188.COMPREHENSIVE SAS ANALYSIS OF WEBSITE DATASET | DATA STEP CREATION | PROC CONTENTS FOR METADATA | PROC MEANS AND PROC FREQ FOR STATISTICS | PROC SQL FOR DATA MANIPULATION | PROC SORT FOR RANKING | PROC TRANSPOSE FOR RESHAPING | PROC EXPORT FOR OUTPUT | MACROS FOR AUTOMATION

COMPREHENSIVE SAS ANALYSIS OF WEBSITE DATASET | DATA STEP CREATION | PROC CONTENTS FOR METADATA | PROC MEANS AND PROC FREQ FOR STATISTICS | PROC SQL FOR DATA MANIPULATION | PROC SORT FOR RANKING | PROC TRANSPOSE FOR RESHAPING | PROC EXPORT FOR OUTPUT | MACROS FOR AUTOMATION

/*Creating a dataset of different types of websites*/

Step 1: Creating the Raw Dataset

data websites_info;

    length Name $20 Type $15 Region $15;

    input Website_ID Name $ Type $ Monthly_Visitors Launch_Year Region $;

    datalines;

1 Amazon         E-commerce    2500 1994 Global

2 Facebook       Social_Media  2900 2004 Global

3 CNN            News           120 1995 USA

4 Alibaba        E-commerce    1100 1999 Asia

5 Twitter        Social_Media   550 2006 Global

6 Flipkart       E-commerce     350 2007 India

7 BBC            News           160 1922 UK

8 LinkedIn       Social_Media   800 2003 Global

9 TimesofIndia   News           180 1838 India

10 Etsy          E-commerce     400 2005 Global

;

run;

proc print;run;

Output:

Obs	Name	Type	Region	Website_ID	Monthly_Visitors	Launch_Year
1	Amazon	E-commerce	Global	1	2500	1994
2	Facebook	Social_Media	Global	2	2900	2004
3	CNN	News	USA	3	120	1995
4	Alibaba	E-commerce	Asia	4	1100	1999
5	Twitter	Social_Media	Global	5	550	2006
6	Flipkart	E-commerce	India	6	350	2007
7	BBC	News	UK	7	160	1922
8	LinkedIn	Social_Media	Global	8	800	2003
9	TimesofIndia	News	India	9	180	1838
10	Etsy	E-commerce	Global	10	400	2005

Step 2: Exploring the Dataset

proc contents data=websites_info;

run;

Output:

The CONTENTS Procedure

Data Set Name	WORK.WEBSITES_INFO	Observations	10
Member Type	DATA	Variables	6
Engine	V9	Indexes	0
Created	14/09/2015 01:39:11	Observation Length	80
Last Modified	14/09/2015 01:39:11	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	817
Obs in First Data Page	10
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD10316_DESKTOP-QFAA4KV_\websites_info.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Alphabetic List of Variables and Attributes
#	Variable	Type	Len
6	Launch_Year	Num	8
5	Monthly_Visitors	Num	8
1	Name	Char	20
3	Region	Char	15
2	Type	Char	15
4	Website_ID	Num	8

Step 3: Summary Statistics

PROC MEANS

Summarizing continuous variables:

proc means data=websites_info mean median max min std;

    var Monthly_Visitors Launch_Year;

run;

Output:

The MEANS Procedure

Variable	Mean	Median	Maximum	Minimum	Std Dev
Monthly_Visitors Launch_Year	906.0000000 1977.30	475.0000000 2001.00	2900.00 2007.00	120.0000000 1838.00	997.7218495 55.1483857

PROC FREQ

To get the frequency of each website type:

proc freq data=websites_info;

    tables Type Region;

run;

Output:

The FREQ Procedure

Type	Frequency	Percent	Cumulative Frequency	Cumulative Percent
E-commerce	4	40.00	4	40.00
News	3	30.00	7	70.00
Social_Media	3	30.00	10	100.00

Region	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Asia	1	10.00	1	10.00
Global	5	50.00	6	60.00
India	2	20.00	8	80.00
UK	1	10.00	9	90.00
USA	1	10.00	10	100.00

Step 4: Aggregation Using PROC SQL

Total Visitors by Website Type

proc sql;

    select Type, 

           count(*) as Total_Websites, 

           sum(Monthly_Visitors) as Total_Visitors

    from websites_info

    group by Type;

quit;

Output:

Type	Total_Websites	Total_Visitors
E-commerce	4	4350
News	3	460
Social_Media	3	4250

Step 5: Creating New Variables

data websites_enriched;

    set websites_info;

    Website_Age = year("23may2025"d) - Launch_Year;

run;

proc print;run;

Output:

Obs	Name	Type	Region	Website_ID	Monthly_Visitors	Launch_Year	Website_Age
1	Amazon	E-commerce	Global	1	2500	1994	31
2	Facebook	Social_Media	Global	2	2900	2004	21
3	CNN	News	USA	3	120	1995	30
4	Alibaba	E-commerce	Asia	4	1100	1999	26
5	Twitter	Social_Media	Global	5	550	2006	19
6	Flipkart	E-commerce	India	6	350	2007	18
7	BBC	News	UK	7	160	1922	103
8	LinkedIn	Social_Media	Global	8	800	2003	22
9	TimesofIndia	News	India	9	180	1838	187
10	Etsy	E-commerce	Global	10	400	2005	20

PROC UNIVARIATE

Understanding the distribution of website age:

proc univariate data=websites_enriched;

    var Website_Age;

run;

Output:

The UNIVARIATE Procedure

Variable: Website_Age

Moments
N	10	Sum Weights	10
Mean	47.7	Sum Observations	477
Std Deviation	55.1483857	Variance	3041.34444
Skewness	2.27276154	Kurtosis	4.84585541
Uncorrected SS	50125	Corrected SS	27372.1
Coeff Variation	115.615064	Std Error Mean	17.4394508

Basic Statistical Measures
Location		Variability
Mean	47.70000	Std Deviation	55.14839
Median	24.00000	Variance	3041
Mode	.	Range	169.00000
		Interquartile Range	11.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	2.735178	Pr > |t|	0.0230
Sign	M	5	Pr >= |M|	0.0020
Signed Rank	S	27.5	Pr >= |S|	0.0020

Quantiles (Definition 5)
Level	Quantile
100% Max	187.0
99%	187.0
95%	187.0
90%	145.0
75% Q3	31.0
50% Median	24.0
25% Q1	20.0
10%	18.5
5%	18.0
1%	18.0
0% Min	18.0

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
18	6	26	4
19	5	30	3
20	10	31	1
21	2	103	7
22	8	187	9

Step 6: Using MACROS

%macro website_report(webtype);

    title "Report for &webtype Websites";

    

    proc sql;

        select * from websites_info

        where Type="&webtype";

    quit;

    proc means data=websites_info;

        where Type="&webtype";

        var Monthly_Visitors;

    run;

    proc freq data=websites_info;

        where Type="&webtype";

        tables Region;

    run;

    

    title;

%mend website_report;

/* Generate reports */

%website_report(E-commerce);

Output:

Report for E-commerce Websites

Name	Type	Region	Website_ID	Monthly_Visitors	Launch_Year
Amazon	E-commerce	Global	1	2500	1994
Alibaba	E-commerce	Asia	4	1100	1999
Flipkart	E-commerce	India	6	350	2007
Etsy	E-commerce	Global	10	400	2005

Report for E-commerce Websites

The MEANS Procedure

Analysis Variable : Monthly_Visitors
N	Mean	Std Dev	Minimum	Maximum
4	1087.50	1001.98	350.0000000	2500.00

Report for E-commerce Websites

The FREQ Procedure

Region	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Asia	1	25.00	1	25.00
Global	2	50.00	3	75.00
India	1	25.00	4	100.00

%website_report(Social_Media);

Output:

Report for Social_Media Websites

Name	Type	Region	Website_ID	Monthly_Visitors	Launch_Year
Facebook	Social_Media	Global	2	2900	2004
Twitter	Social_Media	Global	5	550	2006
LinkedIn	Social_Media	Global	8	800	2003

Report for Social_Media Websites

The MEANS Procedure

Analysis Variable : Monthly_Visitors
N	Mean	Std Dev	Minimum	Maximum
3	1416.67	1290.67	550.0000000	2900.00

Report for Social_Media Websites

The FREQ Procedure

Region	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Global	3	100.00	3	100.00

Step 7: Sorting and Filtering

proc sort data=websites_info out=sorted_websites;

    by descending Monthly_Visitors;

run;

Filter: Websites Launched After 2000

data post2000;

    set websites_info;

    if Launch_Year > 2000;

run;

proc print data=post2000;

    title "Websites Launched After 2000";

run;

Output:

Websites Launched After 2000

Obs	Name	Type	Region	Website_ID	Monthly_Visitors	Launch_Year
1	Facebook	Social_Media	Global	2	2900	2004
2	Twitter	Social_Media	Global	5	550	2006
3	Flipkart	E-commerce	India	6	350	2007
4	LinkedIn	Social_Media	Global	8	800	2003
5	Etsy	E-commerce	Global	10	400	2005

Step 8: Classifying Based on Visitors

data websites_classified;

    set websites_info;

    length Visitor_Category $15.;

    if Monthly_Visitors >= 1000 then Visitor_Category = "Very High";

    else if Monthly_Visitors >= 500 then Visitor_Category = "High";

    else if Monthly_Visitors >= 200 then Visitor_Category = "Medium";

    else Visitor_Category = "Low";

run;

proc print;run;

Output:

Obs	Name	Type	Region	Website_ID	Monthly_Visitors	Launch_Year	Visitor_Category
1	Amazon	E-commerce	Global	1	2500	1994	Very High
2	Facebook	Social_Media	Global	2	2900	2004	Very High
3	CNN	News	USA	3	120	1995	Low
4	Alibaba	E-commerce	Asia	4	1100	1999	Very High
5	Twitter	Social_Media	Global	5	550	2006	High
6	Flipkart	E-commerce	India	6	350	2007	Medium
7	BBC	News	UK	7	160	1922	Low
8	LinkedIn	Social_Media	Global	8	800	2003	High
9	TimesofIndia	News	India	9	180	1838	Low
10	Etsy	E-commerce	Global	10	400	2005	Medium

proc freq data=websites_classified;

    tables Visitor_Category;

run;

Output:

The FREQ Procedure

Visitor_Category	Frequency	Percent	Cumulative Frequency	Cumulative Percent
High	2	20.00	2	20.00
Low	3	30.00	5	50.00
Medium	2	20.00	7	70.00
Very High	3	30.00	10	100.00

Step 9: Using PROC TRANSPOSE

proc transpose data=websites_info out=transposed_visitors;

    by Region notsorted;

    id Name;

    var Monthly_Visitors;

run;

proc print;run;

Output:

Obs	Region	_NAME_	Amazon	Facebook	CNN	Alibaba	Twitter	Flipkart	BBC	LinkedIn	TimesofIndia	Etsy
1	Global	Monthly_Visitors	2500	2900	.	.	.	.	.	.	.	.
2	USA	Monthly_Visitors	.	.	120	.	.	.	.	.	.	.
3	Asia	Monthly_Visitors	.	.	.	1100	.	.	.	.	.	.
4	Global	Monthly_Visitors	.	.	.	.	550	.	.	.	.	.
5	India	Monthly_Visitors	.	.	.	.	.	350	.	.	.	.
6	UK	Monthly_Visitors	.	.	.	.	.	.	160	.	.	.
7	Global	Monthly_Visitors	.	.	.	.	.	.	.	800	.	.
8	India	Monthly_Visitors	.	.	.	.	.	.	.	.	180	.
9	Global	Monthly_Visitors	.	.	.	.	.	.	.	.	.	400

Step 10: Export the Final Dataset

proc export data=websites_classified

    outfile="/folders/myfolders/websites.csv"

    dbms=csv

    replace;

run;

Log:

NOTE: "/folders/myfolders/websites.csv" file was successfully created.

NOTE: PROCEDURE EXPORT used (Total process time):

      real time           1.51 seconds

      cpu time            0.23 seconds

Step 11: Creating a Macro for Age Bracket Summary

%macro age_summary(minage, maxage);

    proc sql;

        select Name, Website_Age 

        from websites_enriched

        where Website_Age between &minage and &maxage;

    quit;

%mend age_summary;

%age_summary(10, 20);

Output:

Name	Website_Age
Twitter	19
Flipkart	18
Etsy	20

%age_summary(20, 30);

Output:

Name	Website_Age
Facebook	21
CNN	30
Alibaba	26
LinkedIn	22
Etsy	20

Step 12: Joining Two Tables Using SQL

data website_ratings;

    input Website_ID Rating Score;

    datalines;

1 5 4.8

2 4 4.3

3 3 3.9

4 4 4.2

5 3 4.0

6 5 4.7

7 4 4.1

8 4 4.4

9 3 3.8

10 4 4.6

;

run;

proc print;run;

Output:

Obs	Website_ID	Rating	Score
1	1	5	4.8
2	2	4	4.3
3	3	3	3.9
4	4	4	4.2
5	5	3	4.0
6	6	5	4.7
7	7	4	4.1
8	8	4	4.4
9	9	3	3.8
10	10	4	4.6

proc sql;

    create table website_full as

    select a.*, b.Rating, b.Score

    from websites_info a

    inner join website_ratings b

    on a.Website_ID = b.Website_ID;

quit;

proc sort data=website_full out=ranked;

    by descending Score;

run;

proc print data=ranked;

    title "Websites Ranked by Score";

run;

Output:

Websites Ranked by Score

Obs	Name	Type	Region	Website_ID	Monthly_Visitors	Launch_Year	Rating	Score
1	Amazon	E-commerce	Global	1	2500	1994	5	4.8
2	Flipkart	E-commerce	India	6	350	2007	5	4.7
3	Etsy	E-commerce	Global	10	400	2005	4	4.6
4	LinkedIn	Social_Media	Global	8	800	2003	4	4.4
5	Facebook	Social_Media	Global	2	2900	2004	4	4.3
6	Alibaba	E-commerce	Asia	4	1100	1999	4	4.2
7	BBC	News	UK	7	160	1922	4	4.1
8	Twitter	Social_Media	Global	5	550	2006	3	4.0
9	CNN	News	USA	3	120	1995	3	3.9
10	TimesofIndia	News	India	9	180	1838	3	3.8

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