	24.0
Asia	7	7	44.6	42.0
Europe	5	5	46.8	36.0
NorthAmerica	5	5	21.8	26.0
Oceania	2	2	27.0	27.0
SouthAmerica	3	3	34.0	36.0

===Region Summary===

The FREQ Procedure

Region	Frequency
Africa	3
Asia	7
Europe	5
NorthAmerica	5
Oceania	2
SouthAmerica	3

===EpilepsyType Summary===

The MEANS Procedure

Analysis Variable : DurationMonths Treatment Duration (Months)
EpilepsyType	N Obs	N	Mean	Median
Focal	9	9	42.0	36.0
Focal_to_Bilateral	4	4	34.5	36.0
Generalized	8	8	36.3	25.0
Unknown	4	4	22.3	24.0

===EpilepsyType Summary===

The FREQ Procedure

EpilepsyType	Frequency
Focal	9
Focal_to_Bilateral	4
Generalized	8
Unknown	4

===TreatmentRegimen Summary===

The MEANS Procedure

Analysis Variable : DurationMonths Treatment Duration (Months)
TreatmentRegimen	N Obs	N	Mean	Median
Mono	8	8	18.6	18.0
Poly	17	17	43.9	36.0

===TreatmentRegimen Summary===

The FREQ Procedure

TreatmentRegimen	Frequency
Mono	8
Poly	17

8.Quality‑control snapshot

Compare derived vs re‑computed (PROC COMPARE)

data qc;

   set work.epil_world;

   RecalcMonths = intck('month', TreatmentStart, TreatmentEnd);

run;

proc print;run;

Obs	PatientID	Country	Region	EpilepsyType	Sex	PrimaryDrug	SeizureControl	TreatmentRegimen	DiagnosisDate	TreatmentStart	TreatmentEnd	Age	DurationMonths	RecalcMonths
1	P001	India	Asia	Focal	M	Levetiracetam	Controlled	Poly	05JAN2022	15JAN2022	15JUL2024	24	30	30
2	P002	USA	NorthAmerica	Generalized	F	Valproate	Partial	Poly	20MAR2021	10APR2021	10JUN2023	32	26	26
3	P003	Brazil	SouthAmerica	Focal_to_Bilateral	M	Carbamazepine	Controlled	Poly	12DEC2019	01JAN2020	01JAN2024	45	48	48
4	P004	UK	Europe	Generalized	F	Topiramate	Controlled	Mono	15FEB2020	20FEB2020	20FEB2022	29	24	24
5	P005	Japan	Asia	Focal	M	Lacosamide	Uncontrolled	Poly	01AUG2018	15AUG2018	15FEB2025	38	78	78
6	P006	Nigeria	Africa	Unknown	F	Phenytoin	Partial	Mono	10OCT2022	20OCT2022	20APR2024	27	18	18
7	P007	Germany	Europe	Focal	M	Carbamazepine	Controlled	Poly	05MAY2017	20MAY2017	20MAY2020	51	36	36
8	P008	Canada	NorthAmerica	Generalized	F	Valproate	Partial	Mono	02JAN2023	05JAN2023	05JUL2024	33	18	18
9	P009	Australia	Oceania	Focal	M	Levetiracetam	Controlled	Poly	22JUL2020	01AUG2020	01FEB2023	41	30	30
10	P010	India	Asia	Focal_to_Bilateral	F	Oxcarbazepine	Uncontrolled	Mono	12SEP2023	15SEP2023	15MAR2025	16	18	18
11	P011	France	Europe	Generalized	M	Valproate	Controlled	Poly	20APR2016	01MAY2016	01MAY2024	60	96	96
12	P012	Brazil	SouthAmerica	Focal	F	Lacosamide	Partial	Poly	15NOV2020	20NOV2020	20NOV2023	34	36	36
13	P013	USA	NorthAmerica	Unknown	M	Levetiracetam	Uncontrolled	Mono	01JAN2024	15JAN2024	15JUN2024	22	5	5
14	P014	Japan	Asia	Generalized	F	Topiramate	Controlled	Poly	10JUN2019	20JUN2019	20DEC2023	57	54	54
15	P015	India	Asia	Focal	M	Carbamazepine	Partial	Poly	05JUL2021	10JUL2021	10JAN2025	30	42	42
16	P016	Kenya	Africa	Focal	F	Lacosamide	Controlled	Mono	22FEB2022	01MAR2022	01MAR2024	28	24	24
17	P017	UK	Europe	Unknown	M	Phenytoin	Partial	Poly	11NOV2020	20NOV2020	20NOV2023	46	36	36
18	P018	USA	NorthAmerica	Focal_to_Bilateral	F	Valproate	Controlled	Poly	01APR2022	10APR2022	10OCT2024	37	30	30
19	P019	Australia	Oceania	Generalized	M	Levetiracetam	Controlled	Mono	01JAN2023	05JAN2023	05JAN2025	19	24	24
20	P020	India	Asia	Focal	F	Carbamazepine	Uncontrolled	Poly	15DEC2018	01JAN2019	01JAN2024	52	60	60
21	P021	Germany	Europe	Focal	M	Lacosamide	Partial	Poly	20MAY2020	25MAY2020	25NOV2023	48	42	42
22	P022	Canada	NorthAmerica	Generalized	F	Topiramate	Controlled	Poly	02FEB2021	10FEB2021	10AUG2023	31	30	30
23	P023	Nigeria	Africa	Focal_to_Bilateral	M	Valproate	Partial	Poly	08AUG2019	15AUG2019	15FEB2023	43	42	42
24	P024	Brazil	SouthAmerica	Generalized	F	Phenytoin	Controlled	Mono	10DEC2022	20DEC2022	20JUN2024	26	18	18
25	P025	Japan	Asia	Unknown	M	Levetiracetam	Uncontrolled	Poly	01SEP2020	10SEP2020	10MAR2023	55	30	30

proc compare base=qc(rename=(DurationMonths=Dur1))

             compare=qc(rename=(RecalcMonths=Dur2))

             criterion=0.0001 /* tolerance */

             out=diff(where=(difference ne 0));

   id PatientID;

   var Dur1;

   with Dur2;

   title "QC: Derived Duration vs Recalc";

run;

Output:

QC: Derived Duration vs Recalc

The COMPARE Procedure Comparison of WORK.QC with WORK.QC (Method=RELATIVE(2.22E-10), Criterion=0.0001) Data Set Summary Dataset Created Modified NVar NObs WORK.QC 14SEP15:00:35:14 14SEP15:00:35:14 14 25 WORK.QC 14SEP15:00:35:14 14SEP15:00:35:14 14 25 Variables Summary Number of Variables in Common: 12. Number of Variables in WORK.QC but not in WORK.QC: 2. Number of Variables in WORK.QC but not in WORK.QC: 2. Number of ID Variables: 1. Number of VAR Statement Variables: 1. Number of WITH Statement Variables: 1.

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225.ANALYZING LEGENDARY INDIAN COURTROOM SCENES USING PROC DATASTEP | PROC CONTENTS | PROC MEANS | PROC UNIVARIATE | PROC FREQ | PROC SQL | PROC REPORT | PROC SUMMARY | PROC EXPORT IN SAS

ANALYZING LEGENDARY INDIAN COURTROOM SCENES USING PROC DATASTEP | PROC CONTENTS | PROC MEANS | PROC UNIVARIATE | PROC FREQ | PROC SQL | PROC REPORT | PROC SUMMARY | PROC EXPORT IN SAS

/*Dataset of “ultimate” courtroom sequences in Indian cinema.*/

1.Global SAS environment options

options nocenter             

        fmtsearch=(work)     

        pagesize=60 linesize=120;

2.Creating the data set (DATA step + functions)

data court_scenes_in_films;

   infile datalines dsd truncover;

   length filmname $40 lang $12 verdict $15 lead_lawyer director $30 famous_quote $120;

   input filmid filmname :$40. lang :$12. rel_year scene_min verdict :$15.

         lead_lawyer :$30. director :$30. drama_score famous_quote :$120. boxoffice_cr;

   /*--- DERIVE decade label with INTNX + PUT ---*/

   dec_start = intnx('year', mdy(1,1,rel_year), 0, 'B'); /* first day of rel_year */

   decade    = put(year(dec_start), 4.) || "s";          /* e.g. '1990s' */

   /*--- Standardise verdict text */

   length verdict_up $15;

   verdict_up = upcase(verdict);

   /*--- Flag for “acquittal” vs “conviction” using COMPRESS + COMPBL ---*/

   if compress(verdict_up) in ("NOTGUILTY","ACQUITTAL") then outcome=0;

   else outcome=1;

   /*--- Make a brief slug for URLs with CATX ---*/

   slug = lowcase(catx('-', compress(filmname, , 'kad'), rel_year));

   label scene_min   = "Minutes of Courtroom Action"

         drama_score = "Subjective Drama Intensity 0–100"

         dec_start   = "Calendar start of release year"

         outcome     = "1=Conviction 0=Acquittal";

   format dec_start date9. boxoffice_cr 8.1;

datalines;

101,"Pink","Hindi",2016,29.4,"Not Guilty","Amitabh Bachchan","Aniruddha Roy Chowdhury",95,"No means no!",108.7

102,"Damini","Hindi",1993,24.7,"Guilty","Sunny Deol","Rajkumar Santoshi",97,"Tareekh pe tareekh!",39.8

103,"Court","Marathi",2014,38.2,"Adjourned","Vivek Gomber","Chaitanya Tamhane",92,"Justice delayed is justice denied",1.8

104,"Vakeel Saab","Telugu",2021,25.5,"Not Guilty","Pawan Kalyan","Venu Sriram",88,"Naaku real stamina undhi!",137.3

105,"Shahid","Hindi",2013,17.0,"Guilty","Rajkummar Rao","Hansal Mehta",90,"Terror has no religion",5.6

106,"Meri Jung","Hindi",1985,30.2,"Guilty","Anil Kapoor","Subhash Ghai",93,"Kanoon andha nahi hai!",9.7

107,"Veer-Zaara","Hindi",2004,15.3,"Not Guilty","Rani Mukerji","Yash Chopra",80,"Love knows no borders",97.5

108,"Anniyan","Tamil",2005,20.8,"Guilty","Vikram","S. Shankar",89,"Rules are for everyone!",100.1

109,"Oh My Kadavule","Tamil",2020,10.5,"Adjourned","Ashok Selvan","Ashwath Marimuthu",70,"Fate is playful",52.4

110,"Nerkonda Paarvai","Tamil",2019,28.7,"Not Guilty","Ajith Kumar","H. Vinoth",91,"No means no—Tamil edition",180.7

111,"Rustom","Hindi",2016,23.0,"Not Guilty","Akshay Kumar","Tinu Suresh Desai",85,"I confess my love, not guilt",216.3

112,"Jolly LLB","Hindi",2013,19.4,"Guilty","Arshad Warsi","Subhash Kapoor",87,"Jolly never quits",32.3

113,"Jolly LLB 2","Hindi",2017,22.8,"Guilty","Akshay Kumar","Subhash Kapoor",86,"Truth will out",195.0

114,"OMG","Hindi",2012,14.6,"Guilty","Paresh Rawal","Umesh Shukla",78,"God is in the details",83.2

115,"Raazi","Hindi",2018,8.4,"Adjourned","Alia Bhatt","Meghna Gulzar",82,"Watan ke aage kuch nahi",197.4

116,"Section 375","Hindi",2019,27.3,"Guilty","Akshaye Khanna","Ajay Bahl",94,"Law is a tool!",20.4

117,"Chekka Chivantha Vaanam","Tamil",2018,6.2,"Adjourned","Arvind Swami","Mani Ratnam",76,"Family first?",92.1

118,"Talvar","Hindi",2015,12.7,"Guilty","Irrfan Khan","Meghna Gulzar",90,"Truth has many versions",30.0

119,"Visaranai","Tamil",2015,16.5,"Guilty","Dinesh","Vetrimaaran",96,"Systemic rot exposed",6.0

120,"Kaappaan","Tamil",2019,9.9,"Acquittal","Suriya","K. V. Anand",75,"Nation over self",150.6

121,"Drishyam 2","Malayalam",2021,18.2,"Acquittal","Mohanlal","Jeethu Joseph",88,"Family is everything",58.0

122,"Manichitrathazhu","Malayalam",1993,11.0,"Adjourned","Suresh Gopi","Fazil",84,"Split personalities at play",23.5

123,"Nyaay: The Justice","Hindi",2021,21.4,"Guilty","Zuber K. Khan","Dilip Gulati",50,"Justice or vendetta?",0.9

124,"Thalaivi","Tamil",2021,13.6,"Acquittal","Kangana Ranaut","A. L. Vijay",79,"History will remember",30.7

125,"Singham","Hindi",2011,7.3,"Guilty","Ajay Devgn","Rohit Shetty",74,"Aata majhi satakli!",155.0

;

run;

proc print;run;

Output:

Obs	filmname	lang	verdict	lead_lawyer	director	famous_quote	filmid	rel_year	scene_min	drama_score	boxoffice_cr	dec_start	decade	verdict_up	outcome	slug
1	Pink	Hindi	Not Guilty	Amitabh Bachchan	Aniruddha Roy Chowdhury	No means no!	101	2016	29.4	95	108.7	01JAN2016	2016s	NOT GUILTY	0	pink-2016
2	Damini	Hindi	Guilty	Sunny Deol	Rajkumar Santoshi	Tareekh pe tareekh!	102	1993	24.7	97	39.8	01JAN1993	1993s	GUILTY	1	damini-1993
3	Court	Marathi	Adjourned	Vivek Gomber	Chaitanya Tamhane	Justice delayed is justice denied	103	2014	38.2	92	1.8	01JAN2014	2014s	ADJOURNED	1	court-2014
4	Vakeel Saab	Telugu	Not Guilty	Pawan Kalyan	Venu Sriram	Naaku real stamina undhi!	104	2021	25.5	88	137.3	01JAN2021	2021s	NOT GUILTY	0	vakeelsaab-2021
5	Shahid	Hindi	Guilty	Rajkummar Rao	Hansal Mehta	Terror has no religion	105	2013	17.0	90	5.6	01JAN2013	2013s	GUILTY	1	shahid-2013
6	Meri Jung	Hindi	Guilty	Anil Kapoor	Subhash Ghai	Kanoon andha nahi hai!	106	1985	30.2	93	9.7	01JAN1985	1985s	GUILTY	1	merijung-1985
7	Veer-Zaara	Hindi	Not Guilty	Rani Mukerji	Yash Chopra	Love knows no borders	107	2004	15.3	80	97.5	01JAN2004	2004s	NOT GUILTY	0	veerzaara-2004
8	Anniyan	Tamil	Guilty	Vikram	S. Shankar	Rules are for everyone!	108	2005	20.8	89	100.1	01JAN2005	2005s	GUILTY	1	anniyan-2005
9	Oh My Kadavule	Tamil	Adjourned	Ashok Selvan	Ashwath Marimuthu	Fate is playful	109	2020	10.5	70	52.4	01JAN2020	2020s	ADJOURNED	1	ohmykadavule-2020
10	Nerkonda Paarvai	Tamil	Not Guilty	Ajith Kumar	H. Vinoth	No means no—Tamil edition	110	2019	28.7	91	180.7	01JAN2019	2019s	NOT GUILTY	0	nerkondapaarvai-2019
11	Rustom	Hindi	Not Guilty	Akshay Kumar	Tinu Suresh Desai	I confess my love, not guilt	111	2016	23.0	85	216.3	01JAN2016	2016s	NOT GUILTY	0	rustom-2016
12	Jolly LLB	Hindi	Guilty	Arshad Warsi	Subhash Kapoor	Jolly never quits	112	2013	19.4	87	32.3	01JAN2013	2013s	GUILTY	1	jollyllb-2013
13	Jolly LLB 2	Hindi	Guilty	Akshay Kumar	Subhash Kapoor	Truth will out	113	2017	22.8	86	195.0	01JAN2017	2017s	GUILTY	1	jollyllb2-2017
14	OMG	Hindi	Guilty	Paresh Rawal	Umesh Shukla	God is in the details	114	2012	14.6	78	83.2	01JAN2012	2012s	GUILTY	1	omg-2012
15	Raazi	Hindi	Adjourned	Alia Bhatt	Meghna Gulzar	Watan ke aage kuch nahi	115	2018	8.4	82	197.4	01JAN2018	2018s	ADJOURNED	1	raazi-2018
16	Section 375	Hindi	Guilty	Akshaye Khanna	Ajay Bahl	Law is a tool!	116	2019	27.3	94	20.4	01JAN2019	2019s	GUILTY	1	section375-2019
17	Chekka Chivantha Vaanam	Tamil	Adjourned	Arvind Swami	Mani Ratnam	Family first?	117	2018	6.2	76	92.1	01JAN2018	2018s	ADJOURNED	1	chekkachivanthavaanam-2018
18	Talvar	Hindi	Guilty	Irrfan Khan	Meghna Gulzar	Truth has many versions	118	2015	12.7	90	30.0	01JAN2015	2015s	GUILTY	1	talvar-2015
19	Visaranai	Tamil	Guilty	Dinesh	Vetrimaaran	Systemic rot exposed	119	2015	16.5	96	6.0	01JAN2015	2015s	GUILTY	1	visaranai-2015
20	Kaappaan	Tamil	Acquittal	Suriya	K. V. Anand	Nation over self	120	2019	9.9	75	150.6	01JAN2019	2019s	ACQUITTAL	0	kaappaan-2019
21	Drishyam 2	Malayalam	Acquittal	Mohanlal	Jeethu Joseph	Family is everything	121	2021	18.2	88	58.0	01JAN2021	2021s	ACQUITTAL	0	drishyam2-2021
22	Manichitrathazhu	Malayalam	Adjourned	Suresh Gopi	Fazil	Split personalities at play	122	1993	11.0	84	23.5	01JAN1993	1993s	ADJOURNED	1	manichitrathazhu-1993
23	Nyaay: The Justice	Hindi	Guilty	Zuber K. Khan	Dilip Gulati	Justice or vendetta?	123	2021	21.4	50	0.9	01JAN2021	2021s	GUILTY	1	nyaaythejustice-2021
24	Thalaivi	Tamil	Acquittal	Kangana Ranaut	A. L. Vijay	History will remember	124	2021	13.6	79	30.7	01JAN2021	2021s	ACQUITTAL	0	thalaivi-2021
25	Singham	Hindi	Guilty	Ajay Devgn	Rohit Shetty	Aata majhi satakli!	125	2011	7.3	74	155.0	01JAN2011	2011s	GUILTY	1	singham-2011

3.Quick structural check – PROC CONTENTS

proc contents data=court_scenes_in_films position;

   title "Structure of Court Scene Dataset";

run;

Output:

Structure of Court Scene Dataset

The CONTENTS Procedure

Data Set Name	WORK.COURT_SCENES_IN_FILMS	Observations	25
Member Type	DATA	Variables	16
Engine	V9	Indexes	0
Created	14/09/2015 00:20:40	Observation Length	528
Last Modified	14/09/2015 00:20:40	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	65536
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	124
Obs in First Data Page	25
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD13972_DESKTOP-QFAA4KV_\court_scenes_in_films.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Alphabetic List of Variables and Attributes
#	Variable	Type	Len	Format	Label
11	boxoffice_cr	Num	8	8.1
12	dec_start	Num	8	DATE9.	Calendar start of release year
13	decade	Char	5
5	director	Char	30
10	drama_score	Num	8		Subjective Drama Intensity 0–100
6	famous_quote	Char	120
7	filmid	Num	8
1	filmname	Char	40
2	lang	Char	12
4	lead_lawyer	Char	30
15	outcome	Num	8		1=Conviction 0=Acquittal
8	rel_year	Num	8
9	scene_min	Num	8		Minutes of Courtroom Action
16	slug	Char	200
3	verdict	Char	15
14	verdict_up	Char	15

Variables in Creation Order
#	Variable	Type	Len	Format	Label
1	filmname	Char	40
2	lang	Char	12
3	verdict	Char	15
4	lead_lawyer	Char	30
5	director	Char	30
6	famous_quote	Char	120
7	filmid	Num	8
8	rel_year	Num	8
9	scene_min	Num	8		Minutes of Courtroom Action
10	drama_score	Num	8		Subjective Drama Intensity 0–100
11	boxoffice_cr	Num	8	8.1
12	dec_start	Num	8	DATE9.	Calendar start of release year
13	decade	Char	5
14	verdict_up	Char	15
15	outcome	Num	8		1=Conviction 0=Acquittal
16	slug	Char	200

4.Descriptive stats – PROC MEANS |PROC UNIVARIATE |PROC FREQ

proc means data=court_scenes_in_films n min q1 median mean q3 max maxdec=1;

   var scene_min drama_score boxoffice_cr;

   class decade;

   title "Minutes, Drama Scores, and Box Office by Decade";

run;

Output:

Minutes, Drama Scores, and Box Office by Decade

The MEANS Procedure

decade	N Obs	Variable	Label	N	Minimum	Lower Quartile	Median	Mean	Upper Quartile	Maximum
1985s	1	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	1 1 1	30.2 93.0 9.7	30.2 93.0 9.7	30.2 93.0 9.7	30.2 93.0 9.7	30.2 93.0 9.7	30.2 93.0 9.7
1993s	2	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	2 2 2	11.0 84.0 23.5	11.0 84.0 23.5	17.9 90.5 31.7	17.9 90.5 31.7	24.7 97.0 39.8	24.7 97.0 39.8
2004s	1	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	1 1 1	15.3 80.0 97.5	15.3 80.0 97.5	15.3 80.0 97.5	15.3 80.0 97.5	15.3 80.0 97.5	15.3 80.0 97.5
2005s	1	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	1 1 1	20.8 89.0 100.1	20.8 89.0 100.1	20.8 89.0 100.1	20.8 89.0 100.1	20.8 89.0 100.1	20.8 89.0 100.1
2011s	1	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	1 1 1	7.3 74.0 155.0	7.3 74.0 155.0	7.3 74.0 155.0	7.3 74.0 155.0	7.3 74.0 155.0	7.3 74.0 155.0
2012s	1	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	1 1 1	14.6 78.0 83.2	14.6 78.0 83.2	14.6 78.0 83.2	14.6 78.0 83.2	14.6 78.0 83.2	14.6 78.0 83.2
2013s	2	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	2 2 2	17.0 87.0 5.6	17.0 87.0 5.6	18.2 88.5 19.0	18.2 88.5 19.0	19.4 90.0 32.3	19.4 90.0 32.3
2014s	1	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	1 1 1	38.2 92.0 1.8	38.2 92.0 1.8	38.2 92.0 1.8	38.2 92.0 1.8	38.2 92.0 1.8	38.2 92.0 1.8
2015s	2	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	2 2 2	12.7 90.0 6.0	12.7 90.0 6.0	14.6 93.0 18.0	14.6 93.0 18.0	16.5 96.0 30.0	16.5 96.0 30.0
2016s	2	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	2 2 2	23.0 85.0 108.7	23.0 85.0 108.7	26.2 90.0 162.5	26.2 90.0 162.5	29.4 95.0 216.3	29.4 95.0 216.3
2017s	1	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	1 1 1	22.8 86.0 195.0	22.8 86.0 195.0	22.8 86.0 195.0	22.8 86.0 195.0	22.8 86.0 195.0	22.8 86.0 195.0
2018s	2	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	2 2 2	6.2 76.0 92.1	6.2 76.0 92.1	7.3 79.0 144.8	7.3 79.0 144.8	8.4 82.0 197.4	8.4 82.0 197.4
2019s	3	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	3 3 3	9.9 75.0 20.4	9.9 75.0 20.4	27.3 91.0 150.6	22.0 86.7 117.2	28.7 94.0 180.7	28.7 94.0 180.7
2020s	1	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	1 1 1	10.5 70.0 52.4	10.5 70.0 52.4	10.5 70.0 52.4	10.5 70.0 52.4	10.5 70.0 52.4	10.5 70.0 52.4
2021s	4	scene_min drama_score boxoffice_cr	Minutes of Courtroom Action Subjective Drama Intensity 0–100	4 4 4	13.6 50.0 0.9	15.9 64.5 15.8	19.8 83.5 44.4	19.7 76.3 56.7	23.5 88.0 97.7	25.5 88.0 137.3

proc univariate data=court_scenes_in_films noprint;

   var drama_score;

   histogram / midpoints=60 to 100 by 5;

   inset mean std / position=ne;

   title1 "Distribution of Drama Intensity Scores";

run;

Output:

WARNING: The MIDPOINTS= list was extended to accommodate the data.

NOTE: PROCEDURE UNIVARIATE used (Total process time):

      real time           3.40 seconds

      cpu time            0.67 seconds

proc freq data=court_scenes_in_films order=freq;

   tables verdict_up*lang / chisq norow nocol nopercent;

   title1 "Verdict by Language (Chi‑square)";

run;

Output:

Verdict by Language (Chi-square)

The FREQ Procedure

Frequency

Table of verdict_up by lang verdict_up lang Hindi Tamil Malayalam Marathi Telugu Total GUILTY 10 2 0 0 0 12 ADJOURNED 1 2 1 1 0 5 NOT GUILTY 3 1 0 0 1 5 ACQUITTAL 0 2 1 0 0 3 Total 14 7 2 1 1 25

Statistics for Table of verdict_up by lang

Statistic	DF	Value	Prob
Chi-Square	12	19.6429	0.0742
Likelihood Ratio Chi-Square	12	19.5778	0.0755
Mantel-Haenszel Chi-Square	1	4.2098	0.0402
Phi Coefficient		0.8864
Contingency Coefficient		0.6633
Cramer's V		0.5118
WARNING: 95% of the cells have expected counts less than 5. Chi-Square may not be a valid test.

Sample Size = 25

5.PROC SQL: Top‑N queries and derived views

proc sql feedback outobs=5;

   /* Top 5 longest courtroom segments */

   create table top5_length as

   select filmname, scene_min format=8.2, rel_year

   from   court_scenes_in_films

   order  by scene_min desc;

quit;

proc print;run;

Output:

Obs	filmname	scene_min	rel_year
1	Court	38.20	2014
2	Meri Jung	30.20	1985
3	Pink	29.40	2016
4	Nerkonda Paarvai	28.70	2019
5	Section 375	27.30	2019

proc sql; 

/* Average drama score by verdict */

   select verdict_up,

          mean(drama_score) as avg_drama label="Avg Drama Score" format=8.1

   from   court_scenes_in_films

   group  by verdict_up

   order  by avg_drama desc;

quit;

Output:

verdict_up	Avg Drama Score
NOT GUILTY	87.8
GUILTY	85.3
ADJOURNED	80.8
ACQUITTAL	80.7

6.Reusable Macro – Summarise by any grouping

%macro court_summary(group=);

   %let grp = %upcase(&group);       

   %put NOTE: Running COURT_SUMMARY grouped by &grp;

   proc means data=court_scenes_in_films n mean median min max maxdec=1;

      class &group;

      var scene_min drama_score boxoffice_cr;

      title2 "Numeric Summary by &grp";

   run;

   proc freq data=court_scenes_in_films;

      tables &group * verdict_up / nopercent norow nocol;

      title2 "Verdict Crosstab by &grp";

   run;

%mend;

%court_summary(group=lang)

Output:

Verdict Crosstab by LANG

The FREQ Procedure

Frequency

Table of lang by verdict_up lang verdict_up ACQUITTAL ADJOURNED GUILTY NOT GUILTY Total Hindi 0 1 10 3 14 Malayalam 1 1 0 0 2 Marathi 0 1 0 0 1 Tamil 2 2 2 1 7 Telugu 0 0 0 1 1 Total 3 5 12 5 25

%court_summary(group=director)

Output:

Verdict Crosstab by DIRECTOR

The FREQ Procedure

Frequency

Table of director by verdict_up director verdict_up ACQUITTAL ADJOURNED GUILTY NOT GUILTY Total A. L. Vijay 1 0 0 0 1 Ajay Bahl 0 0 1 0 1 Aniruddha Roy Chowdhury 0 0 0 1 1 Ashwath Marimuthu 0 1 0 0 1 Chaitanya Tamhane 0 1 0 0 1 Dilip Gulati 0 0 1 0 1 Fazil 0 1 0 0 1 H. Vinoth 0 0 0 1 1 Hansal Mehta 0 0 1 0 1 Jeethu Joseph 1 0 0 0 1 K. V. Anand 1 0 0 0 1 Mani Ratnam 0 1 0 0 1 Meghna Gulzar 0 1 1 0 2 Rajkumar Santoshi 0 0 1 0 1 Rohit Shetty 0 0 1 0 1 S. Shankar 0 0 1 0 1 Subhash Ghai 0 0 1 0 1 Subhash Kapoor 0 0 2 0 2 Tinu Suresh Desai 0 0 0 1 1 Umesh Shukla 0 0 1 0 1 Venu Sriram 0 0 0 1 1 Vetrimaaran 0 0 1 0 1 Yash Chopra 0 0 0 1 1 Total 3 5 12 5 25

7.PROC REPORT for publication‑ready table

proc summary data=court_scenes_in_films nway;

   class decade verdict_up;

   var scene_min drama_score;

   output out=summary_out(drop=_type_ rename=(_freq_=cnt))

      mean(scene_min)=mean_scene

      mean(drama_score)=mean_drama;

run;

proc report data=summary_out nowd;

   column decade verdict_up cnt mean_scene mean_drama;

   define decade / group;

   define verdict_up / group;

   define cnt / 'Count';

   define mean_scene / 'Avg Minutes';

   define mean_drama / 'Avg Drama';

   compute after;

      line @1 "Summary created on %sysfunc(date(), worddate.)";

   endcomp;

   title "Pre-Summarized Courtroom Report";

run;

Output:

Pre-Summarized Courtroom Report

decade	verdict_up	Count	Avg Minutes	Avg Drama
1985s	GUILTY	1	30.2	93
1993s	ADJOURNED	1	11	84
	GUILTY	1	24.7	97
2004s	NOT GUILTY	1	15.3	80
2005s	GUILTY	1	20.8	89
2011s	GUILTY	1	7.3	74
2012s	GUILTY	1	14.6	78
2013s	GUILTY	2	18.2	88.5
2014s	ADJOURNED	1	38.2	92
2015s	GUILTY	2	14.6	93
2016s	NOT GUILTY	2	26.2	90
2017s	GUILTY	1	22.8	86
2018s	ADJOURNED	2	7.3	79
2019s	ACQUITTAL	1	9.9	75
	GUILTY	1	27.3	94
	NOT GUILTY	1	28.7	91
2020s	ADJOURNED	1	10.5	70
2021s	ACQUITTAL	2	15.9	83.5
	GUILTY	1	21.4	50
	NOT GUILTY	1	25.5	88
Summary created on September 14, 2015

8.PROC TABULATE for multidimensional stats

proc tabulate data=court_scenes_in_films;

   class lang verdict_up;

   var drama_score boxoffice_cr;

   table lang,

         verdict_up*(drama_score*mean boxoffice_cr*mean);

   title "Mean Drama & Box‑Office by Language x Verdict Matrix";

run;

Output:

Mean Drama & Box-Office by Language x Verdict Matrix

	verdict_up
	ACQUITTAL		ADJOURNED		GUILTY		NOT GUILTY
	Subjective Drama Intensity 0–100	boxoffice_cr	Subjective Drama Intensity 0–100	boxoffice_cr	Subjective Drama Intensity 0–100	boxoffice_cr	Subjective Drama Intensity 0–100	boxoffice_cr
	Mean	Mean	Mean	Mean	Mean	Mean	Mean	Mean
lang	.	.	82.00	197.40	83.90	57.19	86.67	140.83
Hindi
Malayalam	88.00	58.00	84.00	23.50	.	.	.	.
Marathi	.	.	92.00	1.80	.	.	.	.
Tamil	77.00	90.65	73.00	72.25	92.50	53.05	91.00	180.70
Telugu	.	.	.	.	.	.	88.00	137.30

9.Exporting to CSV – PROC EXPORT

proc export data=court_scenes_in_films

            outfile="court_scenes.csv"

            dbms=csv replace;

   putnames=yes;

run;

Log:

NOTE: The file 'court_scenes.csv' is:

      Filename=C:\sas folder\SASFoundation\9.4\court_scenes.csv,

      RECFM=V,LRECL=32767,File Size (bytes)=0,

      Last Modified=29 June 2025 17:32:11,

      Create Time=29 June 2025 17:32:11

NOTE: 26 records were written to the file 'court_scenes.csv'.

      The minimum record length was 120.

      The maximum record length was 154.

NOTE: There were 25 observations read from the data set WORK.COURT_SCENES_IN_FILMS.

NOTE: DATA statement used (Total process time):

      real time           0.28 seconds

      cpu time            0.06 seconds

25 records created in court_scenes.csv from COURT_SCENES_IN_FILMS.

NOTE: "court_scenes.csv" file was successfully created.

NOTE: PROCEDURE EXPORT used (Total process time):

      real time           2.90 seconds

      cpu time            0.40 seconds

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