246.IN-DEPTH DEMOGRAPHIC AND SOCIOECONOMIC ANALYSIS OF 100+ LIVE INDIAN CITIZENS USING SAS PROCEDURES: PROC PRINT | PROC SORT | PROC FREQ | PROC MEANS | PROC SQL | ADVANCED MACROS FOR REAL-WORLD DATA INSIGHTS

IN-DEPTH DEMOGRAPHIC AND SOCIOECONOMIC ANALYSIS OF 100+ LIVE INDIAN CITIZENS USING SAS PROCEDURES: PROC PRINT | PROC SORT | PROC FREQ | PROC MEANS | PROC SQL | ADVANCED MACROS FOR REAL-WORLD DATA INSIGHTS

/* Creating and analyzing data of  live persons from India */

Step 1: Dataset Creation

options nocenter;

data Indian_Live_People;

    length Name $15 Gender $6 City $15 State $15 Region $12 Occupation $15 MaritalStatus $10 Education $20 Language $10;

    do PersonID = 1 to 20;

        Name = catt('Person', put(PersonID, 3.));

        Age = 18 + int(ranuni(0)*50); /* Ages 18 to 68 */

        Gender = scan('Male Female Other', ceil(ranuni(0)*3));

        City = scan('Hyderabad Chennai Mumbai Delhi Kolkata Bangalore Jaipur Lucknow Patna Bhopal', ceil(ranuni(0)*10));

        State = scan('Telangana TamilNadu Maharashtra Delhi WestBengal Karnataka Rajasthan UP Bihar MP', ceil(ranuni(0)*10));

        Region = scan('South South West North East South West North East Central', ceil(ranuni(0)*10));

        Occupation = scan('Engineer Doctor Farmer Student Artist Driver Teacher Lawyer Accountant Police', ceil(ranuni(0)*10));

        Income = 8000 + int(ranuni(0)*92000); /* Range: 8000 to 100000 */

        MaritalStatus = scan('Single Married Divorced Widowed', ceil(ranuni(0)*4));

        Education = scan('10th 12th BSc MSc BTech MTech MBBS MBA PhD Diploma', ceil(ranuni(0)*10));

        Language = scan('Hindi Telugu Tamil Bengali Kannada Malayalam Marathi Urdu Punjabi Assamese', ceil(ranuni(0)*10));

        output;

    end;

run;

proc print;run;

Output:

Obs	Name	Gender	City	State	Region	Occupation	MaritalStatus	Education	Language	PersonID	Age	Income
1	Person 1	Male	Patna	Telangana	East	Police	Married	MTech	Bengali	1	28	38265
2	Person 2	Male	Hyderabad	MP	West	Lawyer	Divorced	10th	Tamil	2	20	72950
3	Person 3	Other	Bangalore	MP	South	Artist	Married	BTech	Malayalam	3	35	20778
4	Person 4	Female	Chennai	UP	East	Student	Married	MTech	Malayalam	4	22	77156
5	Person 5	Other	Patna	Bihar	West	Accountant	Single	12th	Telugu	5	63	46538
6	Person 6	Female	Mumbai	Rajasthan	South	Artist	Single	MSc	Bengali	6	43	17816
7	Person 7	Other	Patna	UP	East	Driver	Single	12th	Bengali	7	43	56047
8	Person 8	Male	Lucknow	Delhi	East	Police	Widowed	BSc	Telugu	8	49	42725
9	Person 9	Other	Lucknow	Bihar	West	Driver	Married	PhD	Assamese	9	50	21427
10	Person 10	Male	Lucknow	TamilNadu	South	Teacher	Divorced	Diploma	Punjabi	10	48	57333
11	Person 11	Male	Delhi	Maharashtra	West	Engineer	Divorced	12th	Kannada	11	44	18315
12	Person 12	Male	Bhopal	Telangana	Central	Artist	Widowed	MSc	Malayalam	12	61	30434
13	Person 13	Male	Kolkata	WestBengal	Central	Police	Divorced	MBA	Bengali	13	33	18839
14	Person 14	Female	Hyderabad	Telangana	Central	Doctor	Divorced	BSc	Telugu	14	56	80211
15	Person 15	Other	Patna	Delhi	South	Engineer	Widowed	12th	Urdu	15	66	83725
16	Person 16	Male	Patna	Karnataka	West	Farmer	Divorced	BTech	Kannada	16	53	66285
17	Person 17	Other	Jaipur	Delhi	East	Farmer	Divorced	MTech	Malayalam	17	67	29173
18	Person 18	Female	Chennai	Delhi	East	Farmer	Widowed	10th	Telugu	18	65	29256
19	Person 19	Female	Hyderabad	MP	South	Police	Married	Diploma	Malayalam	19	63	14395
20	Person 20	Male	Kolkata	Bihar	North	Farmer	Single	MSc	Hindi	20	67	31117

Step 2: View the Raw Dataset

proc print data=Indian_Live_People (obs=25);

    title "Sample of Live Persons Dataset in India";

run;

Output:

Sample of Live Persons Dataset in India

Obs	Name	Gender	City	State	Region	Occupation	MaritalStatus	Education	Language	PersonID	Age	Income
1	Person 1	Male	Patna	Telangana	East	Police	Married	MTech	Bengali	1	28	38265
2	Person 2	Male	Hyderabad	MP	West	Lawyer	Divorced	10th	Tamil	2	20	72950
3	Person 3	Other	Bangalore	MP	South	Artist	Married	BTech	Malayalam	3	35	20778
4	Person 4	Female	Chennai	UP	East	Student	Married	MTech	Malayalam	4	22	77156
5	Person 5	Other	Patna	Bihar	West	Accountant	Single	12th	Telugu	5	63	46538
6	Person 6	Female	Mumbai	Rajasthan	South	Artist	Single	MSc	Bengali	6	43	17816
7	Person 7	Other	Patna	UP	East	Driver	Single	12th	Bengali	7	43	56047
8	Person 8	Male	Lucknow	Delhi	East	Police	Widowed	BSc	Telugu	8	49	42725
9	Person 9	Other	Lucknow	Bihar	West	Driver	Married	PhD	Assamese	9	50	21427
10	Person 10	Male	Lucknow	TamilNadu	South	Teacher	Divorced	Diploma	Punjabi	10	48	57333
11	Person 11	Male	Delhi	Maharashtra	West	Engineer	Divorced	12th	Kannada	11	44	18315
12	Person 12	Male	Bhopal	Telangana	Central	Artist	Widowed	MSc	Malayalam	12	61	30434
13	Person 13	Male	Kolkata	WestBengal	Central	Police	Divorced	MBA	Bengali	13	33	18839
14	Person 14	Female	Hyderabad	Telangana	Central	Doctor	Divorced	BSc	Telugu	14	56	80211
15	Person 15	Other	Patna	Delhi	South	Engineer	Widowed	12th	Urdu	15	66	83725
16	Person 16	Male	Patna	Karnataka	West	Farmer	Divorced	BTech	Kannada	16	53	66285
17	Person 17	Other	Jaipur	Delhi	East	Farmer	Divorced	MTech	Malayalam	17	67	29173
18	Person 18	Female	Chennai	Delhi	East	Farmer	Widowed	10th	Telugu	18	65	29256
19	Person 19	Female	Hyderabad	MP	South	Police	Married	Diploma	Malayalam	19	63	14395
20	Person 20	Male	Kolkata	Bihar	North	Farmer	Single	MSc	Hindi	20	67	31117

Step 3: Frequency Analysis

proc freq data=Indian_Live_People;

    tables Gender Region Occupation MaritalStatus / nocum nopercent;

    title "Frequency Distribution of Categorical Variables";

run;

Output:

Frequency Distribution of Categorical Variables

The FREQ Procedure

Gender	Frequency
Female	5
Male	9
Other	6

Region	Frequency
Central	3
East	6
North	1
South	5
West	5

Occupation	Frequency
Accountant	1
Artist	3
Doctor	1
Driver	2
Engineer	2
Farmer	4
Lawyer	1
Police	4
Student	1
Teacher	1

MaritalStatus	Frequency
Divorced	7
Married	5
Single	4
Widowed	4

Step 4: Summary Statistics Using PROC MEANS

proc means data=Indian_Live_People n mean min max std;

    var Age Income;

    title "Summary Statistics for Age and Income";

run;

Output:

Summary Statistics for Age and Income

The MEANS Procedure

Variable	N	Mean	Minimum	Maximum	Std Dev
Age Income	20 20	48.8000000 42639.25	20.0000000 14395.00	67.0000000 83725.00	15.1400480 23329.52

Step 5: Sorting and Filtering Using PROC SORT

proc sort data=Indian_Live_People out=SortedPeople;

    by descending Income;

run;

proc print data=SortedPeople (obs=10);

    title "Top 10 Earners in the Dataset";

run;

Output:

Top 10 Earners in the Dataset

Obs	Name	Gender	City	State	Region	Occupation	MaritalStatus	Education	Language	PersonID	Age	Income
1	Person 15	Other	Patna	Delhi	South	Engineer	Widowed	12th	Urdu	15	66	83725
2	Person 14	Female	Hyderabad	Telangana	Central	Doctor	Divorced	BSc	Telugu	14	56	80211
3	Person 4	Female	Chennai	UP	East	Student	Married	MTech	Malayalam	4	22	77156
4	Person 2	Male	Hyderabad	MP	West	Lawyer	Divorced	10th	Tamil	2	20	72950
5	Person 16	Male	Patna	Karnataka	West	Farmer	Divorced	BTech	Kannada	16	53	66285
6	Person 10	Male	Lucknow	TamilNadu	South	Teacher	Divorced	Diploma	Punjabi	10	48	57333
7	Person 7	Other	Patna	UP	East	Driver	Single	12th	Bengali	7	43	56047
8	Person 5	Other	Patna	Bihar	West	Accountant	Single	12th	Telugu	5	63	46538
9	Person 8	Male	Lucknow	Delhi	East	Police	Widowed	BSc	Telugu	8	49	42725
10	Person 1	Male	Patna	Telangana	East	Police	Married	MTech	Bengali	1	28	38265

Step 6: Data Selection Using PROC SQL

proc sql;

    select Name, Age, Income, Occupation, State

    from Indian_Live_People

    where Income > 50000 and Age between 25 and 45

    order by Income desc;

quit;

Output:

Name	Age	Income	Occupation	State
Person 7	43	56047	Driver	UP

Step 7: Macro for Regional Summary

%macro RegionStats(region_name);

    title "Region-wise Summary for &region_name Region";

    proc means data=Indian_Live_People noprint;

        where Region = "&region_name";

        var Age Income;

        output out=RegionSummary_&region_name mean=AvgAge AvgIncome;

    run;

    proc print data=RegionSummary_&region_name;

    run;

%mend RegionStats;

%RegionStats(South)

Output:

Region-wise Summary for South Region

Obs	_TYPE_	_FREQ_	AvgAge	AvgIncome
1	0	5	51	38809.4

%RegionStats(North)

Output:

Region-wise Summary for North Region

Obs	_TYPE_	_FREQ_	AvgAge	AvgIncome
1	0	1	67	31117

%RegionStats(West)

Output:

Region-wise Summary for West Region

Obs	_TYPE_	_FREQ_	AvgAge	AvgIncome
1	0	5	46	45103

Step 8: Group Analysis Using PROC SQL

proc sql;

    select Occupation, Gender, count(*) as Count, avg(Income) as Avg_Income

    from Indian_Live_People

    group by Occupation, Gender

    order by Avg_Income desc;

quit;

Output:

Occupation	Gender	Count	Avg_Income
Engineer	Other	1	83725
Doctor	Female	1	80211
Student	Female	1	77156
Lawyer	Male	1	72950
Teacher	Male	1	57333
Farmer	Male	2	48701
Accountant	Other	1	46538
Driver	Other	2	38737
Police	Male	3	33276.33
Artist	Male	1	30434
Farmer	Female	1	29256
Farmer	Other	1	29173
Artist	Other	1	20778
Engineer	Male	1	18315
Artist	Female	1	17816
Police	Female	1	14395

Step 9: Income Distribution Buckets

data Buckets;

    set Indian_Live_People;

    length IncomeBracket $15;

    if Income < 20000 then IncomeBracket = 'Low';

    else if Income < 50000 then IncomeBracket = 'Middle';

    else if Income < 80000 then IncomeBracket = 'High';

    else IncomeBracket = 'Very High';

run;

proc print;run;

Output:

Obs	Name	Gender	City	State	Region	Occupation	MaritalStatus	Education	Language	PersonID	Age	Income	IncomeBracket
1	Person 1	Male	Patna	Telangana	East	Police	Married	MTech	Bengali	1	28	38265	Middle
2	Person 2	Male	Hyderabad	MP	West	Lawyer	Divorced	10th	Tamil	2	20	72950	High
3	Person 3	Other	Bangalore	MP	South	Artist	Married	BTech	Malayalam	3	35	20778	Middle
4	Person 4	Female	Chennai	UP	East	Student	Married	MTech	Malayalam	4	22	77156	High
5	Person 5	Other	Patna	Bihar	West	Accountant	Single	12th	Telugu	5	63	46538	Middle
6	Person 6	Female	Mumbai	Rajasthan	South	Artist	Single	MSc	Bengali	6	43	17816	Low
7	Person 7	Other	Patna	UP	East	Driver	Single	12th	Bengali	7	43	56047	High
8	Person 8	Male	Lucknow	Delhi	East	Police	Widowed	BSc	Telugu	8	49	42725	Middle
9	Person 9	Other	Lucknow	Bihar	West	Driver	Married	PhD	Assamese	9	50	21427	Middle
10	Person 10	Male	Lucknow	TamilNadu	South	Teacher	Divorced	Diploma	Punjabi	10	48	57333	High
11	Person 11	Male	Delhi	Maharashtra	West	Engineer	Divorced	12th	Kannada	11	44	18315	Low
12	Person 12	Male	Bhopal	Telangana	Central	Artist	Widowed	MSc	Malayalam	12	61	30434	Middle
13	Person 13	Male	Kolkata	WestBengal	Central	Police	Divorced	MBA	Bengali	13	33	18839	Low
14	Person 14	Female	Hyderabad	Telangana	Central	Doctor	Divorced	BSc	Telugu	14	56	80211	Very High
15	Person 15	Other	Patna	Delhi	South	Engineer	Widowed	12th	Urdu	15	66	83725	Very High
16	Person 16	Male	Patna	Karnataka	West	Farmer	Divorced	BTech	Kannada	16	53	66285	High
17	Person 17	Other	Jaipur	Delhi	East	Farmer	Divorced	MTech	Malayalam	17	67	29173	Middle
18	Person 18	Female	Chennai	Delhi	East	Farmer	Widowed	10th	Telugu	18	65	29256	Middle
19	Person 19	Female	Hyderabad	MP	South	Police	Married	Diploma	Malayalam	19	63	14395	Low
20	Person 20	Male	Kolkata	Bihar	North	Farmer	Single	MSc	Hindi	20	67	31117	Middle

proc freq data=Buckets;

    tables IncomeBracket;

    title "Income Bracket Distribution";

run;

Output:

Income Bracket Distribution

The FREQ Procedure

IncomeBracket	Frequency	Percent	Cumulative Frequency	Cumulative Percent
High	5	25.00	5	25.00
Low	4	20.00	9	45.00
Middle	9	45.00	18	90.00
Very High	2	10.00	20	100.00

Step 10: Educational Background vs. Income

proc sql;

    select Education, count(*) as People, avg(Income) as AverageIncome

    from Indian_Live_People

    group by Education

    order by AverageIncome desc;

quit;

Output:

Education	People	AverageIncome
BSc	2	61468
12th	4	51156.25
10th	2	51103
MTech	3	48198
BTech	2	43531.5
Diploma	2	35864
MSc	3	26455.67
PhD	1	21427
MBA	1	18839

Step 11: Custom Macro for Filtering

%macro FilterPeople(min_income=30000, min_age=25);

    proc sql;

        select Name, Occupation, Age, Income

        from Indian_Live_People

        where Income >= &min_income and Age >= &min_age

        order by Income desc;

    quit;

%mend;

%FilterPeople(min_income=50000, min_age=30)

Output:

Name	Occupation	Age	Income
Person 15	Engineer	66	83725
Person 14	Doctor	56	80211
Person 16	Farmer	53	66285
Person 10	Teacher	48	57333
Person 7	Driver	43	56047

Step 12: Frequency by City Using PROC FREQ

proc freq data=Indian_Live_People;

    tables City;

    title "City-wise Count of Individuals";

run;

Output:

City-wise Count of Individuals

The FREQ Procedure

City	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Bangalore	1	5.00	1	5.00
Bhopal	1	5.00	2	10.00
Chennai	2	10.00	4	20.00
Delhi	1	5.00	5	25.00
Hyderabad	3	15.00	8	40.00
Jaipur	1	5.00	9	45.00
Kolkata	2	10.00	11	55.00
Lucknow	3	15.00	14	70.00
Mumbai	1	5.00	15	75.00
Patna	5	25.00	20	100.00

To Visit My Previous Proc  Means And Nway Option:Click Here

To Visit My Previous Proc Means And CharType Option:Click Here

To Visit My Previous SAS Functions:Click Here

To Visit My Previous Length Statement Using In Many Ways:Click Here

--->PLEASE FOLLOW OUR BLOG FOR MORE INFORMATION.

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

PLEASE FOLLOW OUR TELEGRAM CHANNEL CLICK HERE

PLEASE FOLLOW OUR FACEBOOK PAGE  CLICK HERE

PLEASE FOLLOW OUR INSTAGRAM PAGE CLICK HERE