388.From Booth to Ballot: A Complete SAS Analytics System for Election Monitoring

From Booth to Ballot: A Complete SAS Analytics System for Election Monitoring

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HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —
DATA STEP | PROC SQL |  PROC PRINT | PROC SGPLOT | MACROS | PROC CORR | PROC MEANS | PROC FREQ | PROC UNIVARIATE | APPEND | PROC SORT | MERGE | PROC DATASETS DELETE | DATA FUNCTIONS

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INTRODUCTION

In modern elections, managing voting booths efficiently is very important to ensure fair and smooth voting. Election authorities need to know how many voters are registered, how many actually voted, how much time voters waited, and whether there were any equipment problems at each booth.

This project is created to analyze voting booth data using SAS. The main aim is to understand voter turnout, identify poorly performing booths, detect possible fraud, and support better planning for future elections.

By using different SAS procedures like PROC SQL, PROC MEANS, PROC FREQ, PROC UNIVARIATE, PROC CORR, and PROC SGPLOT, along with macros and date functions, this project shows how real election data can be cleaned, analyzed, and converted into useful business insights for decision making.

The final outcome of this project helps election officials improve voter experience, reduce waiting time, prevent misuse of voting systems, and increase public trust in the election process.

Table Of Contents

1. Business Context
2. Project Contents
3. Dataset Creation
4. Data Enrichment
5. PROC SQL Analytics
6. PROC FREQ / MEANS / UNIVARIATE
7. Correlation Analysis
8. Visualization (SGPLOT)
9. Utilization Classification Macro
10. Fraud Detection Macro
11. Advanced Date Logic
12. Transpose / Merge / Append
13. Character & Numeric Functions
14. PROC DATASETS Cleanup
15. Final Business Interpretation

1. BUSINESS CONTEXT

Election commissions want to monitor voting booth performance to ensure:

• Voter turnout is healthy
• Waiting times are reasonable
• Equipment issues are minimal
• Fraud or abnormal behavior is detected
• Booth utilization is optimized

This project simulates a national election monitoring system where data analysts use SAS to:

• Track turnout trends
• Classify booth utilization
• Detect suspicious booths
• Produce management dashboards

2. PROJECT CONTENTS

Here We Use:

• PROC SQL – data creation & analytics
• PROC MEANS / UNIVARIATE – statistics
• PROC FREQ – categorical analysis
• PROC CORR – relationships
• PROC SGPLOT – visualization
• Macros – utilization & fraud logic
• Date functions – MDY, INTCK, INTNX
• Character functions – STRIP, CATX, PROPCASE
• Numeric functions – ROUND, CEIL, FLOOR
• SET / MERGE / APPEND / TRANSPOSE
• PROC DATASETS DELETE – cleanup

3. DATASET CREATION

data voting_booths;

    input Booth_ID $ Region $ Registered_Voters Votes_Cast 

          Waiting_Time Equipment_Issues Election_Date :date9.;

    format Election_Date date9.;

 datalines;

B001 North 1200 950 18 2 01JAN2025

B002 North 1500 1450 25 5 01JAN2025

B003 South 900 870 12 1 01JAN2025

B004 South 1100 600 40 7 01JAN2025

B005 East 1300 1250 15 0 01JAN2025

B006 East 1400 900 35 6 01JAN2025

B007 West 1000 980 10 0 01JAN2025

B008 West 1600 800 50 9 01JAN2025

B009 Central 1800 1700 20 2 01JAN2025

B010 Central 2000 1950 22 1 01JAN2025

B011 North 1700 800 55 10 01JAN2025

B012 South 950 930 14 0 01JAN2025

B013 East 1250 400 60 12 01JAN2025

B014 West 1400 1350 18 1 01JAN2025

B015 Central 1600 1580 16 0 01JAN2025

B016 North 1000 990 11 0 01JAN2025

;

run;

proc print data=voting_booths;

run;

OUTPUT:

Obs	Booth_ID	Region	Registered_Voters	Votes_Cast	Waiting_Time	Equipment_Issues	Election_Date
1	B001	North	1200	950	18	2	01JAN2025
2	B002	North	1500	1450	25	5	01JAN2025
3	B003	South	900	870	12	1	01JAN2025
4	B004	South	1100	600	40	7	01JAN2025
5	B005	East	1300	1250	15	0	01JAN2025
6	B006	East	1400	900	35	6	01JAN2025
7	B007	West	1000	980	10	0	01JAN2025
8	B008	West	1600	800	50	9	01JAN2025
9	B009	Central	1800	1700	20	2	01JAN2025
10	B010	Central	2000	1950	22	1	01JAN2025
11	B011	North	1700	800	55	10	01JAN2025
12	B012	South	950	930	14	0	01JAN2025
13	B013	East	1250	400	60	12	01JAN2025
14	B014	West	1400	1350	18	1	01JAN2025
15	B015	Central	1600	1580	16	0	01JAN2025
16	B016	North	1000	990	11	0	01JAN2025

4. DATA ENRICHMENT

data voting_enriched;

    set voting_booths;

    Turnout_Rate = round((Votes_Cast/Registered_Voters)*100, 0.01);

    Booth_Label = catx('-', strip(Region), Booth_ID);

run;

proc print data=voting_enriched;

run;

OUTPUT:

Obs	Booth_ID	Region	Registered_Voters	Votes_Cast	Waiting_Time	Equipment_Issues	Election_Date	Turnout_Rate	Booth_Label
1	B001	North	1200	950	18	2	01JAN2025	79.17	North-B001
2	B002	North	1500	1450	25	5	01JAN2025	96.67	North-B002
3	B003	South	900	870	12	1	01JAN2025	96.67	South-B003
4	B004	South	1100	600	40	7	01JAN2025	54.55	South-B004
5	B005	East	1300	1250	15	0	01JAN2025	96.15	East-B005
6	B006	East	1400	900	35	6	01JAN2025	64.29	East-B006
7	B007	West	1000	980	10	0	01JAN2025	98.00	West-B007
8	B008	West	1600	800	50	9	01JAN2025	50.00	West-B008
9	B009	Central	1800	1700	20	2	01JAN2025	94.44	Central-B009
10	B010	Central	2000	1950	22	1	01JAN2025	97.50	Central-B010
11	B011	North	1700	800	55	10	01JAN2025	47.06	North-B011
12	B012	South	950	930	14	0	01JAN2025	97.89	South-B012
13	B013	East	1250	400	60	12	01JAN2025	32.00	East-B013
14	B014	West	1400	1350	18	1	01JAN2025	96.43	West-B014
15	B015	Central	1600	1580	16	0	01JAN2025	98.75	Central-B015
16	B016	North	1000	990	11	0	01JAN2025	99.00	North-B016

5. PROC SQL ANALYTICS

proc sql;

    create table region_summary as

    select Region,

           count(*) as Booth_Count,

           avg(Turnout_Rate) as Avg_Turnout,

           avg(Waiting_Time) as Avg_Wait,

           sum(Equipment_Issues) as Total_Issues

    from voting_enriched

    group by Region;

quit;

proc print data=region_summary;

run;

OUTPUT:

Obs	Region	Booth_Count	Avg_Turnout	Avg_Wait	Total_Issues
1	Central	3	96.8967	19.3333	3
2	East	3	64.1467	36.6667	18
3	North	4	80.4750	27.2500	17
4	South	3	83.0367	22.0000	8
5	West	3	81.4767	26.0000	10

6. PROC FREQ

proc freq data=voting_enriched;

    tables Region Equipment_Issues;

run;

OUTPUT:

The FREQ Procedure

Region	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Central	3	18.75	3	18.75
East	3	18.75	6	37.50
North	4	25.00	10	62.50
South	3	18.75	13	81.25
West	3	18.75	16	100.00

Equipment_Issues	Frequency	Percent	Cumulative Frequency	Cumulative Percent
0	5	31.25	5	31.25
1	3	18.75	8	50.00
2	2	12.50	10	62.50
5	1	6.25	11	68.75
6	1	6.25	12	75.00
7	1	6.25	13	81.25
9	1	6.25	14	87.50
10	1	6.25	15	93.75
12	1	6.25	16	100.00

7. PROC MEANS

proc means data=voting_enriched mean min max std;

    var Votes_Cast Waiting_Time Turnout_Rate;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum	Std Dev
Votes_Cast Waiting_Time Turnout_Rate	1093.75 26.3125000 81.1606250	400.0000000 10.0000000 32.0000000	1950.00 60.0000000 99.0000000	417.1790183 16.4751075 23.2674446

8. PROC UNIVARIATE

proc univariate data=voting_enriched;

    var Turnout_Rate;

    histogram;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Turnout_Rate

Moments
N	16	Sum Weights	16
Mean	81.160625	Sum Observations	1298.57
Std Deviation	23.2674446	Variance	541.37398
Skewness	-1.03252	Kurtosis	-0.5143293
Uncorrected SS	113513.363	Corrected SS	8120.60969
Coeff Variation	28.6683902	Std Error Mean	5.81686116

Basic Statistical Measures
Location		Variability
Mean	81.16063	Std Deviation	23.26744
Median	96.29000	Variance	541.37398
Mode	96.67000	Range	67.00000
		Interquartile Range	38.27500

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	13.95265	Pr > |t|	<.0001
Sign	M	8	Pr >= |M|	<.0001
Signed Rank	S	68	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	99.000
99%	99.000
95%	99.000
90%	98.750
75% Q3	97.695
50% Median	96.290
25% Q1	59.420
10%	47.060
5%	32.000
1%	32.000
0% Min	32.000

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
32.00	13	97.50	10
47.06	11	97.89	12
50.00	8	98.00	7
54.55	4	98.75	15
64.29	6	99.00	16

The UNIVARIATE Procedure

9. CORRELATION

proc corr data=voting_enriched;

    var Waiting_Time Equipment_Issues Turnout_Rate;

run;

OUTPUT:

The CORR Procedure

3 Variables:	Waiting_Time Equipment_Issues Turnout_Rate

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Waiting_Time	16	26.31250	16.47511	421.00000	10.00000	60.00000
Equipment_Issues	16	3.50000	4.08248	56.00000	0	12.00000
Turnout_Rate	16	81.16063	23.26744	1299	32.00000	99.00000

Pearson Correlation Coefficients, N = 16 Prob > |r| under H0: Rho=0
	Waiting_Time	Equipment_Issues	Turnout_Rate
Waiting_Time	1.00000	0.98078 <.0001	-0.95566 <.0001
Equipment_Issues	0.98078 <.0001	1.00000	-0.94863 <.0001
Turnout_Rate	-0.95566 <.0001	-0.94863 <.0001	1.00000

10. VISUALIZATION

proc sgplot data=voting_enriched;

    scatter x=Waiting_Time y=Turnout_Rate;

run;

OUTPUT:

11. UTILIZATION CLASSIFICATION MACRO

%macro utilization;

data voting_util;

    set voting_enriched;

    length Utilization $12.;

    if Turnout_Rate >= 90 then Utilization='High';

    else if Turnout_Rate >= 70 then Utilization='Medium';

    else Utilization='Low';

run;

proc print data=voting_util;

run;

%mend;

%utilization;

OUTPUT:

Obs	Booth_ID	Region	Registered_Voters	Votes_Cast	Waiting_Time	Equipment_Issues	Election_Date	Turnout_Rate	Booth_Label	Utilization
1	B001	North	1200	950	18	2	01JAN2025	79.17	North-B001	Medium
2	B002	North	1500	1450	25	5	01JAN2025	96.67	North-B002	High
3	B003	South	900	870	12	1	01JAN2025	96.67	South-B003	High
4	B004	South	1100	600	40	7	01JAN2025	54.55	South-B004	Low
5	B005	East	1300	1250	15	0	01JAN2025	96.15	East-B005	High
6	B006	East	1400	900	35	6	01JAN2025	64.29	East-B006	Low
7	B007	West	1000	980	10	0	01JAN2025	98.00	West-B007	High
8	B008	West	1600	800	50	9	01JAN2025	50.00	West-B008	Low
9	B009	Central	1800	1700	20	2	01JAN2025	94.44	Central-B009	High
10	B010	Central	2000	1950	22	1	01JAN2025	97.50	Central-B010	High
11	B011	North	1700	800	55	10	01JAN2025	47.06	North-B011	Low
12	B012	South	950	930	14	0	01JAN2025	97.89	South-B012	High
13	B013	East	1250	400	60	12	01JAN2025	32.00	East-B013	Low
14	B014	West	1400	1350	18	1	01JAN2025	96.43	West-B014	High
15	B015	Central	1600	1580	16	0	01JAN2025	98.75	Central-B015	High
16	B016	North	1000	990	11	0	01JAN2025	99.00	North-B016	High

12. FRAUD DETECTION MACRO

%macro fraud_check;

data fraud_flags;

    set voting_util;

    if Turnout_Rate > 98 and Equipment_Issues > 5 then Fraud_Flag='Yes';

    else if Waiting_Time > 50 and Votes_Cast < 500 then Fraud_Flag='Yes';

    else Fraud_Flag='No';

run;

proc print data=fraud_flags;

run;

%mend;

%fraud_check;

OUTPUT:

Obs	Booth_ID	Region	Registered_Voters	Votes_Cast	Waiting_Time	Equipment_Issues	Election_Date	Turnout_Rate	Booth_Label	Utilization	Fraud_Flag
1	B001	North	1200	950	18	2	01JAN2025	79.17	North-B001	Medium	No
2	B002	North	1500	1450	25	5	01JAN2025	96.67	North-B002	High	No
3	B003	South	900	870	12	1	01JAN2025	96.67	South-B003	High	No
4	B004	South	1100	600	40	7	01JAN2025	54.55	South-B004	Low	No
5	B005	East	1300	1250	15	0	01JAN2025	96.15	East-B005	High	No
6	B006	East	1400	900	35	6	01JAN2025	64.29	East-B006	Low	No
7	B007	West	1000	980	10	0	01JAN2025	98.00	West-B007	High	No
8	B008	West	1600	800	50	9	01JAN2025	50.00	West-B008	Low	No
9	B009	Central	1800	1700	20	2	01JAN2025	94.44	Central-B009	High	No
10	B010	Central	2000	1950	22	1	01JAN2025	97.50	Central-B010	High	No
11	B011	North	1700	800	55	10	01JAN2025	47.06	North-B011	Low	No
12	B012	South	950	930	14	0	01JAN2025	97.89	South-B012	High	No
13	B013	East	1250	400	60	12	01JAN2025	32.00	East-B013	Low	Yes
14	B014	West	1400	1350	18	1	01JAN2025	96.43	West-B014	High	No
15	B015	Central	1600	1580	16	0	01JAN2025	98.75	Central-B015	High	No
16	B016	North	1000	990	11	0	01JAN2025	99.00	North-B016	High	No

13. DATE FUNCTIONS

data election_dates;

    set fraud_flags;

    Next_Election = intnx('year', Election_Date, 5);

    Days_Since = intck('day', Election_Date, today());

run;

proc print data=election_dates;

run;

OUTPUT:

Obs	Booth_ID	Region	Registered_Voters	Votes_Cast	Waiting_Time	Equipment_Issues	Election_Date	Turnout_Rate	Booth_Label	Utilization	Fraud_Flag	Next_Election	Days_Since
1	B001	North	1200	950	18	2	01JAN2025	79.17	North-B001	Medium	No	25568	396
2	B002	North	1500	1450	25	5	01JAN2025	96.67	North-B002	High	No	25568	396
3	B003	South	900	870	12	1	01JAN2025	96.67	South-B003	High	No	25568	396
4	B004	South	1100	600	40	7	01JAN2025	54.55	South-B004	Low	No	25568	396
5	B005	East	1300	1250	15	0	01JAN2025	96.15	East-B005	High	No	25568	396
6	B006	East	1400	900	35	6	01JAN2025	64.29	East-B006	Low	No	25568	396
7	B007	West	1000	980	10	0	01JAN2025	98.00	West-B007	High	No	25568	396
8	B008	West	1600	800	50	9	01JAN2025	50.00	West-B008	Low	No	25568	396
9	B009	Central	1800	1700	20	2	01JAN2025	94.44	Central-B009	High	No	25568	396
10	B010	Central	2000	1950	22	1	01JAN2025	97.50	Central-B010	High	No	25568	396
11	B011	North	1700	800	55	10	01JAN2025	47.06	North-B011	Low	No	25568	396
12	B012	South	950	930	14	0	01JAN2025	97.89	South-B012	High	No	25568	396
13	B013	East	1250	400	60	12	01JAN2025	32.00	East-B013	Low	Yes	25568	396
14	B014	West	1400	1350	18	1	01JAN2025	96.43	West-B014	High	No	25568	396
15	B015	Central	1600	1580	16	0	01JAN2025	98.75	Central-B015	High	No	25568	396
16	B016	North	1000	990	11	0	01JAN2025	99.00	North-B016	High	No	25568	396

14. TRANSPOSE

proc transpose data=region_summary out=region_transposed;

    by Region NotSorted;

run;

proc print data=region_transposed;

run;

OUTPUT:

Obs	Region	_NAME_	COL1
1	Central	Booth_Count	3.0000
2	Central	Avg_Turnout	96.8967
3	Central	Avg_Wait	19.3333
4	Central	Total_Issues	3.0000
5	East	Booth_Count	3.0000
6	East	Avg_Turnout	64.1467
7	East	Avg_Wait	36.6667
8	East	Total_Issues	18.0000
9	North	Booth_Count	4.0000
10	North	Avg_Turnout	80.4750
11	North	Avg_Wait	27.2500
12	North	Total_Issues	17.0000
13	South	Booth_Count	3.0000
14	South	Avg_Turnout	83.0367
15	South	Avg_Wait	22.0000
16	South	Total_Issues	8.0000
17	West	Booth_Count	3.0000
18	West	Avg_Turnout	81.4767
19	West	Avg_Wait	26.0000
20	West	Total_Issues	10.0000

15. MERGE & APPEND

proc sort data=voting_util;by Booth_ID;run;

proc print data=voting_util;

run;

OUTPUT:

Obs	Booth_ID	Region	Registered_Voters	Votes_Cast	Waiting_Time	Equipment_Issues	Election_Date	Turnout_Rate	Booth_Label	Utilization
1	B001	North	1200	950	18	2	01JAN2025	79.17	North-B001	Medium
2	B002	North	1500	1450	25	5	01JAN2025	96.67	North-B002	High
3	B003	South	900	870	12	1	01JAN2025	96.67	South-B003	High
4	B004	South	1100	600	40	7	01JAN2025	54.55	South-B004	Low
5	B005	East	1300	1250	15	0	01JAN2025	96.15	East-B005	High
6	B006	East	1400	900	35	6	01JAN2025	64.29	East-B006	Low
7	B007	West	1000	980	10	0	01JAN2025	98.00	West-B007	High
8	B008	West	1600	800	50	9	01JAN2025	50.00	West-B008	Low
9	B009	Central	1800	1700	20	2	01JAN2025	94.44	Central-B009	High
10	B010	Central	2000	1950	22	1	01JAN2025	97.50	Central-B010	High
11	B011	North	1700	800	55	10	01JAN2025	47.06	North-B011	Low
12	B012	South	950	930	14	0	01JAN2025	97.89	South-B012	High
13	B013	East	1250	400	60	12	01JAN2025	32.00	East-B013	Low
14	B014	West	1400	1350	18	1	01JAN2025	96.43	West-B014	High
15	B015	Central	1600	1580	16	0	01JAN2025	98.75	Central-B015	High
16	B016	North	1000	990	11	0	01JAN2025	99.00	North-B016	High

proc sort data=fraud_flags;by Booth_ID;run;

proc print data=fraud_flags;

run;

OUTPUT:

Obs	Booth_ID	Region	Registered_Voters	Votes_Cast	Waiting_Time	Equipment_Issues	Election_Date	Turnout_Rate	Booth_Label	Utilization	Fraud_Flag
1	B001	North	1200	950	18	2	01JAN2025	79.17	North-B001	Medium	No
2	B002	North	1500	1450	25	5	01JAN2025	96.67	North-B002	High	No
3	B003	South	900	870	12	1	01JAN2025	96.67	South-B003	High	No
4	B004	South	1100	600	40	7	01JAN2025	54.55	South-B004	Low	No
5	B005	East	1300	1250	15	0	01JAN2025	96.15	East-B005	High	No
6	B006	East	1400	900	35	6	01JAN2025	64.29	East-B006	Low	No
7	B007	West	1000	980	10	0	01JAN2025	98.00	West-B007	High	No
8	B008	West	1600	800	50	9	01JAN2025	50.00	West-B008	Low	No
9	B009	Central	1800	1700	20	2	01JAN2025	94.44	Central-B009	High	No
10	B010	Central	2000	1950	22	1	01JAN2025	97.50	Central-B010	High	No
11	B011	North	1700	800	55	10	01JAN2025	47.06	North-B011	Low	No
12	B012	South	950	930	14	0	01JAN2025	97.89	South-B012	High	No
13	B013	East	1250	400	60	12	01JAN2025	32.00	East-B013	Low	Yes
14	B014	West	1400	1350	18	1	01JAN2025	96.43	West-B014	High	No
15	B015	Central	1600	1580	16	0	01JAN2025	98.75	Central-B015	High	No
16	B016	North	1000	990	11	0	01JAN2025	99.00	North-B016	High	No

data final_merge;

    merge voting_util 

          fraud_flags;

    by Booth_ID;

run;

proc print data=election_dates;

run;

OUTPUT:

Obs	Booth_ID	Region	Registered_Voters	Votes_Cast	Waiting_Time	Equipment_Issues	Election_Date	Turnout_Rate	Booth_Label	Utilization	Fraud_Flag	Next_Election	Days_Since
1	B001	North	1200	950	18	2	01JAN2025	79.17	North-B001	Medium	No	25568	396
2	B002	North	1500	1450	25	5	01JAN2025	96.67	North-B002	High	No	25568	396
3	B003	South	900	870	12	1	01JAN2025	96.67	South-B003	High	No	25568	396
4	B004	South	1100	600	40	7	01JAN2025	54.55	South-B004	Low	No	25568	396
5	B005	East	1300	1250	15	0	01JAN2025	96.15	East-B005	High	No	25568	396
6	B006	East	1400	900	35	6	01JAN2025	64.29	East-B006	Low	No	25568	396
7	B007	West	1000	980	10	0	01JAN2025	98.00	West-B007	High	No	25568	396
8	B008	West	1600	800	50	9	01JAN2025	50.00	West-B008	Low	No	25568	396
9	B009	Central	1800	1700	20	2	01JAN2025	94.44	Central-B009	High	No	25568	396
10	B010	Central	2000	1950	22	1	01JAN2025	97.50	Central-B010	High	No	25568	396
11	B011	North	1700	800	55	10	01JAN2025	47.06	North-B011	Low	No	25568	396
12	B012	South	950	930	14	0	01JAN2025	97.89	South-B012	High	No	25568	396
13	B013	East	1250	400	60	12	01JAN2025	32.00	East-B013	Low	Yes	25568	396
14	B014	West	1400	1350	18	1	01JAN2025	96.43	West-B014	High	No	25568	396
15	B015	Central	1600	1580	16	0	01JAN2025	98.75	Central-B015	High	No	25568	396
16	B016	North	1000	990	11	0	01JAN2025	99.00	North-B016	High	No	25568	396

proc append base=voting_booths 

            data=fraud_flags(where=(Fraud_Flag='Yes')) force;

run;

proc print data=election_dates;

run;

OUTPUT:

Obs	Booth_ID	Region	Registered_Voters	Votes_Cast	Waiting_Time	Equipment_Issues	Election_Date	Turnout_Rate	Booth_Label	Utilization	Fraud_Flag	Next_Election	Days_Since
1	B001	North	1200	950	18	2	01JAN2025	79.17	North-B001	Medium	No	25568	396
2	B002	North	1500	1450	25	5	01JAN2025	96.67	North-B002	High	No	25568	396
3	B003	South	900	870	12	1	01JAN2025	96.67	South-B003	High	No	25568	396
4	B004	South	1100	600	40	7	01JAN2025	54.55	South-B004	Low	No	25568	396
5	B005	East	1300	1250	15	0	01JAN2025	96.15	East-B005	High	No	25568	396
6	B006	East	1400	900	35	6	01JAN2025	64.29	East-B006	Low	No	25568	396
7	B007	West	1000	980	10	0	01JAN2025	98.00	West-B007	High	No	25568	396
8	B008	West	1600	800	50	9	01JAN2025	50.00	West-B008	Low	No	25568	396
9	B009	Central	1800	1700	20	2	01JAN2025	94.44	Central-B009	High	No	25568	396
10	B010	Central	2000	1950	22	1	01JAN2025	97.50	Central-B010	High	No	25568	396
11	B011	North	1700	800	55	10	01JAN2025	47.06	North-B011	Low	No	25568	396
12	B012	South	950	930	14	0	01JAN2025	97.89	South-B012	High	No	25568	396
13	B013	East	1250	400	60	12	01JAN2025	32.00	East-B013	Low	Yes	25568	396
14	B014	West	1400	1350	18	1	01JAN2025	96.43	West-B014	High	No	25568	396
15	B015	Central	1600	1580	16	0	01JAN2025	98.75	Central-B015	High	No	25568	396
16	B016	North	1000	990	11	0	01JAN2025	99.00	North-B016	High	No	25568	396

16. CHARACTER FUNCTIONS

data char_demo;

    set final_merge;

    Region_Upper = upcase(Region);

    Region_Lower = lowcase(Region);

    Region_Proper = propcase(Region);

run;

proc print data=char_demo;

run;

OUTPUT:

Obs	Booth_ID	Region	Registered_Voters	Votes_Cast	Waiting_Time	Equipment_Issues	Election_Date	Turnout_Rate	Booth_Label	Utilization	Fraud_Flag	Region_Upper	Region_Lower	Region_Proper
1	B001	North	1200	950	18	2	01JAN2025	79.17	North-B001	Medium	No	NORTH	north	North
2	B002	North	1500	1450	25	5	01JAN2025	96.67	North-B002	High	No	NORTH	north	North
3	B003	South	900	870	12	1	01JAN2025	96.67	South-B003	High	No	SOUTH	south	South
4	B004	South	1100	600	40	7	01JAN2025	54.55	South-B004	Low	No	SOUTH	south	South
5	B005	East	1300	1250	15	0	01JAN2025	96.15	East-B005	High	No	EAST	east	East
6	B006	East	1400	900	35	6	01JAN2025	64.29	East-B006	Low	No	EAST	east	East
7	B007	West	1000	980	10	0	01JAN2025	98.00	West-B007	High	No	WEST	west	West
8	B008	West	1600	800	50	9	01JAN2025	50.00	West-B008	Low	No	WEST	west	West
9	B009	Central	1800	1700	20	2	01JAN2025	94.44	Central-B009	High	No	CENTRAL	central	Central
10	B010	Central	2000	1950	22	1	01JAN2025	97.50	Central-B010	High	No	CENTRAL	central	Central
11	B011	North	1700	800	55	10	01JAN2025	47.06	North-B011	Low	No	NORTH	north	North
12	B012	South	950	930	14	0	01JAN2025	97.89	South-B012	High	No	SOUTH	south	South
13	B013	East	1250	400	60	12	01JAN2025	32.00	East-B013	Low	Yes	EAST	east	East
14	B014	West	1400	1350	18	1	01JAN2025	96.43	West-B014	High	No	WEST	west	West
15	B015	Central	1600	1580	16	0	01JAN2025	98.75	Central-B015	High	No	CENTRAL	central	Central
16	B016	North	1000	990	11	0	01JAN2025	99.00	North-B016	High	No	NORTH	north	North

17. NUMERIC FUNCTIONS

data numeric_demo;

    set final_merge;

    Wait_Hours = round(Waiting_Time/60,0.01);

    Issues_Ceil = ceil(Equipment_Issues/2);

run;

proc print data=numeric_demo;

run;

OUTPUT:

Obs	Booth_ID	Region	Registered_Voters	Votes_Cast	Waiting_Time	Equipment_Issues	Election_Date	Turnout_Rate	Booth_Label	Utilization	Fraud_Flag	Wait_Hours	Issues_Ceil
1	B001	North	1200	950	18	2	01JAN2025	79.17	North-B001	Medium	No	0.30	1
2	B002	North	1500	1450	25	5	01JAN2025	96.67	North-B002	High	No	0.42	3
3	B003	South	900	870	12	1	01JAN2025	96.67	South-B003	High	No	0.20	1
4	B004	South	1100	600	40	7	01JAN2025	54.55	South-B004	Low	No	0.67	4
5	B005	East	1300	1250	15	0	01JAN2025	96.15	East-B005	High	No	0.25	0
6	B006	East	1400	900	35	6	01JAN2025	64.29	East-B006	Low	No	0.58	3
7	B007	West	1000	980	10	0	01JAN2025	98.00	West-B007	High	No	0.17	0
8	B008	West	1600	800	50	9	01JAN2025	50.00	West-B008	Low	No	0.83	5
9	B009	Central	1800	1700	20	2	01JAN2025	94.44	Central-B009	High	No	0.33	1
10	B010	Central	2000	1950	22	1	01JAN2025	97.50	Central-B010	High	No	0.37	1
11	B011	North	1700	800	55	10	01JAN2025	47.06	North-B011	Low	No	0.92	5
12	B012	South	950	930	14	0	01JAN2025	97.89	South-B012	High	No	0.23	0
13	B013	East	1250	400	60	12	01JAN2025	32.00	East-B013	Low	Yes	1.00	6
14	B014	West	1400	1350	18	1	01JAN2025	96.43	West-B014	High	No	0.30	1
15	B015	Central	1600	1580	16	0	01JAN2025	98.75	Central-B015	High	No	0.27	0
16	B016	North	1000	990	11	0	01JAN2025	99.00	North-B016	High	No	0.18	0

18. PROC DATASETS DELETE

proc datasets library=work nolist;

    delete char_demo numeric_demo;

quit;

LOG:

NOTE: Deleting WORK.CHAR_DEMO (memtype=DATA).

NOTE: Deleting WORK.NUMERIC_DEMO (memtype=DATA).

FINAL BUSINESS INTERPRETATION

This system allows election authorities to:

1.     Identify high performing booths

2.     Detect fraud-prone locations

3.     Optimize staff allocation

4.     Reduce waiting time

5.     Improve voter satisfaction

6.     Schedule future elections

7.     Generate automated dashboards

THIS PROJECT COVERED

This single project demonstrates:

Skill	Covered
Data Creation	YES
SQL Analytics	YES
Statistics	YES
Visualization	YES
Macros	YES
Fraud Logic	YES
Date Handling	YES
Cleaning	YES
Reporting	YES
Real Business Use	YES

CONCLUSION

This project successfully demonstrates how voting booth data can be analyzed using SAS to improve election management. By studying variables like registered voters, votes cast, waiting time, equipment issues, and turnout rate, we can clearly understand how each booth is performing.

Using different SAS procedures such as PROC SQL, PROC MEANS, PROC FREQ, PROC UNIVARIATE, PROC CORR, and PROC SGPLOT, the data was converted into meaningful reports and visualizations. Macros were used to classify booth utilization and to detect possible fraud, which helps in identifying risky or underperforming booths.

Overall, this project shows that SAS is a powerful tool for election analytics. It helps authorities take better decisions, reduce operational problems, improve voter satisfaction, and ensure a fair and transparent election system.

INTERVIEW QUESTIONS FOR YOU

·  What is the difference between PROC MEANS and PROC SUMMARY?

·  What is PROC FREQ used for?

·  What is PROC UNIVARIATE?

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About the Author:

SAS Learning Hub is a data analytics and SAS programming platform focused on clinical, financial, and real-world data analysis. The content is created by professionals with academic training in Pharmaceutics and hands-on experience in Base SAS, PROC SQL, Macros, SDTM, and ADaM, providing practical and industry-relevant SAS learning resources.

Disclaimer:

The datasets and analysis in this article are created for educational and demonstration purposes only. They do not represent Election data.

Our Mission:

This blog provides industry-focused SAS programming tutorials and analytics projects covering finance, healthcare, and technology.

This project is suitable for:

·  Students learning SAS

·  Data analysts building portfolios

·  Professionals preparing for SAS interviews

·  Bloggers writing about analytics and smart cities

·  EV and energy industry professionals

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