271.GANESH MANDAPS IN INDIA: DATA VISUALIZATION AND ANALYSIS VIA PROC SQL | PROC SGPLOT | PROC MEANS | PROC PRINT

GANESH MANDAPS IN INDIA: DATA VISUALIZATION AND ANALYSIS VIA PROC SQL | PROC SGPLOT | PROC MEANS | PROC PRINT

 /*Creating A Dataset Of Ganesh Mandap In India*/

Step 1: create Ganesh Mandap dataset 

options nocenter;

data ganesh_mandap;

  length MandapType $15 Location $15 Material $15 Weather $10;

  input MandapType $ Location $ Material $ Visitors Duration Cost Volunteers 

        Satisfaction EnvironmentalImpact SocialMedia Weather $;

  datalines;

Traditional Pune Clay 5000 10 12000 50 8 3 450 Sunny

Ecofriendly Guwahati Bamboo 3500 7 8000 30 9 5 320 Rainy

Marble Chennai Marble 4500 9 25000 40 7 2 410 Sunny

Terracotta Kolkata Terracotta 3000 8 6000 25 8 4 180 Cloudy

Wooden Bengaluru Wood 4200 10 15000 45 9 3 375 Sunny

Glass Delhi Glass 6000 5 30000 60 6 1 900 Rainy

Papermache Ahmedabad Paper 2500 6 5000 20 7 5 150 Sunny

Sand Goa Sand 2700 4 4000 15 8 5 230 Sunny

Flower Kerala Flowers 3200 11 7000 35 10 2 260 Cloudy

Metal Jaipur Metal 2900 7 10000 28 7 3 190 Sunny

Fusion Hyderabad Glass/Wood 4800 8 18000 38 9 3 550 Sunny

;

run;

proc print data=ganesh_mandap;

  title "Ganesh Mandap Data Overview";

run;

Output:

Ganesh Mandap Data Overview

Obs	MandapType	Location	Material	Weather	Visitors	Duration	Cost	Volunteers	Satisfaction	EnvironmentalImpact	SocialMedia
1	Traditional	Pune	Clay	Sunny	5000	10	12000	50	8	3	450
2	Ecofriendly	Guwahati	Bamboo	Rainy	3500	7	8000	30	9	5	320
3	Marble	Chennai	Marble	Sunny	4500	9	25000	40	7	2	410
4	Terracotta	Kolkata	Terracotta	Cloudy	3000	8	6000	25	8	4	180
5	Wooden	Bengaluru	Wood	Sunny	4200	10	15000	45	9	3	375
6	Glass	Delhi	Glass	Rainy	6000	5	30000	60	6	1	900
7	Papermache	Ahmedabad	Paper	Sunny	2500	6	5000	20	7	5	150
8	Sand	Goa	Sand	Sunny	2700	4	4000	15	8	5	230
9	Flower	Kerala	Flowers	Cloudy	3200	11	7000	35	10	2	260
10	Metal	Jaipur	Metal	Sunny	2900	7	10000	28	7	3	190
11	Fusion	Hyderabad	Glass/Wood	Sunny	4800	8	18000	38	9	3	550

Step 2: Frequency count of Mandap Types 

proc freq data=ganesh_mandap;

  tables MandapType / nocum nopercent;

  title "Frequency of Different Mandap Types";

run;

Output:

Frequency of Different Mandap Types

The FREQ Procedure

MandapType	Frequency
Ecofriendly	1
Flower	1
Fusion	1
Glass	1
Marble	1
Metal	1
Papermache	1
Sand	1
Terracotta	1
Traditional	1
Wooden	1

Step 3: Descriptive statistics for Visitors & Cost 

proc means data=ganesh_mandap mean median min max std;

  var Visitors Cost;

  title "Visitor Count & Decoration Cost Statistics";

run;

Output:

Visitor Count & Decoration Cost Statistics

The MEANS Procedure

Variable	Mean	Median	Minimum	Maximum	Std Dev
Visitors Cost	3845.45 12727.27	3500.00 10000.00	2500.00 4000.00	6000.00 30000.00	1127.27 8521.63

Step 4: Calculate average Satisfaction Score by Mandap Type using PROC SQL 

proc sql;

  title "Average Satisfaction Score by Mandap Type";

  select MandapType, avg(Satisfaction) as AvgSatisfaction

  from ganesh_mandap

  group by MandapType

  order by AvgSatisfaction desc;

quit;

Output:

Average Satisfaction Score by Mandap Type

MandapType	AvgSatisfaction
Flower	10
Fusion	9
Wooden	9
Ecofriendly	9
Traditional	8
Terracotta	8
Sand	8
Papermache	7
Metal	7
Marble	7
Glass	6

Step 5: Sort data by Visitors descending to find most popular Mandaps 

proc sort data=ganesh_mandap out=sorted_visitors;

  by descending Visitors;

run;

proc print data=sorted_visitors (obs=5);

  title "Top 5 Mandaps by Visitor Count";

run;

Output:

Top 5 Mandaps by Visitor Count

Obs	MandapType	Location	Material	Weather	Visitors	Duration	Cost	Volunteers	Satisfaction	EnvironmentalImpact	SocialMedia
1	Glass	Delhi	Glass	Rainy	6000	5	30000	60	6	1	900
2	Traditional	Pune	Clay	Sunny	5000	10	12000	50	8	3	450
3	Fusion	Hyderabad	Glass/Wood	Sunny	4800	8	18000	38	9	3	550
4	Marble	Chennai	Marble	Sunny	4500	9	25000	40	7	2	410
5	Wooden	Bengaluru	Wood	Sunny	4200	10	15000	45	9	3	375

Step 6: Creating Macro to dynamically generate summary report by any numeric variable 

%macro summary_report(varlist);

  %do i=1 %to %sysfunc(countw(&varlist));

    %let var=%scan(&varlist, &i);

    proc means data=ganesh_mandap mean median std min max;

      var &var;

      title "Summary Statistics for &var";

    run;

  %end;

%mend;

%summary_report(Visitors Cost Duration Volunteers);

Output:

Summary Statistics for Visitors

The MEANS Procedure

Analysis Variable : Visitors
Mean	Median	Std Dev	Minimum	Maximum
3845.45	3500.00	1127.27	2500.00	6000.00

Summary Statistics for Cost

The MEANS Procedure

Analysis Variable : Cost
Mean	Median	Std Dev	Minimum	Maximum
12727.27	10000.00	8521.63	4000.00	30000.00

Summary Statistics for Duration

The MEANS Procedure

Analysis Variable : Duration
Mean	Median	Std Dev	Minimum	Maximum
7.7272727	8.0000000	2.1950357	4.0000000	11.0000000

Summary Statistics for Volunteers

The MEANS Procedure

Analysis Variable : Volunteers
Mean	Median	Std Dev	Minimum	Maximum
35.0909091	35.0000000	13.3525619	15.0000000	60.0000000

Step 7: Using PROC FORMAT to categorize Environmental Impact 

proc format;

  value envfmt

    1 = 'Low Impact'

    2 = 'Low Impact'

    3 = 'Moderate Impact'

    4 = 'High Impact'

    5 = 'Very High Impact';

run;

proc freq data=ganesh_mandap;

  tables EnvironmentalImpact / norow nocol;

  format EnvironmentalImpact envfmt.;

  title "Environmental Impact Categorization";

run;

Output:

Environmental Impact Categorization

The FREQ Procedure

EnvironmentalImpact	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Low Impact	3	27.27	3	27.27
Moderate Impact	4	36.36	7	63.64
High Impact	1	9.09	8	72.73
Very High Impact	3	27.27	11	100.00

Step 8: Transpose dataset to see Visitors by Location 

proc sort data=ganesh_mandap;

 by MandapType;

run;

proc transpose data=ganesh_mandap out=visitors_by_location(drop=_Name_) prefix=Visitors_;

  by MandapType;

  id Location;

  var Visitors;

run;

proc print data=visitors_by_location;

  title "Visitors Transposed by Mandap Type and Location";

run;

Output:

Visitors Transposed by Mandap Type and Location

Obs	MandapType	Visitors_Guwahati	Visitors_Kerala	Visitors_Hyderabad	Visitors_Delhi	Visitors_Chennai	Visitors_Jaipur	Visitors_Ahmedabad	Visitors_Goa	Visitors_Kolkata	Visitors_Pune	Visitors_Bengaluru
1	Ecofriendly	3500	.	.	.	.	.	.	.	.	.	.
2	Flower	.	3200	.	.	.	.	.	.	.	.	.
3	Fusion	.	.	4800	.	.	.	.	.	.	.	.
4	Glass	.	.	.	6000	.	.	.	.	.	.	.
5	Marble	.	.	.	.	4500	.	.	.	.	.	.
6	Metal	.	.	.	.	.	2900	.	.	.	.	.
7	Papermache	.	.	.	.	.	.	2500	.	.	.	.
8	Sand	.	.	.	.	.	.	.	2700	.	.	.
9	Terracotta	.	.	.	.	.	.	.	.	3000	.	.
10	Traditional	.	.	.	.	.	.	.	.	.	5000	.
11	Wooden	.	.	.	.	.	.	.	.	.	.	4200

Step 9: Report combining multiple variables with PROC REPORT 

proc report data=ganesh_mandap nowd;

  columns MandapType Location Visitors Cost Satisfaction EnvironmentalImpact;

  define MandapType / group;

  define Location / group;

  define Visitors / analysis sum;

  define Cost / analysis mean format=dollar12.;

  define Satisfaction / analysis mean;

  define EnvironmentalImpact / format=envfmt.;

  title "Ganesh Mandap Report: Visitor & Cost Summary";

run;

Output:

Ganesh Mandap Report: Visitor & Cost Summary

MandapType	Location	Visitors	Cost	Satisfaction	EnvironmentalImpact
Ecofriendly	Guwahati	3500	$8,000	9	Very High Impact
Flower	Kerala	3200	$7,000	10	Low Impact
Fusion	Hyderabad	4800	$18,000	9	Moderate Impact
Glass	Delhi	6000	$30,000	6	Low Impact
Marble	Chennai	4500	$25,000	7	Low Impact
Metal	Jaipur	2900	$10,000	7	Moderate Impact
Papermache	Ahmedabad	2500	$5,000	7	Very High Impact
Sand	Goa	2700	$4,000	8	Very High Impact
Terracotta	Kolkata	3000	$6,000	8	High Impact
Traditional	Pune	5000	$12,000	8	Moderate Impact
Wooden	Bengaluru	4200	$15,000	9	Moderate Impact

Step 10: Visualizing Visitor Count across Mandap Types with PROC SGPLOT 

proc sgplot data=ganesh_mandap;

  vbar MandapType / response=Visitors datalabel;

  title "Visitor Counts by Mandap Type";

run;

Output:

Step 11: Advanced PROC SQL Join - If we had a second dataset with volunteer details 

data volunteers;

  input Location $ Volunteers Available_Hours;

  datalines;

Pune 50 340

Guwahati 30 180

Chennai 40 320

Kolkata 25 120

Bengaluru 45 360

Delhi 60 400

Ahmedabad 20 150

Goa 15 90

Kerala 35 280

Jaipur 28 210

Hyderabad 65 450

;

run;

proc print;run;

Output:

Obs	Location	Volunteers	Available_Hours
1	Pune	50	340
2	Guwahati	30	180
3	Chennai	40	320
4	Kolkata	25	120
5	Bengalur	45	360
6	Delhi	60	400
7	Ahmedaba	20	150
8	Goa	15	90
9	Kerala	35	280
10	Jaipur	28	210
11	Hyderaba	65	450

proc sql;

  title "Join Ganesh Mandap with Volunteers Data for Analysis";

  select a.Location, a.MandapType, a.Visitors, b.Volunteers, b.Available_Hours

  from ganesh_mandap as a

  inner join volunteers as b

  on a.Location = b.Location

  order by a.Visitors desc;

quit;

Output:

Join Ganesh Mandap with Volunteers Data for Analysis

Location	MandapType	Visitors	Volunteers	Available_Hours
Delhi	Glass	6000	60	400
Pune	Traditional	5000	50	340
Chennai	Marble	4500	40	320
Guwahati	Ecofriendly	3500	30	180
Kerala	Flower	3200	35	280
Kolkata	Terracotta	3000	25	120
Jaipur	Metal	2900	28	210
Goa	Sand	2700	15	90

Step 12: Macro to filter mandaps with visitor count above a threshold 

%macro filter_visitors(threshold=);

  proc print data=ganesh_mandap;

    where Visitors > &threshold;

    title "Mandaps with Visitors Greater than &threshold";

  run;

%mend;

%filter_visitors(threshold=4000);

Output:

Mandaps with Visitors Greater than 4000

Obs	MandapType	Location	Material	Weather	Visitors	Duration	Cost	Volunteers	Satisfaction	EnvironmentalImpact	SocialMedia
3	Fusion	Hyderabad	Glass/Wood	Sunny	4800	8	18000	38	9	3	550
4	Glass	Delhi	Glass	Rainy	6000	5	30000	60	6	1	900
5	Marble	Chennai	Marble	Sunny	4500	9	25000	40	7	2	410
10	Traditional	Pune	Clay	Sunny	5000	10	12000	50	8	3	450
11	Wooden	Bengaluru	Wood	Sunny	4200	10	15000	45	9	3	375

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