191.COMPLETE SAS FASHION DATA ANALYSIS USING PROC PRINT | PROC CONTENTS | PROC FREQ | PROC MEANS | PROC SQL | PROC FORMAT | PROC UNIVARIATE | PROC SORT | PROC SGPLOT | MACROS

COMPLETE SAS FASHION DATA ANALYSIS USING PROC PRINT | PROC CONTENTS | PROC FREQ | PROC MEANS | PROC SQL | PROC FORMAT | PROC UNIVARIATE | PROC SORT | PROC SGPLOT | MACROS

/*Analysis of Different Types of Dresses using SAS (PROC, SQL, Macros)*/

Step 1: Create the Dataset

Creating a dataset called dresses with 20 observations and the following variables:

DressID – Unique identifier

Type – Type of dress (e.g., Casual, Formal, Party, Ethnic)

Material – Fabric (e.g., Cotton, Silk, Polyester, Linen)

Size – Size category (S, M, L, XL)

Price – Cost in INR

Color – Color of the dress

Brand – Brand name

Availability – In stock (Yes/No)

Rating – Customer rating (1 to 5)

data dresses;

    input DressID Type $ Material:$15. Size $ Price Color $ Brand:$15.

          Availability $ Rating;

    datalines;

101 Casual Cotton M 1200 Blue Zara Yes 4

102 Formal Silk L 2500 Black H&M Yes 5

103 Party Polyester S 1800 Red Forever21 No 3

104 Ethnic Cotton XL 2000 Orange Biba Yes 4

105 Casual Linen M 1300 White AllenSolly No 2

106 Formal Polyester L 2600 Grey VanHeusen Yes 5

107 Party Silk S 3000 Pink Zivame Yes 4

108 Ethnic Silk M 2200 Yellow FabIndia Yes 5

109 Casual Cotton L 1400 Green Max Yes 3

110 Formal Linen XL 2700 Maroon Arrow No 4

111 Party Cotton M 1900 Blue ONLY Yes 5

112 Ethnic Polyester L 2100 Beige W Yes 3

113 Casual Silk S 1600 Red Aurelia Yes 4

114 Formal Cotton M 2400 Navy VanHeusen Yes 5

115 Party Linen XL 2800 Golden GlobalDesi No 3

116 Ethnic Cotton M 2000 Pink Biba Yes 4

117 Casual Polyester L 1500 Brown Max Yes 3

118 Formal Silk S 2550 Black AllenSolly Yes 5

119 Party Cotton M 1750 Green ONLY Yes 4

120 Ethnic Linen L 2300 Yellow FabIndia Yes 5

;

run;

proc print;run;

Output:

Obs	DressID	Type	Material	Size	Price	Color	Brand	Availability	Rating
1	101	Casual	Cotton	M	1200	Blue	Zara	Yes	4
2	102	Formal	Silk	L	2500	Black	H&M	Yes	5
3	103	Party	Polyester	S	1800	Red	Forever21	No	3
4	104	Ethnic	Cotton	XL	2000	Orange	Biba	Yes	4
5	105	Casual	Linen	M	1300	White	AllenSolly	No	2
6	106	Formal	Polyester	L	2600	Grey	VanHeusen	Yes	5
7	107	Party	Silk	S	3000	Pink	Zivame	Yes	4
8	108	Ethnic	Silk	M	2200	Yellow	FabIndia	Yes	5
9	109	Casual	Cotton	L	1400	Green	Max	Yes	3
10	110	Formal	Linen	XL	2700	Maroon	Arrow	No	4
11	111	Party	Cotton	M	1900	Blue	ONLY	Yes	5
12	112	Ethnic	Polyester	L	2100	Beige	W	Yes	3
13	113	Casual	Silk	S	1600	Red	Aurelia	Yes	4
14	114	Formal	Cotton	M	2400	Navy	VanHeusen	Yes	5
15	115	Party	Linen	XL	2800	Golden	GlobalDesi	No	3
16	116	Ethnic	Cotton	M	2000	Pink	Biba	Yes	4
17	117	Casual	Polyester	L	1500	Brown	Max	Yes	3
18	118	Formal	Silk	S	2550	Black	AllenSolly	Yes	5
19	119	Party	Cotton	M	1750	Green	ONLY	Yes	4
20	120	Ethnic	Linen	L	2300	Yellow	FabIndia	Yes	5

Step 2: Explore the Dataset using PROC PRINT and PROC CONTENTS

/*PROC PRINT is used to display the dataset.*/

/*PROC CONTENTS is used to show metadata (variable names, types, lengths).*/

proc print data=dresses;

    title "Complete Dress Dataset";

run;

Output:

Complete Dress Dataset

Obs	DressID	Type	Material	Size	Price	Color	Brand	Availability	Rating
1	101	Casual	Cotton	M	1200	Blue	Zara	Yes	4
2	102	Formal	Silk	L	2500	Black	H&M	Yes	5
3	103	Party	Polyester	S	1800	Red	Forever21	No	3
4	104	Ethnic	Cotton	XL	2000	Orange	Biba	Yes	4
5	105	Casual	Linen	M	1300	White	AllenSolly	No	2
6	106	Formal	Polyester	L	2600	Grey	VanHeusen	Yes	5
7	107	Party	Silk	S	3000	Pink	Zivame	Yes	4
8	108	Ethnic	Silk	M	2200	Yellow	FabIndia	Yes	5
9	109	Casual	Cotton	L	1400	Green	Max	Yes	3
10	110	Formal	Linen	XL	2700	Maroon	Arrow	No	4
11	111	Party	Cotton	M	1900	Blue	ONLY	Yes	5
12	112	Ethnic	Polyester	L	2100	Beige	W	Yes	3
13	113	Casual	Silk	S	1600	Red	Aurelia	Yes	4
14	114	Formal	Cotton	M	2400	Navy	VanHeusen	Yes	5
15	115	Party	Linen	XL	2800	Golden	GlobalDesi	No	3
16	116	Ethnic	Cotton	M	2000	Pink	Biba	Yes	4
17	117	Casual	Polyester	L	1500	Brown	Max	Yes	3
18	118	Formal	Silk	S	2550	Black	AllenSolly	Yes	5
19	119	Party	Cotton	M	1750	Green	ONLY	Yes	4
20	120	Ethnic	Linen	L	2300	Yellow	FabIndia	Yes	5

proc contents data=dresses;

    title "Dataset Metadata: DRESSES";

run;

Output:

Dataset Metadata: DRESSES

The CONTENTS Procedure

Data Set Name	WORK.DRESSES	Observations	20
Member Type	DATA	Variables	9
Engine	V9	Indexes	0
Created	14/09/2015 00:16:55	Observation Length	88
Last Modified	14/09/2015 00:16:55	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	743
Obs in First Data Page	20
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD11200_DESKTOP-QFAA4KV_\dresses.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Alphabetic List of Variables and Attributes
#	Variable	Type	Len
8	Availability	Char	8
7	Brand	Char	15
6	Color	Char	8
1	DressID	Num	8
3	Material	Char	15
5	Price	Num	8
9	Rating	Num	8
4	Size	Char	8
2	Type	Char	8

Step 3: Frequency Analysis using PROC FREQ

proc freq data=dresses;

    tables Type Material Size Brand / nocum nopercent;

    title "Frequency Distribution of Type, Material, Size, and Brand";

run;

Output:

Frequency Distribution of Type, Material, Size, and Brand

The FREQ Procedure

Type	Frequency
Casual	5
Ethnic	5
Formal	5
Party	5

Material	Frequency
Cotton	7
Linen	4
Polyester	4
Silk	5

Size	Frequency
L	6
M	7
S	4
XL	3

Brand	Frequency
AllenSolly	2
Arrow	1
Aurelia	1
Biba	2
FabIndia	2
Forever21	1
GlobalDesi	1
H&M	1
Max	2
ONLY	2
VanHeusen	2
W	1
Zara	1
Zivame	1

Step 4: Summary Statistics using PROC MEANS

proc means data=dresses mean maxdec=2;

    class Type;

    var Price Rating;

    title "Mean Price and Rating by Dress Type";

run;

Output:

Mean Price and Rating by Dress Type

The MEANS Procedure

Type	N Obs	Variable	Mean
Casual	5	Price Rating	1400.00 3.20
Ethnic	5	Price Rating	2120.00 4.20
Formal	5	Price Rating	2550.00 4.80
Party	5	Price Rating	2250.00 3.80

Step 5: Using PROC SQL for Grouping and Filtering

Let’s use PROC SQL to identify:

1.Top 3 highest-rated brand

2.Count of dresses by size

3.Average price of dresses that are "In stock"

proc sql outobs=3;

    title "Top Rated Brands";

    select Brand, avg(Rating) as AvgRating format=4.2

    from dresses

    group by Brand

    having AvgRating >= 4

    order by AvgRating desc;

quit;

Output:

Top Rated Brands

Brand	AvgRating
H&M	5.00
VanHeusen	5.00
FabIndia	5.00

proc sql;

    title "Dress Count by Size";

    select Size, count(*) as Total

    from dresses

    group by Size;

quit;

Output:

Dress Count by Size

Size	Total
L	6
M	7
S	4
XL	3

proc sql;

    title "Average Price of Available Dresses";

    select avg(Price) as AvgPrice_InStock format=8.2

    from dresses

    where Availability = 'Yes';

quit;

Output:

Average Price of Available Dresses

AvgPrice_InStock
2062.50

Step 6: Using PROC SORT and PROC UNIVARIATE

proc sort data=dresses out=sorted_dresses;

    by descending Price;

run;

proc print data=sorted_dresses(obs=10);

    title "Top 10 Most Expensive Dresses";

run;

Output:

Top 10 Most Expensive Dresses

Obs	DressID	Type	Material	Size	Price	Color	Brand	Availability	Rating
1	107	Party	Silk	S	3000	Pink	Zivame	Yes	4
2	115	Party	Linen	XL	2800	Golden	GlobalDesi	No	3
3	110	Formal	Linen	XL	2700	Maroon	Arrow	No	4
4	106	Formal	Polyester	L	2600	Grey	VanHeusen	Yes	5
5	118	Formal	Silk	S	2550	Black	AllenSolly	Yes	5
6	102	Formal	Silk	L	2500	Black	H&M	Yes	5
7	114	Formal	Cotton	M	2400	Navy	VanHeusen	Yes	5
8	120	Ethnic	Linen	L	2300	Yellow	FabIndia	Yes	5
9	108	Ethnic	Silk	M	2200	Yellow	FabIndia	Yes	5
10	112	Ethnic	Polyester	L	2100	Beige	W	Yes	3

proc univariate data=dresses;

    var Price;

    histogram Price;

    title "Price Distribution of Dresses";

run;

Output:

Price Distribution of Dresses

The UNIVARIATE Procedure

Variable: Price

Moments
N	20	Sum Weights	20
Mean	2080	Sum Observations	41600
Std Deviation	524.504779	Variance	275105.263
Skewness	-0.0352472	Kurtosis	-0.9926
Uncorrected SS	91755000	Corrected SS	5227000
Coeff Variation	25.2165759	Std Error Mean	117.282834

Basic Statistical Measures
Location		Variability
Mean	2080.000	Std Deviation	524.50478
Median	2050.000	Variance	275105
Mode	2000.000	Range	1800
		Interquartile Range	850.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	17.73491	Pr > |t|	<.0001
Sign	M	10	Pr >= |M|	<.0001
Signed Rank	S	105	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	3000
99%	3000
95%	2900
90%	2750
75% Q3	2525
50% Median	2050
25% Q1	1675
10%	1350
5%	1250
1%	1200
0% Min	1200

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
1200	1	2550	18
1300	5	2600	6
1400	9	2700	10
1500	17	2800	15
1600	13	3000	7

Step 7: Conditional Formatting using PROC FORMAT

 proc format;

    value pricegrp

        low -< 1500 = 'Low'

        1500 -< 2500 = 'Medium'

        2500 - high = 'High';

run;

proc freq data=dresses;

    tables Price;

    format Price pricegrp.;

    title "Price Group Distribution";

run;

Output:

Price Group Distribution

The FREQ Procedure

Price	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Low	3	15.00	3	15.00
Medium	11	55.00	14	70.00
High	6	30.00	20	100.00

Step 8: Creating a Macro to Generate Summary Reports

%macro summary_report(groupvar);

    proc sql;

        title "Average Price and Rating by &groupvar";

        select &groupvar,

               count(*) as Total,

               avg(Price) as AvgPrice format=8.2,

               avg(Rating) as AvgRating format=4.2

        from dresses

        group by &groupvar;

    quit;

%mend;

%summary_report(Type);

Output:

Average Price and Rating by Type

Type	Total	AvgPrice	AvgRating
Casual	5	1400.00	3.20
Ethnic	5	2120.00	4.20
Formal	5	2550.00	4.80
Party	5	2250.00	3.80

%summary_report(Brand);

Output:

Average Price and Rating by Brand

Brand	Total	AvgPrice	AvgRating
AllenSolly	2	1925.00	3.50
Arrow	1	2700.00	4.00
Aurelia	1	1600.00	4.00
Biba	2	2000.00	4.00
FabIndia	2	2250.00	5.00
Forever21	1	1800.00	3.00
GlobalDesi	1	2800.00	3.00
H&M	1	2500.00	5.00
Max	2	1450.00	3.00
ONLY	2	1825.00	4.50
VanHeusen	2	2500.00	5.00
W	1	2100.00	3.00
Zara	1	1200.00	4.00
Zivame	1	3000.00	4.00

%summary_report(Size);

Output:

Average Price and Rating by Size

Size	Total	AvgPrice	AvgRating
L	6	2066.67	4.00
M	7	1821.43	4.14
S	4	2237.50	4.00
XL	3	2500.00	3.67

Step 9: Filtering Dresses using Macros and SQL

%macro filter_dress(brand=, minprice=, maxprice=);

    proc sql;

        title "Filtered Dresses for &brand Between &minprice and &maxprice";

        select * from dresses

        where Brand = "&brand"

          and Price between &minprice and &maxprice;

    quit;

%mend;

%filter_dress(brand=Biba, minprice=1800, maxprice=2200);

Output:

Filtered Dresses for Biba Between 1800 and 2200

DressID	Type	Material	Size	Price	Color	Brand	Availability	Rating
104	Ethnic	Cotton	XL	2000	Orange	Biba	Yes	4
116	Ethnic	Cotton	M	2000	Pink	Biba	Yes	4

%filter_dress(brand=VanHeusen, minprice=2000, maxprice=2700);

Output:

Filtered Dresses for VanHeusen Between 2000 and 2700

DressID	Type	Material	Size	Price	Color	Brand	Availability	Rating
106	Formal	Polyester	L	2600	Grey	VanHeusen	Yes	5
114	Formal	Cotton	M	2400	Navy	VanHeusen	Yes	5

Step 10: Creating New Variables (Derived Columns)

Add two new columns:

1.DiscountedPrice – 10% discount if rating = 5

2.IsPopular – If Rating ≥ 4 then 'Yes' else 'No'

data dresses_updated;

    set dresses;

    if Rating = 5 then DiscountedPrice = Price * 0.99;

    else DiscountedPrice = Price;

    if Rating >= 4 then IsPopular = 'Yes';

    else IsPopular = 'No';

run;

proc print data=dresses_updated;

    title "Dresses with Derived Columns";

run;

Output:

Dresses with Derived Columns

Obs	DressID	Type	Material	Size	Price	Color	Brand	Availability	Rating	DiscountedPrice	IsPopular
1	101	Casual	Cotton	M	1200	Blue	Zara	Yes	4	1200.0	Yes
2	102	Formal	Silk	L	2500	Black	H&M	Yes	5	2475.0	Yes
3	103	Party	Polyester	S	1800	Red	Forever21	No	3	1800.0	No
4	104	Ethnic	Cotton	XL	2000	Orange	Biba	Yes	4	2000.0	Yes
5	105	Casual	Linen	M	1300	White	AllenSolly	No	2	1300.0	No
6	106	Formal	Polyester	L	2600	Grey	VanHeusen	Yes	5	2574.0	Yes
7	107	Party	Silk	S	3000	Pink	Zivame	Yes	4	3000.0	Yes
8	108	Ethnic	Silk	M	2200	Yellow	FabIndia	Yes	5	2178.0	Yes
9	109	Casual	Cotton	L	1400	Green	Max	Yes	3	1400.0	No
10	110	Formal	Linen	XL	2700	Maroon	Arrow	No	4	2700.0	Yes
11	111	Party	Cotton	M	1900	Blue	ONLY	Yes	5	1881.0	Yes
12	112	Ethnic	Polyester	L	2100	Beige	W	Yes	3	2100.0	No
13	113	Casual	Silk	S	1600	Red	Aurelia	Yes	4	1600.0	Yes
14	114	Formal	Cotton	M	2400	Navy	VanHeusen	Yes	5	2376.0	Yes
15	115	Party	Linen	XL	2800	Golden	GlobalDesi	No	3	2800.0	No
16	116	Ethnic	Cotton	M	2000	Pink	Biba	Yes	4	2000.0	Yes
17	117	Casual	Polyester	L	1500	Brown	Max	Yes	3	1500.0	No
18	118	Formal	Silk	S	2550	Black	AllenSolly	Yes	5	2524.5	Yes
19	119	Party	Cotton	M	1750	Green	ONLY	Yes	4	1750.0	Yes
20	120	Ethnic	Linen	L	2300	Yellow	FabIndia	Yes	5	2277.0	Yes

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