344.MOVIE INDUSTRY DATA ANALYSIS USING SAS | PROFITABILITY INSIGHTS | STATISTICAL EVALUATION | VISUAL ANALYTICS | PROC SQL | PROC MEANS | PROC CORR | PROC UNIVARIATE | PROC SGPLOT | MACROS | DATE FUNCTIONS

MOVIE INDUSTRY DATA ANALYSIS USING SAS | PROFITABILITY INSIGHTS | STATISTICAL EVALUATION | VISUAL ANALYTICS | PROC SQL | PROC MEANS | PROC CORR | PROC UNIVARIATE | PROC SGPLOT | MACROS | DATE FUNCTIONS

options nocenter;

1.MOVIE DATASET CREATION

data movies;

    length Movie_Name $25 Genre $15;

    format Release_Date date9.;

    input Movie_Name $ Genre $ Budget Revenue IMDb_Rating Release_Date :date9.;

    Year = year(Release_Date);

    datalines;

Inception SciFi 160 829 8.8 16JUL2010

Titanic Romance 200 2200 7.9 19DEC1997

Avatar SciFi 237 2920 7.8 18DEC2009

Joker Drama 55 1074 8.4 04OCT2019

Interstellar SciFi 165 677 8.6 07NOV2014

Gladiator Action 103 460 8.5 05MAY2000

Matrix SciFi 63 466 8.7 31MAR1999

Frozen Animation 150 1280 7.4 27NOV2013

Avengers Action 220 1519 8.0 04MAY2012

Parasite Thriller 11 258 8.6 30MAY2019

Dangal Sports 70 300 8.4 23DEC2016

RRR Action 72 160 8.0 25MAR2022

;

run;

proc print data=movies;

run;

OUTPUT:

Obs	Movie_Name	Genre	Release_Date	Budget	Revenue	IMDb_Rating	Year
1	Inception	SciFi	16JUL2010	160	829	8.8	2010
2	Titanic	Romance	19DEC1997	200	2200	7.9	1997
3	Avatar	SciFi	18DEC2009	237	2920	7.8	2009
4	Joker	Drama	04OCT2019	55	1074	8.4	2019
5	Interstellar	SciFi	07NOV2014	165	677	8.6	2014
6	Gladiator	Action	05MAY2000	103	460	8.5	2000
7	Matrix	SciFi	31MAR1999	63	466	8.7	1999
8	Frozen	Animation	27NOV2013	150	1280	7.4	2013
9	Avengers	Action	04MAY2012	220	1519	8.0	2012
10	Parasite	Thriller	30MAY2019	11	258	8.6	2019
11	Dangal	Sports	23DEC2016	70	300	8.4	2016
12	RRR	Action	25MAR2022	72	160	8.0	2022

2.PROFIT CALCULATION USING PROC SQL

proc sql;

    create table movie_profit as

    select *,

           (Revenue - Budget) as Profit format=comma10.

    from movies;

quit;

proc print data=movie_profit;

run;

OUTPUT:

Obs	Movie_Name	Genre	Release_Date	Budget	Revenue	IMDb_Rating	Year	Profit
1	Inception	SciFi	16JUL2010	160	829	8.8	2010	669
2	Titanic	Romance	19DEC1997	200	2200	7.9	1997	2,000
3	Avatar	SciFi	18DEC2009	237	2920	7.8	2009	2,683
4	Joker	Drama	04OCT2019	55	1074	8.4	2019	1,019
5	Interstellar	SciFi	07NOV2014	165	677	8.6	2014	512
6	Gladiator	Action	05MAY2000	103	460	8.5	2000	357
7	Matrix	SciFi	31MAR1999	63	466	8.7	1999	403
8	Frozen	Animation	27NOV2013	150	1280	7.4	2013	1,130
9	Avengers	Action	04MAY2012	220	1519	8.0	2012	1,299
10	Parasite	Thriller	30MAY2019	11	258	8.6	2019	247
11	Dangal	Sports	23DEC2016	70	300	8.4	2016	230
12	RRR	Action	25MAR2022	72	160	8.0	2022	88

3.DATE ANALYSIS USING INTNX AND INTCK

data movie_dates;

    set movie_profit;

    Years_Since_Release = intck('year', Release_Date, today());

    Next_Anniversary = intnx('year', Release_Date, Years_Since_Release + 1, 'same');

    format Next_Anniversary date9.;

run;

proc print data=movie_dates;

run;

OUTPUT:

Obs	Movie_Name	Genre	Release_Date	Budget	Revenue	IMDb_Rating	Year	Profit	Years_Since_Release	Next_Anniversary
1	Inception	SciFi	16JUL2010	160	829	8.8	2010	669	15	16JUL2026
2	Titanic	Romance	19DEC1997	200	2200	7.9	1997	2,000	28	19DEC2026
3	Avatar	SciFi	18DEC2009	237	2920	7.8	2009	2,683	16	18DEC2026
4	Joker	Drama	04OCT2019	55	1074	8.4	2019	1,019	6	04OCT2026
5	Interstellar	SciFi	07NOV2014	165	677	8.6	2014	512	11	07NOV2026
6	Gladiator	Action	05MAY2000	103	460	8.5	2000	357	25	05MAY2026
7	Matrix	SciFi	31MAR1999	63	466	8.7	1999	403	26	31MAR2026
8	Frozen	Animation	27NOV2013	150	1280	7.4	2013	1,130	12	27NOV2026
9	Avengers	Action	04MAY2012	220	1519	8.0	2012	1,299	13	04MAY2026
10	Parasite	Thriller	30MAY2019	11	258	8.6	2019	247	6	30MAY2026
11	Dangal	Sports	23DEC2016	70	300	8.4	2016	230	9	23DEC2026
12	RRR	Action	25MAR2022	72	160	8.0	2022	88	3	25MAR2026

4.MACRO FOR PROFIT CLASSIFICATION

%macro profit_flag(ds=, out=);

data &out;

    set &ds;

    length Profit_Category $15;

    if Profit >= 500 then Profit_Category = 'BLOCKBUSTER';

    else if Profit >= 100 then Profit_Category = 'HIT';

    else if Profit > 0 then Profit_Category = 'AVERAGE';

    else Profit_Category = 'FLOP';

run;

proc print data=&out;

run;

%mend;

%profit_flag(ds=movie_profit, out=movie_analysis);

OUTPUT:

Obs	Movie_Name	Genre	Release_Date	Budget	Revenue	IMDb_Rating	Year	Profit	Profit_Category
1	Inception	SciFi	16JUL2010	160	829	8.8	2010	669	BLOCKBUSTER
2	Titanic	Romance	19DEC1997	200	2200	7.9	1997	2,000	BLOCKBUSTER
3	Avatar	SciFi	18DEC2009	237	2920	7.8	2009	2,683	BLOCKBUSTER
4	Joker	Drama	04OCT2019	55	1074	8.4	2019	1,019	BLOCKBUSTER
5	Interstellar	SciFi	07NOV2014	165	677	8.6	2014	512	BLOCKBUSTER
6	Gladiator	Action	05MAY2000	103	460	8.5	2000	357	HIT
7	Matrix	SciFi	31MAR1999	63	466	8.7	1999	403	HIT
8	Frozen	Animation	27NOV2013	150	1280	7.4	2013	1,130	BLOCKBUSTER
9	Avengers	Action	04MAY2012	220	1519	8.0	2012	1,299	BLOCKBUSTER
10	Parasite	Thriller	30MAY2019	11	258	8.6	2019	247	HIT
11	Dangal	Sports	23DEC2016	70	300	8.4	2016	230	HIT
12	RRR	Action	25MAR2022	72	160	8.0	2022	88	AVERAGE

5.DESCRIPTIVE STATISTICS USING PROC MEANS

proc means data=movie_analysis mean min max;

    var Budget Revenue Profit IMDb_Rating;

    title "SUMMARY STATISTICS OF MOVIE FINANCIALS";

run;

OUTPUT:

SUMMARY STATISTICS OF MOVIE FINANCIALS

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Budget Revenue Profit IMDb_Rating	125.5000000 1011.92 886.4166667 8.2583333	11.0000000 160.0000000 88.0000000 7.4000000	237.0000000 2920.00 2683.00 8.8000000

6.CORRELATION ANALYSIS USING PROC CORR

proc corr data=movie_analysis;

    var Budget Revenue IMDb_Rating Profit;

    title "CORRELATION ANALYSIS OF MOVIE VARIABLES";

run;

OUTPUT:

CORRELATION ANALYSIS OF MOVIE VARIABLES

The CORR Procedure

4 Variables:	Budget Revenue IMDb_Rating Profit

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Budget	12	125.50000	73.04980	1506	11.00000	237.00000
Revenue	12	1012	849.30040	12143	160.00000	2920
IMDb_Rating	12	8.25833	0.42950	99.10000	7.40000	8.80000
Profit	12	886.41667	791.63541	10637	88.00000	2683

Pearson Correlation Coefficients, N = 12 Prob > |r| under H0: Rho=0
	Budget	Revenue	IMDb_Rating	Profit
Budget	1.00000	0.80560 0.0016	-0.48461 0.1103	0.77201 0.0033
Revenue	0.80560 0.0016	1.00000	-0.59288 0.0422	0.99850 <.0001
IMDb_Rating	-0.48461 0.1103	-0.59288 0.0422	1.00000	-0.59135 0.0429
Profit	0.77201 0.0033	0.99850 <.0001	-0.59135 0.0429	1.00000

7.DISTRIBUTION ANALYSIS USING PROC UNIVARIATE

proc univariate data=movie_analysis;

    var Profit;

    histogram Profit;

    inset mean median std skewness;

    title "PROFIT DISTRIBUTION ANALYSIS";

run;

OUTPUT:

PROFIT DISTRIBUTION ANALYSIS

The UNIVARIATE Procedure

Variable: Profit

Moments
N	12	Sum Weights	12
Mean	886.416667	Sum Observations	10637
Std Deviation	791.635414	Variance	626686.629
Skewness	1.2950494	Kurtosis	1.14365626
Uncorrected SS	16322367	Corrected SS	6893552.92
Coeff Variation	89.3073702	Std Error Mean	228.52546

Basic Statistical Measures
Location		Variability
Mean	886.4167	Std Deviation	791.63541
Median	590.5000	Variance	626687
Mode	.	Range	2595
		Interquartile Range	912.50000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	3.878853	Pr > |t|	0.0026
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	2683.0
99%	2683.0
95%	2683.0
90%	2000.0
75% Q3	1214.5
50% Median	590.5
25% Q1	302.0
10%	230.0
5%	88.0
1%	88.0
0% Min	88.0

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
88	12	1019	4
230	11	1130	8
247	10	1299	9
357	6	2000	2
403	7	2683	3

PROFIT DISTRIBUTION ANALYSIS

The UNIVARIATE Procedure

8.DATA VISUALIZATION USING PROC SGPLOT

A. Revenue vs Budget

proc sgplot data=movie_analysis;

    scatter x=Budget y=Revenue / datalabel=Movie_Name;

    title "BUDGET VS REVENUE SCATTER PLOT";

run;

OUTPUT:

B. Profit by Genre

proc sgplot data=movie_analysis;

    vbar Genre / response=Profit stat=mean;

    title "AVERAGE PROFIT BY GENRE";

run;

OUTPUT:

9.PROC FREQ

Genre Distribution

proc freq data=movie_analysis;

    tables Genre*Profit_Category;

    title "GENRE AND PROFIT CATEGORY DISTRIBUTION";

run;

OUTPUT:

GENRE AND PROFIT CATEGORY DISTRIBUTION

The FREQ Procedure

Frequency Percent Row Pct Col Pct

Table of Genre by Profit_Category Genre Profit_Category AVERAGE BLOCKBUSTER HIT Total Action 1 8.33 33.33 100.00 1 8.33 33.33 14.29 1 8.33 33.33 25.00 3 25.00     Animation 0 0.00 0.00 0.00 1 8.33 100.00 14.29 0 0.00 0.00 0.00 1 8.33     Drama 0 0.00 0.00 0.00 1 8.33 100.00 14.29 0 0.00 0.00 0.00 1 8.33     Romance 0 0.00 0.00 0.00 1 8.33 100.00 14.29 0 0.00 0.00 0.00 1 8.33     SciFi 0 0.00 0.00 0.00 3 25.00 75.00 42.86 1 8.33 25.00 25.00 4 33.33     Sports 0 0.00 0.00 0.00 0 0.00 0.00 0.00 1 8.33 100.00 25.00 1 8.33     Thriller 0 0.00 0.00 0.00 0 0.00 0.00 0.00 1 8.33 100.00 25.00 1 8.33     Total 1 8.33 7 58.33 4 33.33 12 100.00

10.PROC: PROC RANK

Ranking Movies by Profit

proc rank data=movie_analysis out=movie_rank descending;

    var Profit;

    ranks Profit_Rank;

run;

proc print data=movie_rank;

run;

OUTPUT:

Obs	Movie_Name	Genre	Release_Date	Budget	Revenue	IMDb_Rating	Year	Profit	Profit_Category	Profit_Rank
1	Inception	SciFi	16JUL2010	160	829	8.8	2010	669	BLOCKBUSTER	6
2	Titanic	Romance	19DEC1997	200	2200	7.9	1997	2,000	BLOCKBUSTER	2
3	Avatar	SciFi	18DEC2009	237	2920	7.8	2009	2,683	BLOCKBUSTER	1
4	Joker	Drama	04OCT2019	55	1074	8.4	2019	1,019	BLOCKBUSTER	5
5	Interstellar	SciFi	07NOV2014	165	677	8.6	2014	512	BLOCKBUSTER	7
6	Gladiator	Action	05MAY2000	103	460	8.5	2000	357	HIT	9
7	Matrix	SciFi	31MAR1999	63	466	8.7	1999	403	HIT	8
8	Frozen	Animation	27NOV2013	150	1280	7.4	2013	1,130	BLOCKBUSTER	4
9	Avengers	Action	04MAY2012	220	1519	8.0	2012	1,299	BLOCKBUSTER	3
10	Parasite	Thriller	30MAY2019	11	258	8.6	2019	247	HIT	10
11	Dangal	Sports	23DEC2016	70	300	8.4	2016	230	HIT	11
12	RRR	Action	25MAR2022	72	160	8.0	2022	88	AVERAGE	12

YESTERDAY INVALID CODE ANSWER

10.PIE Chart

proc sgplot data=olympic_efficiency;

    pie Efficiency_Level / datalabel;

run;

/* Note: In practice above there is an Invalid in this code Find it,Correct it and Use it /*

CORRECT ANSWER

proc gchart data=olympic_efficiency;

    pie Efficiency_Level;

run;

quit;

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