Which namkeen products sell the most, and what does SAS data creation reveal about customer taste?A Sas Study

option nocenter;

1: Creating the Base Datasets

/*Macro 1: Data Simulation*/

%macro namkeen_simulate;

/* === NORTH REGION NAMKEEN DATA === */

filename north temp;

data _null_;

file north;

put "101 Haldiram Bhujia 45 200 4.7";

put "102 Bikaji AlooBhujia 40 180 4.5";

put "103 Bikanervala Navratan 55 250 4.6";

put "104 PepsiCo LaysMasala 20 100 4.2";

put "105 Balaji RatlamiSev 35 150 4.4";

put "106 Pratap Gathiya 30 200 4.1";

run;

data work.namkeen_north;

infile north;

input SnackID Brand :$20. Type :$20. Price Weight_g Rating;

run;

proc print data=work.namkeen_north noobs;

title "NAMKEEN PRODUCTS - NORTH REGION";

run;

/* === SOUTH REGION NAMKEEN DATA === */

filename south temp;

data _null_;

file south;

put "107 Aachi Mixture 50 220 4.3";

put "108 GRB KaraBoondi 45 200 4.5";

put "109 MTR Chakli 60 250 4.6";

put "110 Maiyas Nippattu 55 230 4.2";

put "111 Lays ClassicSalt 20 100 4.1";

put "112 Haldiram MoongDal 40 150 4.7";

run;

data work.namkeen_south;

infile south;

input SnackID Brand :$20. Type :$20. Price Weight_g Rating;

run;

proc print data=work.namkeen_south noobs;

title "NAMKEEN PRODUCTS - SOUTH REGION";

run;

%mend namkeen_simulate;

%namkeen_simulate;

OUTPUT:

NAMKEEN PRODUCTS - NORTH REGION


SnackID	Brand	Type	Price	Weight_g	Rating
101	Haldiram	Bhujia	45	200	4.7
102	Bikaji	AlooBhujia	40	180	4.5
103	Bikanervala	Navratan	55	250	4.6
104	PepsiCo	LaysMasala	20	100	4.2
105	Balaji	RatlamiSev	35	150	4.4
106	Pratap	Gathiya	30	200	4.1

NAMKEEN PRODUCTS - SOUTH REGION


SnackID	Brand	Type	Price	Weight_g	Rating
107	Aachi	Mixture	50	220	4.3
108	GRB	KaraBoondi	45	200	4.5
109	MTR	Chakli	60	250	4.6
110	Maiyas	Nippattu	55	230	4.2
111	Lays	ClassicSalt	20	100	4.1
112	Haldiram	MoongDal	40	150	4.7

2: Combining Datasets

/*2.1 Using SET Statement*/

data all_namkeen_set;

set namkeen_north namkeen_south;

run;

proc print data=all_namkeen_set;

title "NAMKEEN DATA COMBINED USING SET STATEMENT";

run;

OUTPUT:

NAMKEEN DATA COMBINED USING SET STATEMENT


Obs	SnackID	Brand	Type	Price	Weight_g	Rating
1	101	Haldiram	Bhujia	45	200	4.7
2	102	Bikaji	AlooBhujia	40	180	4.5
3	103	Bikanervala	Navratan	55	250	4.6
4	104	PepsiCo	LaysMasala	20	100	4.2
5	105	Balaji	RatlamiSev	35	150	4.4
6	106	Pratap	Gathiya	30	200	4.1
7	107	Aachi	Mixture	50	220	4.3
8	108	GRB	KaraBoondi	45	200	4.5
9	109	MTR	Chakli	60	250	4.6
10	110	Maiyas	Nippattu	55	230	4.2
11	111	Lays	ClassicSalt	20	100	4.1
12	112	Haldiram	MoongDal	40	150	4.7

/*2.2 Using MERGE Statement*/

Before merging, we’ll create two supporting datasets:

namkeen_price → contains price & weight

namkeen_rating → contains rating

data namkeen_price;

set all_namkeen_set(keep=SnackID Brand Type Price Weight_g);

run;

proc print data=namkeen_price;

run;

OUTPUT:


Obs	SnackID	Brand	Type	Price	Weight_g
1	101	Haldiram	Bhujia	45	200
2	102	Bikaji	AlooBhujia	40	180
3	103	Bikanervala	Navratan	55	250
4	104	PepsiCo	LaysMasala	20	100
5	105	Balaji	RatlamiSev	35	150
6	106	Pratap	Gathiya	30	200
7	107	Aachi	Mixture	50	220
8	108	GRB	KaraBoondi	45	200
9	109	MTR	Chakli	60	250
10	110	Maiyas	Nippattu	55	230
11	111	Lays	ClassicSalt	20	100
12	112	Haldiram	MoongDal	40	150

data namkeen_rating;

set all_namkeen_set(keep=SnackID Rating);

run;

proc print data=namkeen_rating;

run;

OUTPUT:


Obs	SnackID	Rating
1	101	4.7
2	102	4.5
3	103	4.6
4	104	4.2
5	105	4.4
6	106	4.1
7	107	4.3
8	108	4.5
9	109	4.6
10	110	4.2
11	111	4.1
12	112	4.7

proc sort data=namkeen_price Out=namkeen_price_sorted;

by SnackID;

run;

proc print data=namkeen_price_sorted;

run;

OUTPUT:


Obs	SnackID	Brand	Type	Price	Weight_g
1	101	Haldiram	Bhujia	45	200
2	102	Bikaji	AlooBhujia	40	180
3	103	Bikanervala	Navratan	55	250
4	104	PepsiCo	LaysMasala	20	100
5	105	Balaji	RatlamiSev	35	150
6	106	Pratap	Gathiya	30	200
7	107	Aachi	Mixture	50	220
8	108	GRB	KaraBoondi	45	200
9	109	MTR	Chakli	60	250
10	110	Maiyas	Nippattu	55	230
11	111	Lays	ClassicSalt	20	100
12	112	Haldiram	MoongDal	40	150

proc sort data=namkeen_rating Out=namkeen_rating_sorted;

by SnackID;

run;

proc print data=namkeen_rating_sorted;

run;

OUTPUT:


Obs	SnackID	Rating
1	101	4.7
2	102	4.5
3	103	4.6
4	104	4.2
5	105	4.4
6	106	4.1
7	107	4.3
8	108	4.5
9	109	4.6
10	110	4.2
11	111	4.1
12	112	4.7

data merged_namkeen;

merge namkeen_price_sorted(in=a) namkeen_rating_sorted(in=b);

by SnackID;

if a and b;

run;

proc print data=merged_namkeen;

title "NAMKEEN DATA COMBINED USING MERGE STATEMENT";

run;

OUTPUT:

NAMKEEN DATA COMBINED USING MERGE STATEMENT


Obs	SnackID	Brand	Type	Price	Weight_g	Rating
1	101	Haldiram	Bhujia	45	200	4.7
2	102	Bikaji	AlooBhujia	40	180	4.5
3	103	Bikanervala	Navratan	55	250	4.6
4	104	PepsiCo	LaysMasala	20	100	4.2
5	105	Balaji	RatlamiSev	35	150	4.4
6	106	Pratap	Gathiya	30	200	4.1
7	107	Aachi	Mixture	50	220	4.3
8	108	GRB	KaraBoondi	45	200	4.5
9	109	MTR	Chakli	60	250	4.6
10	110	Maiyas	Nippattu	55	230	4.2
11	111	Lays	ClassicSalt	20	100	4.1
12	112	Haldiram	MoongDal	40	150	4.7

/*2.3 Using PROC SQL Join*/

proc sql;

create table namkeen_sql as

select a.SnackID, a.Brand, a.Type, a.Price, a.Weight_g, b.Rating

from namkeen_price a

inner join namkeen_rating b

on a.SnackID = b.SnackID;

quit;

proc print data=namkeen_sql;

title "NAMKEEN DATA COMBINED USING PROC SQL JOIN";

run;

OUTPUT:

NAMKEEN DATA COMBINED USING PROC SQL JOIN


Obs	SnackID	Brand	Type	Price	Weight_g	Rating
1	101	Haldiram	Bhujia	45	200	4.7
2	102	Bikaji	AlooBhujia	40	180	4.5
3	103	Bikanervala	Navratan	55	250	4.6
4	104	PepsiCo	LaysMasala	20	100	4.2
5	105	Balaji	RatlamiSev	35	150	4.4
6	106	Pratap	Gathiya	30	200	4.1
7	107	Aachi	Mixture	50	220	4.3
8	108	GRB	KaraBoondi	45	200	4.5
9	109	MTR	Chakli	60	250	4.6
10	110	Maiyas	Nippattu	55	230	4.2
11	111	Lays	ClassicSalt	20	100	4.1
12	112	Haldiram	MoongDal	40	150	4.7

3: QC Check – PROC COMPARE

proc compare base=merged_namkeen compare=namkeen_sql briefsummary;

title "QC CHECK: COMPARISON BETWEEN MERGE AND PROC SQL OUTPUTS";

run;

OUTPUT:

QC CHECK: COMPARISON BETWEEN MERGE AND PROC SQL OUTPUTS

The COMPARE Procedure                                                                                                               
Comparison of WORK.MERGED_NAMKEEN with WORK.NAMKEEN_SQL                                                                             
(Method=EXACT)                                                                                                                      
                                                                                                                                    
NOTE: No unequal values were found. All values compared are exactly equal.

4: TLF-style Summary Tables

/*4.1 Listing: PROC REPORT*/

proc report data=all_namkeen_set nowd headline headskip;

columns SnackID Brand Type Price Weight_g Rating;

define SnackID / order "Snack ID";

define Brand / display "Brand Name";

define Type / display "Namkeen Type";

define Price / display "Price (₹)";

define Weight_g / display "Weight (g)";

define Rating / display "Customer Rating";

title "LISTING REPORT OF NAMKEEN PRODUCTS IN INDIA";

run;

OUTPUT:

LISTING REPORT OF NAMKEEN PRODUCTS IN INDIA


Snack ID	Brand Name	Namkeen Type	Price (₹)	Weight (g)	Customer Rating
101	Haldiram	Bhujia	45	200	4.7
102	Bikaji	AlooBhujia	40	180	4.5
103	Bikanervala	Navratan	55	250	4.6
104	PepsiCo	LaysMasala	20	100	4.2
105	Balaji	RatlamiSev	35	150	4.4
106	Pratap	Gathiya	30	200	4.1
107	Aachi	Mixture	50	220	4.3
108	GRB	KaraBoondi	45	200	4.5
109	MTR	Chakli	60	250	4.6
110	Maiyas	Nippattu	55	230	4.2
111	Lays	ClassicSalt	20	100	4.1
112	Haldiram	MoongDal	40	150	4.7

/*4.2 Table: PROC TABULATE Summary*/

proc tabulate data=all_namkeen_set;

class Brand Type;

var Price Rating;

table Brand*Type,

(Price Rating)*(mean min max);

title "SUMMARY TABLE OF PRICE AND RATING BY BRAND AND TYPE";

run;

OUTPUT:

SUMMARY TABLE OF PRICE AND RATING BY BRAND AND TYPE


		Price			Rating
		Mean	Min	Max	Mean	Min	Max
Brand	Type	50.00	50.00	50.00	4.30	4.30	4.30
Aachi	Mixture	50.00	50.00	50.00	4.30	4.30	4.30
Balaji	RatlamiSev	35.00	35.00	35.00	4.40	4.40	4.40
Bikaji	AlooBhujia	40.00	40.00	40.00	4.50	4.50	4.50
Bikanervala	Navratan	55.00	55.00	55.00	4.60	4.60	4.60
GRB	KaraBoondi	45.00	45.00	45.00	4.50	4.50	4.50
Haldiram	Bhujia	45.00	45.00	45.00	4.70	4.70	4.70
Haldiram	MoongDal	40.00	40.00	40.00	4.70	4.70	4.70
Lays	ClassicSalt	20.00	20.00	20.00	4.10	4.10	4.10
MTR	Chakli	60.00	60.00	60.00	4.60	4.60	4.60
Maiyas	Nippattu	55.00	55.00	55.00	4.20	4.20	4.20
PepsiCo	LaysMasala	20.00	20.00	20.00	4.20	4.20	4.20
Pratap	Gathiya	30.00	30.00	30.00	4.10	4.10	4.10

5: Automation with Reporting Macro

/*Macro 2: Summary Reporting*/

%macro namkeen_report(ds=);

%put Generating summary report for dataset: &ds;

proc means data=&ds n mean min max;

var Price Weight_g Rating;

class Brand;

title "PROC MEANS SUMMARY FOR &ds";

run;

proc freq data=&ds;

tables Type / nocum nopercent;

title "FREQUENCY OF NAMKEEN TYPES IN &ds";

run;

proc sgplot data=&ds;

vbar Brand / response=Price stat=mean datalabel;

title "AVERAGE PRICE BY BRAND - &ds";

run;

proc sgplot data=&ds;

vbar Type / response=Rating stat=mean datalabel;

title "AVERAGE CUSTOMER RATING BY NAMKEEN TYPE - &ds";

run;

%mend namkeen_report;

%namkeen_report(ds=all_namkeen_set);

OUTPUT:

PROC MEANS SUMMARY FOR all_namkeen_set

The MEANS Procedure


Brand	N Obs	Variable	N	Mean	Minimum	Maximum
Aachi	1	Price Weight_g Rating	1 1 1	50.0000000 220.0000000 4.3000000	50.0000000 220.0000000 4.3000000	50.0000000 220.0000000 4.3000000
Balaji	1	Price Weight_g Rating	1 1 1	35.0000000 150.0000000 4.4000000	35.0000000 150.0000000 4.4000000	35.0000000 150.0000000 4.4000000
Bikaji	1	Price Weight_g Rating	1 1 1	40.0000000 180.0000000 4.5000000	40.0000000 180.0000000 4.5000000	40.0000000 180.0000000 4.5000000
Bikanervala	1	Price Weight_g Rating	1 1 1	55.0000000 250.0000000 4.6000000	55.0000000 250.0000000 4.6000000	55.0000000 250.0000000 4.6000000
GRB	1	Price Weight_g Rating	1 1 1	45.0000000 200.0000000 4.5000000	45.0000000 200.0000000 4.5000000	45.0000000 200.0000000 4.5000000
Haldiram	2	Price Weight_g Rating	2 2 2	42.5000000 175.0000000 4.7000000	40.0000000 150.0000000 4.7000000	45.0000000 200.0000000 4.7000000
Lays	1	Price Weight_g Rating	1 1 1	20.0000000 100.0000000 4.1000000	20.0000000 100.0000000 4.1000000	20.0000000 100.0000000 4.1000000
MTR	1	Price Weight_g Rating	1 1 1	60.0000000 250.0000000 4.6000000	60.0000000 250.0000000 4.6000000	60.0000000 250.0000000 4.6000000
Maiyas	1	Price Weight_g Rating	1 1 1	55.0000000 230.0000000 4.2000000	55.0000000 230.0000000 4.2000000	55.0000000 230.0000000 4.2000000
PepsiCo	1	Price Weight_g Rating	1 1 1	20.0000000 100.0000000 4.2000000	20.0000000 100.0000000 4.2000000	20.0000000 100.0000000 4.2000000
Pratap	1	Price Weight_g Rating	1 1 1	30.0000000 200.0000000 4.1000000	30.0000000 200.0000000 4.1000000	30.0000000 200.0000000 4.1000000

FREQUENCY OF NAMKEEN TYPES IN all_namkeen_set

The FREQ Procedure


Type	Frequency
AlooBhujia	1
Bhujia	1
Chakli	1
ClassicSalt	1
Gathiya	1
KaraBoondi	1
LaysMasala	1
Mixture	1
MoongDal	1
Navratan	1
Nippattu	1
RatlamiSev	1

To Visit My Previous Online Streaming Flatform Dataset:Click Here

To Visit My Previous Statistical Evaluation Of Clinical Trials:Click Here

To Visit My Previous Unlocking Retail Insights Dataset:Click Here

To Visit My Previous Sas Interview Questions-1:Click Here

Follow Us On :

--- FOLLOW OUR BLOG FOR MORE INFORMATION.

--->PLEASE DO COMMENTS AND SHARE OUR BLOG.

Search This Blog

SAS Learning Hub

299.Which namkeen products sell the most, and what does SAS data creation reveal about customer taste?A Sas Study

Which namkeen products sell the most, and what does SAS data creation reveal about customer taste?A Sas Study

Follow Us On :

Comments

Post a Comment

Popular posts from this blog

409.Can We Build a Reliable Emergency Services Analytics & Fraud Detection System in SAS While Identifying and Fixing Intentional Errors?

397.If a satellite has excellent signal strength but very high latency, can it still deliver good quality communication? Why or why not?A Sas Study

401.How Efficient Are Global Data Centers? A Complete SAS Analytics Study

Obs	SnackID	Rating
1	101	4.7
2	102	4.5
3	103	4.6
4	104	4.2
5	105	4.4
6	106	4.1
7	107	4.3
8	108	4.5
9	109	4.6
10	110	4.2
11	111	4.1
12	112	4.7

Obs	SnackID	Rating
1	101	4.7
2	102	4.5
3	103	4.6
4	104	4.2
5	105	4.4
6	106	4.1
7	107	4.3
8	108	4.5
9	109	4.6
10	110	4.2
11	111	4.1
12	112	4.7

Obs	SnackID	Rating
1	101	4.7
2	102	4.5
3	103	4.6
4	104	4.2
5	105	4.4
6	106	4.1
7	107	4.3
8	108	4.5
9	109	4.6
10	110	4.2
11	111	4.1
12	112	4.7

Obs	SnackID	Rating
1	101	4.7
2	102	4.5
3	103	4.6
4	104	4.2
5	105	4.4
6	106	4.1
7	107	4.3
8	108	4.5
9	109	4.6
10	110	4.2
11	111	4.1
12	112	4.7

Obs	SnackID	Rating
1	101	4.7
2	102	4.5
3	103	4.6
4	104	4.2
5	105	4.4
6	106	4.1
7	107	4.3
8	108	4.5
9	109	4.6
10	110	4.2
11	111	4.1
12	112	4.7

Obs	SnackID	Rating
1	101	4.7
2	102	4.5
3	103	4.6
4	104	4.2
5	105	4.4
6	106	4.1
7	107	4.3
8	108	4.5
9	109	4.6
10	110	4.2
11	111	4.1
12	112	4.7