210.ANALYZING DIFFERENT TYPES OF IPS TOPPERS BY BACKGROUND STATE RANK | GENDER | YEAR | SUBJECT | AGE | USING | PROC SQL | PROC MEANS | PROC FREQ | PROC PRINT | PROC SORT | PROC FORMAT | PROC MACRO | DATA STEP IN SAS

ANALYZING DIFFERENT TYPES OF IPS TOPPERS BY BACKGROUND STATE RANK | GENDER | YEAR | SUBJECT | AGE | USING | PROC SQL | PROC MEANS | PROC FREQ | PROC PRINT | PROC SORT | PROC FORMAT | PROC MACRO | DATA STEP IN SAS

/*Creating a simulated SAS dataset of IPS toppers with information on their rank, educational background, state, gender, and optional subjects*/

Step 1: Dataset Creation — IPS_Toppers

data IPS_Toppers;

    length Topper_ID 8 Name $20 Gender $6 State $15 Background $20 

           Optional_Subject $25 Year 8 Rank 8 Age 8;

    input Topper_ID Name $ Gender $ State $ Background $ 

          Optional_Subject $ Year Rank Age;

    datalines;

1 Aarav M Delhi Engineering PSIR 2020 1 26

2 Sneha F Maharashtra Medical Sociology 2020 2 25

3 Raj M TamilNadu Arts Geography 2021 3 27

4 Kavya F Kerala Engineering Anthropology 2021 4 24

5 Aditya M UP Commerce PSIR 2021 5 28

6 Meera F Karnataka Arts History 2022 6 25

7 Rohit M Punjab Law PSIR 2022 7 26

8 Priya F Haryana Engineering Geography 2022 8 24

9 Arjun M Gujarat Science Sociology 2023 9 27

10 Isha F MP Commerce Anthropology 2023 10 23

11 Dev M Rajasthan Engineering Geography 2023 11 28

12 Pooja F WB Medical PSIR 2023 12 25

13 Karan M Bihar Arts History 2023 13 27

14 Tanya F Jharkhand Science Sociology 2024 14 24

15 Rakesh M AP Engineering Anthropology 2024 15 29

16 Divya F Telangana Arts Geography 2024 16 26

17 Yash M Odisha Law PSIR 2024 17 25

;

run;

proc print;run;

Output:

Obs	Topper_ID	Name	Gender	State	Background	Optional_Subject	Year	Rank	Age
1	1	Aarav	M	Delhi	Engineering	PSIR	2020	1	26
2	2	Sneha	F	Maharashtra	Medical	Sociology	2020	2	25
3	3	Raj	M	TamilNadu	Arts	Geography	2021	3	27
4	4	Kavya	F	Kerala	Engineering	Anthropology	2021	4	24
5	5	Aditya	M	UP	Commerce	PSIR	2021	5	28
6	6	Meera	F	Karnataka	Arts	History	2022	6	25
7	7	Rohit	M	Punjab	Law	PSIR	2022	7	26
8	8	Priya	F	Haryana	Engineering	Geography	2022	8	24
9	9	Arjun	M	Gujarat	Science	Sociology	2023	9	27
10	10	Isha	F	MP	Commerce	Anthropology	2023	10	23
11	11	Dev	M	Rajasthan	Engineering	Geography	2023	11	28
12	12	Pooja	F	WB	Medical	PSIR	2023	12	25
13	13	Karan	M	Bihar	Arts	History	2023	13	27
14	14	Tanya	F	Jharkhand	Science	Sociology	2024	14	24
15	15	Rakesh	M	AP	Engineering	Anthropology	2024	15	29
16	16	Divya	F	Telangana	Arts	Geography	2024	16	26
17	17	Yash	M	Odisha	Law	PSIR	2024	17	25

Step 2: Basic Data Inspection Using PROC PRINT

proc print data=IPS_Toppers;

    title "List of IPS Toppers with Full Details";

run;

Output:

List of IPS Toppers with Full Details

Obs	Topper_ID	Name	Gender	State	Background	Optional_Subject	Year	Rank	Age
1	1	Aarav	M	Delhi	Engineering	PSIR	2020	1	26
2	2	Sneha	F	Maharashtra	Medical	Sociology	2020	2	25
3	3	Raj	M	TamilNadu	Arts	Geography	2021	3	27
4	4	Kavya	F	Kerala	Engineering	Anthropology	2021	4	24
5	5	Aditya	M	UP	Commerce	PSIR	2021	5	28
6	6	Meera	F	Karnataka	Arts	History	2022	6	25
7	7	Rohit	M	Punjab	Law	PSIR	2022	7	26
8	8	Priya	F	Haryana	Engineering	Geography	2022	8	24
9	9	Arjun	M	Gujarat	Science	Sociology	2023	9	27
10	10	Isha	F	MP	Commerce	Anthropology	2023	10	23
11	11	Dev	M	Rajasthan	Engineering	Geography	2023	11	28
12	12	Pooja	F	WB	Medical	PSIR	2023	12	25
13	13	Karan	M	Bihar	Arts	History	2023	13	27
14	14	Tanya	F	Jharkhand	Science	Sociology	2024	14	24
15	15	Rakesh	M	AP	Engineering	Anthropology	2024	15	29
16	16	Divya	F	Telangana	Arts	Geography	2024	16	26
17	17	Yash	M	Odisha	Law	PSIR	2024	17	25

Step 3: Summary Statistics Using PROC MEANS

proc means data=IPS_Toppers n mean min max;

    var Rank Age;

    title "Summary Statistics of Rank and Age of IPS Toppers";

run;

Output:

Summary Statistics of Rank and Age of IPS Toppers

The MEANS Procedure

Variable	N	Mean	Minimum	Maximum
Rank Age	17 17	9.0000000 25.8235294	1.0000000 23.0000000	17.0000000 29.0000000

Step 4: Frequency Distribution Using PROC FREQ

proc freq data=IPS_Toppers;

    tables Gender State Background Optional_Subject;

    title "Frequency of IPS Toppers by Various Categorical Variables";

run;

Output:

Frequency of IPS Toppers by Various Categorical Variables

The FREQ Procedure

Gender	Frequency	Percent	Cumulative Frequency	Cumulative Percent
F	8	47.06	8	47.06
M	9	52.94	17	100.00

State	Frequency	Percent	Cumulative Frequency	Cumulative Percent
AP	1	5.88	1	5.88
Bihar	1	5.88	2	11.76
Delhi	1	5.88	3	17.65
Gujarat	1	5.88	4	23.53
Haryana	1	5.88	5	29.41
Jharkhand	1	5.88	6	35.29
Karnataka	1	5.88	7	41.18
Kerala	1	5.88	8	47.06
MP	1	5.88	9	52.94
Maharashtra	1	5.88	10	58.82
Odisha	1	5.88	11	64.71
Punjab	1	5.88	12	70.59
Rajasthan	1	5.88	13	76.47
TamilNadu	1	5.88	14	82.35
Telangana	1	5.88	15	88.24
UP	1	5.88	16	94.12
WB	1	5.88	17	100.00

Background	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Arts	4	23.53	4	23.53
Commerce	2	11.76	6	35.29
Engineering	5	29.41	11	64.71
Law	2	11.76	13	76.47
Medical	2	11.76	15	88.24
Science	2	11.76	17	100.00

Optional_Subject	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Anthropology	3	17.65	3	17.65
Geography	4	23.53	7	41.18
History	2	11.76	9	52.94
PSIR	5	29.41	14	82.35
Sociology	3	17.65	17	100.00

Step 5: Using PROC SQL to Analyze Trends

5.1: Top States Producing Toppers

proc sql;

    create table Top_States as

    select State, count(*) as No_of_Toppers

    from IPS_Toppers

    group by State

    order by No_of_Toppers desc;

quit;

proc print data=Top_States;

    title "Top States by Number of IPS Toppers";

run;

Output:

Top States by Number of IPS Toppers

Obs	State	No_of_Toppers
1	Punjab	1
2	Maharashtra	1
3	Jharkhand	1
4	TamilNadu	1
5	MP	1
6	Haryana	1
7	Odisha	1
8	Delhi	1
9	Rajasthan	1
10	Karnataka	1
11	Telangana	1
12	WB	1
13	AP	1
14	Bihar	1
15	Gujarat	1
16	Kerala	1
17	UP	1

5.2: Most Common Optional Subjects

proc sql;

    create table Popular_Subjects as

    select Optional_Subject, count(*) as Frequency

    from IPS_Toppers

    group by Optional_Subject

    order by Frequency desc;

quit;

proc print data=Popular_Subjects;

    title "Most Preferred Optional Subjects";

run;

Output:

Most Preferred Optional Subjects

Obs	Optional_Subject	Frequency
1	PSIR	5
2	Geography	4
3	Anthropology	3
4	Sociology	3
5	History	2

5.3: Average Rank by Background

proc sql;

    create table Rank_By_Background as

    select Background, avg(Rank) as Avg_Rank format=8.2

    from IPS_Toppers

    group by Background;

quit;

proc print data=Rank_By_Background;

    title "Average Rank Based on Background";

run;

Output:

Average Rank Based on Background

Obs	Background	Avg_Rank
1	Arts	9.50
2	Commerce	7.50
3	Engineering	7.80
4	Law	12.00
5	Medical	7.00
6	Science	11.50

Step 6: Create Macro for Dynamic Filtering

Define Macro: %TopperByState

%macro TopperByState(state);

    proc sql;

        create table Toppers_&state as

        select * 

        from IPS_Toppers

        where upcase(State) = upcase("&state");

    quit;

    proc print data=Toppers_&state;

        title "Toppers from &state";

    run;

%mend;

%TopperByState(Karnataka)

Output:

Toppers from Karnataka

Obs	Topper_ID	Name	Gender	State	Background	Optional_Subject	Year	Rank	Age
1	6	Meera	F	Karnataka	Arts	History	2022	6	25

%TopperByState(UP)

Output:

Toppers from UP

Obs	Topper_ID	Name	Gender	State	Background	Optional_Subject	Year	Rank	Age
1	5	Aditya	M	UP	Commerce	PSIR	2021	5	28

%TopperByState(Telangana)

Output:

Toppers from Telangana

Obs	Topper_ID	Name	Gender	State	Background	Optional_Subject	Year	Rank	Age
1	16	Divya	F	Telangana	Arts	Geography	2024	16	26

Step 7: Macro to Analyze Gender Gap

Define Macro: %GenderGap

%macro GenderGap;

    proc sql;

        create table Gender_Stats as

        select Gender, count(*) as Count

        from IPS_Toppers

        group by Gender;

    quit;

    proc print data=Gender_Stats;

        title "Gender Distribution Among IPS Toppers";

    run;

%mend;

%GenderGap;

Output:

Gender Distribution Among IPS Toppers

Obs	Gender	Count
1	F	8
2	M	9

Step 8: Macro Loop to Show Background Trends

Loop Macro: %BackgroundStats

%macro BackgroundStats;

    %let backgrounds = Engineering Medical Arts Commerce Law Science;

    %do i=1 %to 6;

        %let bg = %scan(&backgrounds, &i);

        proc sql;

            create table Stats_&bg as

            select Year, count(*) as Toppers_Count

            from IPS_Toppers

            where Background = "&bg"

            group by Year;

        quit;

        proc print data=Stats_&bg;

            title "Yearly Toppers from &bg Background";

        run;

    %end;

%mend;

%BackgroundStats;

Output:

Yearly Toppers from Engineering Background

Obs	Year	Toppers_Count
1	2020	1
2	2021	1
3	2022	1
4	2023	1
5	2024	1

Yearly Toppers from Medical Background

Obs	Year	Toppers_Count
1	2020	1
2	2023	1

Yearly Toppers from Arts Background

Obs	Year	Toppers_Count
1	2021	1
2	2022	1
3	2023	1
4	2024	1

Yearly Toppers from Commerce Background

Obs	Year	Toppers_Count
1	2021	1
2	2023	1

Yearly Toppers from Law Background

Obs	Year	Toppers_Count
1	2022	1
2	2024	1

Yearly Toppers from Science Background

Obs	Year	Toppers_Count
1	2023	1
2	2024	1

Step 9: Advanced SQL – Multi-Condition Filter

proc sql;

    select Name, Gender, State, Optional_Subject, Rank

    from IPS_Toppers

    where Gender = 'F' and Rank <= 10 and Background = 'Engineering'

    order by Rank;

quit;

Output:

Name	Gender	State	Optional_Subject	Rank
Kavya	F	Kerala	Anthropology	4
Priya	F	Haryana	Geography	8

Step 10: Use of Macro Variables for Dynamic Ranking

%let top_rank = 5;

proc sql;

    create table Top_&top_rank as

    select * from IPS_Toppers

    where Rank <= &top_rank

    order by Rank;

quit;

proc print data=Top_&top_rank;

    title "Top &top_rank IPS Toppers";

run;

Output:

Top 5 IPS Toppers

Obs	Topper_ID	Name	Gender	State	Background	Optional_Subject	Year	Rank	Age
1	1	Aarav	M	Delhi	Engineering	PSIR	2020	1	26
2	2	Sneha	F	Maharashtra	Medical	Sociology	2020	2	25
3	3	Raj	M	TamilNadu	Arts	Geography	2021	3	27
4	4	Kavya	F	Kerala	Engineering	Anthropology	2021	4	24
5	5	Aditya	M	UP	Commerce	PSIR	2021	5	28

Step 11: Export to CSV

proc export data=IPS_Toppers

    outfile="IPS_Toppers.csv"

    dbms=csv

    replace;

run;

Log:

NOTE: The file 'IPS_Toppers.csv' is:

      Filename=C:\sas folder\SASFoundation\9.4\IPS_Toppers.csv,

      RECFM=V,LRECL=32767,File Size (bytes)=0,

      Last Modified=14 June 2025 16:23:04,

      Create Time=14 June 2025 16:23:04

NOTE: 18 records were written to the file 'IPS_Toppers.csv'.

      The minimum record length was 35.

      The maximum record length was 69.

NOTE: There were 17 observations read from the data set WORK.IPS_TOPPERS.

NOTE: DATA statement used (Total process time):

      real time           0.21 seconds

      cpu time            0.06 seconds

17 records created in IPS_Toppers.csv from IPS_TOPPERS.

NOTE: "IPS_Toppers.csv" file was successfully created.

NOTE: PROCEDURE EXPORT used (Total process time):

      real time           2.67 seconds

      cpu time            0.23 seconds

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