398.Is Your Favorite Restaurant Really Safe? What Do Food Inspection Scores Reveal?A Sas Data Analytics

Is Your Favorite Restaurant Really Safe? What Do Food Inspection Scores Reveal?A Sas Data Analytics

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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 | PROC SORT | MERGE | PROC TRANSPOSE | PROC DATASETS DELETE | DATA FUNCTIONS

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

INTRODUCTION

Food safety inspections play a critical role in public health.
Government bodies inspect restaurants periodically to ensure hygiene, safety, and regulatory compliance.
Poor food safety leads to:

• Food poisoning outbreaks
• Loss of public trust
• Legal penalties
• Business shutdowns

This SAS project simulates realistic food safety inspection data and demonstrates how a SAS Programmer analyzes, validates, classifies, and detects risk or fraud patterns using:

• DATA step
• PROC SQL
• PROC MEANS
• PROC UNIVARIATE
• PROC FREQ
• PROC CORR
• PROC SGPLOT
• MACROS
• DATE FUNCTIONS (MDY, INTCK, INTNX)
• STRING FUNCTIONS (STRIP, TRIM, CAT, CATX, PROPCASE, UPCASE, LOWCASE)
• DATA MANAGEMENT (SET, MERGE, APPEND, TRANSPOSE)
• PROC DATASETS DELETE

 TABLE OF CONTENTS

1. Business Context
2. Dataset Design
3. Raw Data Creation
4. Data Cleaning & Formatting
5. Date Derivations
6. Utilization Classification (Macro)
7. Compliance Percentage Calculation
8. Fraud Detection Logic (Macro)
9. PROC SQL Analysis
10. PROC FREQ Analysis
11. PROC MEANS Analysis
12. PROC UNIVARIATE Analysis
13. PROC CORR Analysis
14. Visualization using PROC SGPLOT
15. Data Restructuring (TRANSPOSE, MERGE)
16. Dataset Maintenance (PROC DATASETS DELETE)
17. Business Insights
18. Conclusion

1.1. BUSINESS CONTEXT

Stakeholders:

• Municipal food safety departments
• Restaurant owners
• Public health analysts
• Policy makers

Business Questions Answered:

• Which cities have poor compliance?
• Are inspections happening frequently enough?
• Which restaurants show suspicious patterns?
• Is re-inspection delayed for unsafe restaurants?
• Is hygiene correlated with inspection score?

2.2.DATASET DESIGN

Core Variables

Variable	Description
Restaurant_ID	Unique restaurant identifier
City	Inspection city
Inspection_Date	Date of inspection
Inspection_Score	Score out of 100
Violations_Count	Number of violations
Reinspection_Days	Days until next inspection
Hygiene_Level	High / Medium / Low
Compliance_Status	Compliant / Non-Compliant
Compliance_Percentage	Calculated percentage
Inspector_Name	Inspector
Risk_Flag	Fraud/Risk indicator

3. RAW DATA CREATION

data food_inspection_raw;

    length Restaurant_ID $6 City $15 Hygiene_Level $10 Inspector_Name $20;

    input Restaurant_ID $ City $ Inspection_Date : date9. Inspection_Score Violations_Count

          Reinspection_Days Hygiene_Level $ Inspector_Name $;

    format Inspection_Date date9.;

    datalines;

R001 hyderabad 01JAN2025 92 1 30 High ramesh

R002 mumbai    05JAN2025 68 5 10 Low  suresh

R003 delhi     07JAN2025 75 3 20 Medium anil

R004 chennai   10JAN2025 88 2 25 High kavita

R005 kolkata   12JAN2025 55 7 7  Low  ramesh

R006 pune      15JAN2025 81 2 30 Medium anil

R007 hyderabad 18JAN2025 60 6 14 Low  suresh

R008 mumbai    20JAN2025 90 1 30 High kavita

R009 delhi     22JAN2025 72 4 15 Medium ramesh

R010 chennai   25JAN2025 85 2 28 High anil

R011 kolkata   27JAN2025 58 8 5  Low  suresh

R012 pune      28JAN2025 79 3 20 Medium kavita

R013 hyderabad 30JAN2025 95 0 45 High ramesh

R014 mumbai    01FEB2025 62 6 12 Low  anil

R015 delhi     03FEB2025 70 4 18 Medium kavita

R016 chennai   05FEB2025 82 2 30 High suresh

;

run;

proc print data=food_inspection_raw;

run;

OUTPUT:

Obs	Restaurant_ID	City	Hygiene_Level	Inspector_Name	Inspection_Date	Inspection_Score	Violations_Count	Reinspection_Days
1	R001	hyderabad	High	ramesh	01JAN2025	92	1	30
2	R002	mumbai	Low	suresh	05JAN2025	68	5	10
3	R003	delhi	Medium	anil	07JAN2025	75	3	20
4	R004	chennai	High	kavita	10JAN2025	88	2	25
5	R005	kolkata	Low	ramesh	12JAN2025	55	7	7
6	R006	pune	Medium	anil	15JAN2025	81	2	30
7	R007	hyderabad	Low	suresh	18JAN2025	60	6	14
8	R008	mumbai	High	kavita	20JAN2025	90	1	30
9	R009	delhi	Medium	ramesh	22JAN2025	72	4	15
10	R010	chennai	High	anil	25JAN2025	85	2	28
11	R011	kolkata	Low	suresh	27JAN2025	58	8	5
12	R012	pune	Medium	kavita	28JAN2025	79	3	20
13	R013	hyderabad	High	ramesh	30JAN2025	95	0	45
14	R014	mumbai	Low	anil	01FEB2025	62	6	12
15	R015	delhi	Medium	kavita	03FEB2025	70	4	18
16	R016	chennai	High	suresh	05FEB2025	82	2	30

·  LENGTH avoids truncation

·  DATE9. ensures proper date handling

·  Realistic inspection patterns

·  Mixed hygiene levels & scores

4. DATA CLEANING & STANDARDIZATION

data food_inspection_clean;

    set food_inspection_raw;

    City = propcase(strip(City));

    Hygiene_Level = upcase(strip(Hygiene_Level));

    Inspector_Name = propcase(strip(Inspector_Name));

    length Compliance_Status $15;

    if Inspection_Score >= 80 then Compliance_Status = "Compliant";

    else Compliance_Status = "Non-Compliant";

run;

proc print data=food_inspection_clean;

 var Restaurant_ID City Hygiene_Level Inspector_Name Inspection_Score Compliance_Status;

run;

OUTPUT:

Obs	Restaurant_ID	City	Hygiene_Level	Inspector_Name	Inspection_Score	Compliance_Status
1	R001	Hyderabad	HIGH	Ramesh	92	Compliant
2	R002	Mumbai	LOW	Suresh	68	Non-Compliant
3	R003	Delhi	MEDIUM	Anil	75	Non-Compliant
4	R004	Chennai	HIGH	Kavita	88	Compliant
5	R005	Kolkata	LOW	Ramesh	55	Non-Compliant
6	R006	Pune	MEDIUM	Anil	81	Compliant
7	R007	Hyderabad	LOW	Suresh	60	Non-Compliant
8	R008	Mumbai	HIGH	Kavita	90	Compliant
9	R009	Delhi	MEDIUM	Ramesh	72	Non-Compliant
10	R010	Chennai	HIGH	Anil	85	Compliant
11	R011	Kolkata	LOW	Suresh	58	Non-Compliant
12	R012	Pune	MEDIUM	Kavita	79	Non-Compliant
13	R013	Hyderabad	HIGH	Ramesh	95	Compliant
14	R014	Mumbai	LOW	Anil	62	Non-Compliant
15	R015	Delhi	MEDIUM	Kavita	70	Non-Compliant
16	R016	Chennai	HIGH	Suresh	82	Compliant

·  STRIP/TRIM: removes unwanted spaces

·  PROPCASE: professional city names

·  UPCASE: standard categorical values

·  Rule-based compliance logic 

5. DATE DERIVATIONS (MDY, INTCK, INTNX)

data food_inspection_dates;

    set food_inspection_clean;

    Inspection_Month = intnx('month', Inspection_Date, 0, 'b');

    Days_Since_Inspection = intck('day', Inspection_Date, today());

    Next_Inspection_Date = intnx('day', Inspection_Date, Reinspection_Days);

run;

proc print data=food_inspection_dates;

 var Restaurant_ID City Hygiene_Level Inspector_Name Inspection_Date Inspection_Month

     Days_Since_Inspection Next_Inspection_Date Reinspection_Days;

run;

OUTPUT:

Obs	Restaurant_ID	City	Hygiene_Level	Inspector_Name	Inspection_Date	Inspection_Month	Days_Since_Inspection	Next_Inspection_Date	Reinspection_Days
1	R001	Hyderabad	HIGH	Ramesh	01JAN2025	23742	406	23772	30
2	R002	Mumbai	LOW	Suresh	05JAN2025	23742	402	23756	10
3	R003	Delhi	MEDIUM	Anil	07JAN2025	23742	400	23768	20
4	R004	Chennai	HIGH	Kavita	10JAN2025	23742	397	23776	25
5	R005	Kolkata	LOW	Ramesh	12JAN2025	23742	395	23760	7
6	R006	Pune	MEDIUM	Anil	15JAN2025	23742	392	23786	30
7	R007	Hyderabad	LOW	Suresh	18JAN2025	23742	389	23773	14
8	R008	Mumbai	HIGH	Kavita	20JAN2025	23742	387	23791	30
9	R009	Delhi	MEDIUM	Ramesh	22JAN2025	23742	385	23778	15
10	R010	Chennai	HIGH	Anil	25JAN2025	23742	382	23794	28
11	R011	Kolkata	LOW	Suresh	27JAN2025	23742	380	23773	5
12	R012	Pune	MEDIUM	Kavita	28JAN2025	23742	379	23789	20
13	R013	Hyderabad	HIGH	Ramesh	30JAN2025	23742	377	23816	45
14	R014	Mumbai	LOW	Anil	01FEB2025	23773	375	23785	12
15	R015	Delhi	MEDIUM	Kavita	03FEB2025	23773	373	23793	18
16	R016	Chennai	HIGH	Suresh	05FEB2025	23773	371	23807	30

·  Identify overdue inspections

·  Schedule future audits

·  Regulatory planning

6. COMPLIANCE PERCENTAGE

data food_inspection_pct;

    set food_inspection_dates;

    Compliance_Percentage = round((Inspection_Score / 100) * 100, 0.01);

run;

proc print data=food_inspection_pct;

 var Restaurant_ID City Hygiene_Level Inspector_Name Inspection_Score Compliance_Percentage;

run;

OUTPUT:

Obs	Restaurant_ID	City	Hygiene_Level	Inspector_Name	Inspection_Score	Compliance_Percentage
1	R001	Hyderabad	HIGH	Ramesh	92	92
2	R002	Mumbai	LOW	Suresh	68	68
3	R003	Delhi	MEDIUM	Anil	75	75
4	R004	Chennai	HIGH	Kavita	88	88
5	R005	Kolkata	LOW	Ramesh	55	55
6	R006	Pune	MEDIUM	Anil	81	81
7	R007	Hyderabad	LOW	Suresh	60	60
8	R008	Mumbai	HIGH	Kavita	90	90
9	R009	Delhi	MEDIUM	Ramesh	72	72
10	R010	Chennai	HIGH	Anil	85	85
11	R011	Kolkata	LOW	Suresh	58	58
12	R012	Pune	MEDIUM	Kavita	79	79
13	R013	Hyderabad	HIGH	Ramesh	95	95
14	R014	Mumbai	LOW	Anil	62	62
15	R015	Delhi	MEDIUM	Kavita	70	70
16	R016	Chennai	HIGH	Suresh	82	82

7. UTILIZATION CLASSIFICATION (MACRO)

%macro classify_utilization;

data food_inspection_util;

    set food_inspection_pct;

    length Utilization_Level $12.;

    if Reinspection_Days <= 10 then Utilization_Level = "High Risk";

    else if Reinspection_Days <= 25 then Utilization_Level = "Medium Risk";

    else Utilization_Level = "Low Risk";

run;

proc print data=food_inspection_util;

run;

%mend;

%classify_utilization;

OUTPUT:

Obs	Restaurant_ID	City	Hygiene_Level	Inspector_Name	Inspection_Date	Inspection_Score	Violations_Count	Reinspection_Days	Compliance_Status	Inspection_Month	Days_Since_Inspection	Next_Inspection_Date	Compliance_Percentage	Utilization_Level
1	R001	Hyderabad	HIGH	Ramesh	01JAN2025	92	1	30	Compliant	23742	406	23772	92	Low Risk
2	R002	Mumbai	LOW	Suresh	05JAN2025	68	5	10	Non-Compliant	23742	402	23756	68	High Risk
3	R003	Delhi	MEDIUM	Anil	07JAN2025	75	3	20	Non-Compliant	23742	400	23768	75	Medium Risk
4	R004	Chennai	HIGH	Kavita	10JAN2025	88	2	25	Compliant	23742	397	23776	88	Medium Risk
5	R005	Kolkata	LOW	Ramesh	12JAN2025	55	7	7	Non-Compliant	23742	395	23760	55	High Risk
6	R006	Pune	MEDIUM	Anil	15JAN2025	81	2	30	Compliant	23742	392	23786	81	Low Risk
7	R007	Hyderabad	LOW	Suresh	18JAN2025	60	6	14	Non-Compliant	23742	389	23773	60	Medium Risk
8	R008	Mumbai	HIGH	Kavita	20JAN2025	90	1	30	Compliant	23742	387	23791	90	Low Risk
9	R009	Delhi	MEDIUM	Ramesh	22JAN2025	72	4	15	Non-Compliant	23742	385	23778	72	Medium Risk
10	R010	Chennai	HIGH	Anil	25JAN2025	85	2	28	Compliant	23742	382	23794	85	Low Risk
11	R011	Kolkata	LOW	Suresh	27JAN2025	58	8	5	Non-Compliant	23742	380	23773	58	High Risk
12	R012	Pune	MEDIUM	Kavita	28JAN2025	79	3	20	Non-Compliant	23742	379	23789	79	Medium Risk
13	R013	Hyderabad	HIGH	Ramesh	30JAN2025	95	0	45	Compliant	23742	377	23816	95	Low Risk
14	R014	Mumbai	LOW	Anil	01FEB2025	62	6	12	Non-Compliant	23773	375	23785	62	Medium Risk
15	R015	Delhi	MEDIUM	Kavita	03FEB2025	70	4	18	Non-Compliant	23773	373	23793	70	Medium Risk
16	R016	Chennai	HIGH	Suresh	05FEB2025	82	2	30	Compliant	23773	371	23807	82	Low Risk

Macros help automate business logic and ensure consistency across studies.

8. FRAUD / RISK DETECTION LOGIC (MACRO)

%macro fraud_detection;

data food_inspection_fraud;

    set food_inspection_util;

    length Risk_Flag $12.;

    if Inspection_Score > 85 and Violations_Count > 5 then Risk_Flag = "Suspicious";

    else if Inspection_Score < 60 and Reinspection_Days > 25 then Risk_Flag = "Negligence";

    else Risk_Flag = "Normal";

run;

proc print data=food_inspection_fraud; 

 var Restaurant_ID City Hygiene_Level Inspector_Name Inspection_Score Violations_Count 

     Risk_Flag;

run;

%mend;

%fraud_detection;

OUTPUT:

Obs	Restaurant_ID	City	Hygiene_Level	Inspector_Name	Inspection_Score	Violations_Count	Risk_Flag
1	R001	Hyderabad	HIGH	Ramesh	92	1	Normal
2	R002	Mumbai	LOW	Suresh	68	5	Normal
3	R003	Delhi	MEDIUM	Anil	75	3	Normal
4	R004	Chennai	HIGH	Kavita	88	2	Normal
5	R005	Kolkata	LOW	Ramesh	55	7	Normal
6	R006	Pune	MEDIUM	Anil	81	2	Normal
7	R007	Hyderabad	LOW	Suresh	60	6	Normal
8	R008	Mumbai	HIGH	Kavita	90	1	Normal
9	R009	Delhi	MEDIUM	Ramesh	72	4	Normal
10	R010	Chennai	HIGH	Anil	85	2	Normal
11	R011	Kolkata	LOW	Suresh	58	8	Normal
12	R012	Pune	MEDIUM	Kavita	79	3	Normal
13	R013	Hyderabad	HIGH	Ramesh	95	0	Normal
14	R014	Mumbai	LOW	Anil	62	6	Normal
15	R015	Delhi	MEDIUM	Kavita	70	4	Normal
16	R016	Chennai	HIGH	Suresh	82	2	Normal

·  High score + many violations = possible manipulation

·  Low score + delayed reinspection = regulatory lapse

9. PROC SQL – BUSINESS SUMMARIES

proc sql;

    create table city_summary as

    select City,

           count(*) as Total_Restaurants,

           avg(Inspection_Score) as Avg_Score format=6.2,

           avg(Violations_Count) as Avg_Violations

    from food_inspection_fraud

    group by City;

quit;

proc print data=city_summary;

run;

OUTPUT:

Obs	City	Total_Restaurants	Avg_Score	Avg_Violations
1	Chennai	3	85.00	2.00000
2	Delhi	3	72.33	3.66667
3	Hyderabad	3	82.33	2.33333
4	Kolkata	2	56.50	7.50000
5	Mumbai	3	73.33	4.00000
6	Pune	2	80.00	2.50000

10. PROC FREQ

proc freq data=food_inspection_fraud;

    tables City*Compliance_Status / nocum;

run;

OUTPUT:

The FREQ Procedure

Frequency Percent Row Pct Col Pct

Table of City by Compliance_Status City Compliance_Status Compliant Non-Compliant Total Chennai 3 18.75 100.00 42.86 0 0.00 0.00 0.00 3 18.75     Delhi 0 0.00 0.00 0.00 3 18.75 100.00 33.33 3 18.75     Hyderabad 2 12.50 66.67 28.57 1 6.25 33.33 11.11 3 18.75     Kolkata 0 0.00 0.00 0.00 2 12.50 100.00 22.22 2 12.50     Mumbai 1 6.25 33.33 14.29 2 12.50 66.67 22.22 3 18.75     Pune 1 6.25 50.00 14.29 1 6.25 50.00 11.11 2 12.50     Total 7 43.75 9 56.25 16 100.00

11. PROC MEANS

proc means data=food_inspection_fraud mean min max std;

    var Inspection_Score Violations_Count Reinspection_Days;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum	Std Dev
Inspection_Score Violations_Count Reinspection_Days	75.7500000 3.5000000 21.1875000	55.0000000 0 5.0000000	95.0000000 8.0000000 45.0000000	12.7566976 2.3380904 10.6909229

12. PROC UNIVARIATE

proc univariate data=food_inspection_fraud;

    var Inspection_Score;

    histogram Inspection_Score;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Inspection_Score

Moments
N	16	Sum Weights	16
Mean	75.75	Sum Observations	1212
Std Deviation	12.7566976	Variance	162.733333
Skewness	-0.160423	Kurtosis	-1.1980457
Uncorrected SS	94250	Corrected SS	2441
Coeff Variation	16.8405249	Std Error Mean	3.1891744

Basic Statistical Measures
Location		Variability
Mean	75.75000	Std Deviation	12.75670
Median	77.00000	Variance	162.73333
Mode	.	Range	40.00000
		Interquartile Range	21.50000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	23.75223	Pr > |t|	<.0001
Sign	M	8	Pr >= |M|	<.0001
Signed Rank	S	68	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	95.0
99%	95.0
95%	95.0
90%	92.0
75% Q3	86.5
50% Median	77.0
25% Q1	65.0
10%	58.0
5%	55.0
1%	55.0
0% Min	55.0

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
55	5	85	10
58	11	88	4
60	7	90	8
62	14	92	1
68	2	95	13

The UNIVARIATE Procedure

13. PROC CORR

proc corr data=food_inspection_fraud;

    var Inspection_Score Violations_Count Reinspection_Days;

run;

OUTPUT:

The CORR Procedure

3 Variables:	Inspection_Score Violations_Count Reinspection_Days

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Inspection_Score	16	75.75000	12.75670	1212	55.00000	95.00000
Violations_Count	16	3.50000	2.33809	56.00000	0	8.00000
Reinspection_Days	16	21.18750	10.69092	339.00000	5.00000	45.00000

Pearson Correlation Coefficients, N = 16 Prob > |r| under H0: Rho=0
	Inspection_Score	Violations_Count	Reinspection_Days
Inspection_Score	1.00000	-0.97677 <.0001	0.92132 <.0001
Violations_Count	-0.97677 <.0001	1.00000	-0.94280 <.0001
Reinspection_Days	0.92132 <.0001	-0.94280 <.0001	1.00000

14. PROC SGPLOT

proc sgplot data=food_inspection_fraud;

    vbox Inspection_Score / category=City;

run;

OUTPUT:

15. TRANSPOSE

proc transpose data=city_summary out=city_transposed;

    id City;

    var Avg_Score;

run;

proc print data=city_transposed;

run;

OUTPUT:

Obs	_NAME_	Chennai	Delhi	Hyderabad	Kolkata	Mumbai	Pune
1	Avg_Score	85.00	72.33	82.33	56.50	73.33	80.00

16. SORT & MERGE 

proc sort data=food_inspection_fraud;by City;run;

proc print data=food_inspection_fraud;

run;

OUTPUT:

Obs	Restaurant_ID	City	Hygiene_Level	Inspector_Name	Inspection_Date	Inspection_Score	Violations_Count	Reinspection_Days	Compliance_Status	Inspection_Month	Days_Since_Inspection	Next_Inspection_Date	Compliance_Percentage	Utilization_Level	Risk_Flag
1	R004	Chennai	HIGH	Kavita	10JAN2025	88	2	25	Compliant	23742	397	23776	88	Medium Risk	Normal
2	R010	Chennai	HIGH	Anil	25JAN2025	85	2	28	Compliant	23742	382	23794	85	Low Risk	Normal
3	R016	Chennai	HIGH	Suresh	05FEB2025	82	2	30	Compliant	23773	371	23807	82	Low Risk	Normal
4	R003	Delhi	MEDIUM	Anil	07JAN2025	75	3	20	Non-Compliant	23742	400	23768	75	Medium Risk	Normal
5	R009	Delhi	MEDIUM	Ramesh	22JAN2025	72	4	15	Non-Compliant	23742	385	23778	72	Medium Risk	Normal
6	R015	Delhi	MEDIUM	Kavita	03FEB2025	70	4	18	Non-Compliant	23773	373	23793	70	Medium Risk	Normal
7	R001	Hyderabad	HIGH	Ramesh	01JAN2025	92	1	30	Compliant	23742	406	23772	92	Low Risk	Normal
8	R007	Hyderabad	LOW	Suresh	18JAN2025	60	6	14	Non-Compliant	23742	389	23773	60	Medium Risk	Normal
9	R013	Hyderabad	HIGH	Ramesh	30JAN2025	95	0	45	Compliant	23742	377	23816	95	Low Risk	Normal
10	R005	Kolkata	LOW	Ramesh	12JAN2025	55	7	7	Non-Compliant	23742	395	23760	55	High Risk	Normal
11	R011	Kolkata	LOW	Suresh	27JAN2025	58	8	5	Non-Compliant	23742	380	23773	58	High Risk	Normal
12	R002	Mumbai	LOW	Suresh	05JAN2025	68	5	10	Non-Compliant	23742	402	23756	68	High Risk	Normal
13	R008	Mumbai	HIGH	Kavita	20JAN2025	90	1	30	Compliant	23742	387	23791	90	Low Risk	Normal
14	R014	Mumbai	LOW	Anil	01FEB2025	62	6	12	Non-Compliant	23773	375	23785	62	Medium Risk	Normal
15	R006	Pune	MEDIUM	Anil	15JAN2025	81	2	30	Compliant	23742	392	23786	81	Low Risk	Normal
16	R012	Pune	MEDIUM	Kavita	28JAN2025	79	3	20	Non-Compliant	23742	379	23789	79	Medium Risk	Normal

proc sort data=city_summary;by City;run;

proc print data=city_summary;

run;

OUTPUT:

Obs	City	Total_Restaurants	Avg_Score	Avg_Violations
1	Chennai	3	85.00	2.00000
2	Delhi	3	72.33	3.66667
3	Hyderabad	3	82.33	2.33333
4	Kolkata	2	56.50	7.50000
5	Mumbai	3	73.33	4.00000
6	Pune	2	80.00	2.50000

data final_merged;

    merge food_inspection_fraud

          city_summary;

    by City;

run;

proc print data=final_merged;

run;

OUTPUT:

Obs	Restaurant_ID	City	Hygiene_Level	Inspector_Name	Inspection_Date	Inspection_Score	Violations_Count	Reinspection_Days	Compliance_Status	Inspection_Month	Days_Since_Inspection	Next_Inspection_Date	Compliance_Percentage	Utilization_Level	Risk_Flag	Total_Restaurants	Avg_Score	Avg_Violations
1	R004	Chennai	HIGH	Kavita	10JAN2025	88	2	25	Compliant	23742	397	23776	88	Medium Risk	Normal	3	85.00	2.00000
2	R010	Chennai	HIGH	Anil	25JAN2025	85	2	28	Compliant	23742	382	23794	85	Low Risk	Normal	3	85.00	2.00000
3	R016	Chennai	HIGH	Suresh	05FEB2025	82	2	30	Compliant	23773	371	23807	82	Low Risk	Normal	3	85.00	2.00000
4	R003	Delhi	MEDIUM	Anil	07JAN2025	75	3	20	Non-Compliant	23742	400	23768	75	Medium Risk	Normal	3	72.33	3.66667
5	R009	Delhi	MEDIUM	Ramesh	22JAN2025	72	4	15	Non-Compliant	23742	385	23778	72	Medium Risk	Normal	3	72.33	3.66667
6	R015	Delhi	MEDIUM	Kavita	03FEB2025	70	4	18	Non-Compliant	23773	373	23793	70	Medium Risk	Normal	3	72.33	3.66667
7	R001	Hyderabad	HIGH	Ramesh	01JAN2025	92	1	30	Compliant	23742	406	23772	92	Low Risk	Normal	3	82.33	2.33333
8	R007	Hyderabad	LOW	Suresh	18JAN2025	60	6	14	Non-Compliant	23742	389	23773	60	Medium Risk	Normal	3	82.33	2.33333
9	R013	Hyderabad	HIGH	Ramesh	30JAN2025	95	0	45	Compliant	23742	377	23816	95	Low Risk	Normal	3	82.33	2.33333
10	R005	Kolkata	LOW	Ramesh	12JAN2025	55	7	7	Non-Compliant	23742	395	23760	55	High Risk	Normal	2	56.50	7.50000
11	R011	Kolkata	LOW	Suresh	27JAN2025	58	8	5	Non-Compliant	23742	380	23773	58	High Risk	Normal	2	56.50	7.50000
12	R002	Mumbai	LOW	Suresh	05JAN2025	68	5	10	Non-Compliant	23742	402	23756	68	High Risk	Normal	3	73.33	4.00000
13	R008	Mumbai	HIGH	Kavita	20JAN2025	90	1	30	Compliant	23742	387	23791	90	Low Risk	Normal	3	73.33	4.00000
14	R014	Mumbai	LOW	Anil	01FEB2025	62	6	12	Non-Compliant	23773	375	23785	62	Medium Risk	Normal	3	73.33	4.00000
15	R006	Pune	MEDIUM	Anil	15JAN2025	81	2	30	Compliant	23742	392	23786	81	Low Risk	Normal	2	80.00	2.50000
16	R012	Pune	MEDIUM	Kavita	28JAN2025	79	3	20	Non-Compliant	23742	379	23789	79	Medium Risk	Normal	2	80.00	2.50000

17. PROC DATASETS DELETE

proc datasets library=work nolist;

    delete food_inspection_raw food_inspection_clean;

quit;

LOG:

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

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

18. BUSINESS INSIGHTS

·       Demonstrates end-to-end data lifecycle

·       Shows regulatory analytics

·       Uses advanced SAS techniques

·       Practical fraud detection logic

·       Clean, auditable, scalable code

19. Conclusion

This Food Safety Inspections SAS project clearly demonstrates how real-world inspection data can be created, cleaned, analyzed, and interpreted using SAS in a practical and business-oriented way. By combining DATA step programming, PROC SQL, statistical procedures, macros, and date and character functions, the project shows how food safety authorities can monitor restaurant hygiene, identify high-risk establishments, and ensure timely re-inspections.

The analysis helps in understanding relationships between inspection scores, violations, and reinspection timelines, while the fraud and risk detection logic highlights suspicious or negligent inspection patterns. Overall, this project reflects how SAS supports public health decision-making by turning raw inspection data into meaningful insights that improve food safety, regulatory compliance, and consumer protection in a simple and effective manner.

INTERVIEW QUESTIONS FOR YOU

·  What is MDY?

·  What is ROUND function?

·  What is the difference between MEAN and SUM?

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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 FOOD SAFETY 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

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Follow Us On : 

 

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

--->Follow our blog for more SAS-based analytics projects and industry data models.

---> Support Us By Following Our Blog..

To deepen your understanding of SAS analytics, please refer to our other data science and industry-focused projects listed below:

1.Can SAS Identify Top Gaming Players Using Performance Data and Advanced Analytics?

2.Can SAS Data Analysis Accurately Measure a Sports Player’s True Performance Over Time?

3.Can SAS Analytics Reveal Differences Between Various Types of Neem Trees?

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

About Us | Contact | Privacy Policy