343.Which Country Truly Dominates the Olympics? – A Complete SAS Medal Efficiency Analytics Project

Which Country Truly Dominates the Olympics? – A Complete SAS Medal Efficiency Analytics Project

options nocenter;

1.OLYMPIC PERFORMANCE DATA CREATION

data olympic_performance;

    length Country $20;

    format Olympics_Date date9.;

    input Country $ Gold Silver Bronze Population Rank Olympics_Date :date9.;

    Total_Medals = Gold + Silver + Bronze;

datalines;

USA 39 41 33 331 1 24JUL2021

China 38 32 18 1441 2 24JUL2021

Japan 27 14 17 126 3 24JUL2021

GreatBritain 22 21 22 68 4 24JUL2021

ROC 20 28 23 146 5 24JUL2021

Australia 17 7 22 26 6 24JUL2021

Netherlands 10 12 14 17 7 24JUL2021

France 10 12 11 65 8 24JUL2021

Germany 10 11 16 83 9 24JUL2021

Italy 10 10 20 60 10 24JUL2021

Canada 7 6 11 38 11 24JUL2021

Brazil 7 6 8 213 12 24JUL2021

;

run;

proc print data=olympic_performance;

run;

OUTPUT:

Obs	Country	Olympics_Date	Gold	Silver	Bronze	Population	Rank	Total_Medals
1	USA	24JUL2021	39	41	33	331	1	113
2	China	24JUL2021	38	32	18	1441	2	88
3	Japan	24JUL2021	27	14	17	126	3	58
4	GreatBritain	24JUL2021	22	21	22	68	4	65
5	ROC	24JUL2021	20	28	23	146	5	71
6	Australia	24JUL2021	17	7	22	26	6	46
7	Netherlands	24JUL2021	10	12	14	17	7	36
8	France	24JUL2021	10	12	11	65	8	33
9	Germany	24JUL2021	10	11	16	83	9	37
10	Italy	24JUL2021	10	10	20	60	10	40
11	Canada	24JUL2021	7	6	11	38	11	24
12	Brazil	24JUL2021	7	6	8	213	12	21

2.DATA VALIDATION

proc contents data=olympic_performance;

run;

OUTPUT:

The CONTENTS Procedure

Data Set Name	WORK.OLYMPIC_PERFORMANCE	Observations	12
Member Type	DATA	Variables	8
Engine	V9	Indexes	0
Created	12/19/2025 07:20:13	Observation Length	80
Last Modified	12/19/2025 07:20:13	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	SOLARIS_X86_64, LINUX_X86_64, ALPHA_TRU64, LINUX_IA64
Encoding	utf-8 Unicode (UTF-8)

Engine/Host Dependent Information
Data Set Page Size	131072
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	1635
Obs in First Data Page	12
Number of Data Set Repairs	0
Filename	/saswork/SAS_workEE3900010E8A_odaws02-apse1-2.oda.sas.com/SAS_work271200010E8A_odaws02-apse1-2.oda.sas.com/olympic_performance.sas7bdat
Release Created	9.0401M8
Host Created	Linux
Inode Number	67178131
Access Permission	rw-r--r--
Owner Name	u63247146
File Size	256KB
File Size (bytes)	262144

Alphabetic List of Variables and Attributes
#	Variable	Type	Len	Format
5	Bronze	Num	8
1	Country	Char	20
3	Gold	Num	8
2	Olympics_Date	Num	8	DATE9.
6	Population	Num	8
7	Rank	Num	8
4	Silver	Num	8
8	Total_Medals	Num	8

proc print data=olympic_performance(obs=5);

run;

OUTPUT:

Obs	Country	Olympics_Date	Gold	Silver	Bronze	Population	Rank	Total_Medals
1	USA	24JUL2021	39	41	33	331	1	113
2	China	24JUL2021	38	32	18	1441	2	88
3	Japan	24JUL2021	27	14	17	126	3	58
4	GreatBritain	24JUL2021	22	21	22	68	4	65
5	ROC	24JUL2021	20	28	23	146	5	71

3.DATE FUNCTION APPLICATION (INTNX & INTCK)

data olympic_dates;

    set olympic_performance;

    Next_Olympics = intnx('year', Olympics_Date, 4, 'same');

    Years_Since_2021 = intck('year', Olympics_Date, '24JUL2025'd);

    format Next_Olympics date9.;

run;

proc print data=olympic_dates;

run;

OUTPUT:

Obs	Country	Olympics_Date	Gold	Silver	Bronze	Population	Rank	Total_Medals	Next_Olympics	Years_Since_2021
1	USA	24JUL2021	39	41	33	331	1	113	24JUL2025	4
2	China	24JUL2021	38	32	18	1441	2	88	24JUL2025	4
3	Japan	24JUL2021	27	14	17	126	3	58	24JUL2025	4
4	GreatBritain	24JUL2021	22	21	22	68	4	65	24JUL2025	4
5	ROC	24JUL2021	20	28	23	146	5	71	24JUL2025	4
6	Australia	24JUL2021	17	7	22	26	6	46	24JUL2025	4
7	Netherlands	24JUL2021	10	12	14	17	7	36	24JUL2025	4
8	France	24JUL2021	10	12	11	65	8	33	24JUL2025	4
9	Germany	24JUL2021	10	11	16	83	9	37	24JUL2025	4
10	Italy	24JUL2021	10	10	20	60	10	40	24JUL2025	4
11	Canada	24JUL2021	7	6	11	38	11	24	24JUL2025	4
12	Brazil	24JUL2021	7	6	8	213	12	21	24JUL2025	4

4.PROC SQL – DERIVED METRICS

proc sql;

    create table olympic_sql as

    select *,

           Total_Medals / Population as Medal_Efficiency format=8.3,

           Gold / Population as Gold_Efficiency format=8.3

    from olympic_dates

    order by Medal_Efficiency desc;

quit;

proc print data=olympic_sql;

run;

OUTPUT:

Obs	Country	Olympics_Date	Gold	Silver	Bronze	Population	Rank	Total_Medals	Next_Olympics	Years_Since_2021	Medal_Efficiency	Gold_Efficiency
1	Netherlands	24JUL2021	10	12	14	17	7	36	24JUL2025	4	2.118	0.588
2	Australia	24JUL2021	17	7	22	26	6	46	24JUL2025	4	1.769	0.654
3	GreatBritain	24JUL2021	22	21	22	68	4	65	24JUL2025	4	0.956	0.324
4	Italy	24JUL2021	10	10	20	60	10	40	24JUL2025	4	0.667	0.167
5	Canada	24JUL2021	7	6	11	38	11	24	24JUL2025	4	0.632	0.184
6	France	24JUL2021	10	12	11	65	8	33	24JUL2025	4	0.508	0.154
7	ROC	24JUL2021	20	28	23	146	5	71	24JUL2025	4	0.486	0.137
8	Japan	24JUL2021	27	14	17	126	3	58	24JUL2025	4	0.460	0.214
9	Germany	24JUL2021	10	11	16	83	9	37	24JUL2025	4	0.446	0.120
10	USA	24JUL2021	39	41	33	331	1	113	24JUL2025	4	0.341	0.118
11	Brazil	24JUL2021	7	6	8	213	12	21	24JUL2025	4	0.099	0.033
12	China	24JUL2021	38	32	18	1441	2	88	24JUL2025	4	0.061	0.026

5.MACRO FOR MEDAL EFFICIENCY CLASSIFICATION

%macro efficiency_flag;

data olympic_efficiency;

    set olympic_sql;

    length Efficiency_Level $12;

    if Medal_Efficiency >= 1 then Efficiency_Level = "EXCELLENT";

    else if Medal_Efficiency >= 0.5 then Efficiency_Level = "GOOD";

    else Efficiency_Level = "AVERAGE";

run;

proc print data=olympic_efficiency;

run;

%mend;

%efficiency_flag;

OUTPUT:

Obs	Country	Olympics_Date	Gold	Silver	Bronze	Population	Rank	Total_Medals	Next_Olympics	Years_Since_2021	Medal_Efficiency	Gold_Efficiency	Efficiency_Level
1	Netherlands	24JUL2021	10	12	14	17	7	36	24JUL2025	4	2.118	0.588	EXCELLENT
2	Australia	24JUL2021	17	7	22	26	6	46	24JUL2025	4	1.769	0.654	EXCELLENT
3	GreatBritain	24JUL2021	22	21	22	68	4	65	24JUL2025	4	0.956	0.324	GOOD
4	Italy	24JUL2021	10	10	20	60	10	40	24JUL2025	4	0.667	0.167	GOOD
5	Canada	24JUL2021	7	6	11	38	11	24	24JUL2025	4	0.632	0.184	GOOD
6	France	24JUL2021	10	12	11	65	8	33	24JUL2025	4	0.508	0.154	GOOD
7	ROC	24JUL2021	20	28	23	146	5	71	24JUL2025	4	0.486	0.137	AVERAGE
8	Japan	24JUL2021	27	14	17	126	3	58	24JUL2025	4	0.460	0.214	AVERAGE
9	Germany	24JUL2021	10	11	16	83	9	37	24JUL2025	4	0.446	0.120	AVERAGE
10	USA	24JUL2021	39	41	33	331	1	113	24JUL2025	4	0.341	0.118	AVERAGE
11	Brazil	24JUL2021	7	6	8	213	12	21	24JUL2025	4	0.099	0.033	AVERAGE
12	China	24JUL2021	38	32	18	1441	2	88	24JUL2025	4	0.061	0.026	AVERAGE

6.PROC MEANS – STATISTICAL SUMMARY

proc means data=olympic_efficiency min max mean sum;

    var Gold Silver Bronze Total_Medals Population Medal_Efficiency;

run;

OUTPUT:

The MEANS Procedure

Variable	Minimum	Maximum	Mean	Sum
Gold Silver Bronze Total_Medals Population Medal_Efficiency	7.0000000 6.0000000 8.0000000 21.0000000 17.0000000 0.0610687	39.0000000 41.0000000 33.0000000 113.0000000 1441.00 2.1176471	18.0833333 16.6666667 17.9166667 52.6666667 217.8333333 0.7118458	217.0000000 200.0000000 215.0000000 632.0000000 2614.00 8.5421500

7.PROC FREQ – RANK DISTRIBUTION

proc freq data=olympic_efficiency;

    tables Rank Efficiency_Level;

run;

OUTPUT:

The FREQ Procedure

Rank	Frequency	Percent	Cumulative Frequency	Cumulative Percent
1	1	8.33	1	8.33
2	1	8.33	2	16.67
3	1	8.33	3	25.00
4	1	8.33	4	33.33
5	1	8.33	5	41.67
6	1	8.33	6	50.00
7	1	8.33	7	58.33
8	1	8.33	8	66.67
9	1	8.33	9	75.00
10	1	8.33	10	83.33
11	1	8.33	11	91.67
12	1	8.33	12	100.00

Efficiency_Level	Frequency	Percent	Cumulative Frequency	Cumulative Percent
AVERAGE	6	50.00	6	50.00
EXCELLENT	2	16.67	8	66.67
GOOD	4	33.33	12	100.00

8.PROC SGPLOT – VISUAL ANALYSIS

A. BAR CHART – TOTAL MEDALS BY COUNTRY

proc sgplot data=olympic_efficiency;

    vbar Country / response=Total_Medals datalabel;

    title "TOTAL OLYMPIC MEDALS BY COUNTRY";

run;

OUTPUT:

B. SCATTER PLOT – POPULATION VS MEDAL EFFICIENCY

proc sgplot data=olympic_efficiency;

    scatter x=Population y=Medal_Efficiency / datalabel=Country;

    title "POPULATION VS MEDAL EFFICIENCY";

run;

OUTPUT:

9.PROC SORT & PROC PRINT – TOP PERFORMERS

proc sort data=olympic_efficiency;

    by descending Medal_Efficiency;

run;

proc print data=olympic_efficiency(obs=5);

    title "TOP 5 COUNTRIES BY MEDAL EFFICIENCY";

run;

OUTPUT:

TOP 5 COUNTRIES BY MEDAL EFFICIENCY

Obs	Country	Olympics_Date	Gold	Silver	Bronze	Population	Rank	Total_Medals	Next_Olympics	Years_Since_2021	Medal_Efficiency	Gold_Efficiency	Efficiency_Level
1	Netherlands	24JUL2021	10	12	14	17	7	36	24JUL2025	4	2.118	0.588	EXCELLENT
2	Australia	24JUL2021	17	7	22	26	6	46	24JUL2025	4	1.769	0.654	EXCELLENT
3	GreatBritain	24JUL2021	22	21	22	68	4	65	24JUL2025	4	0.956	0.324	GOOD
4	Italy	24JUL2021	10	10	20	60	10	40	24JUL2025	4	0.667	0.167	GOOD
5	Canada	24JUL2021	7	6	11	38	11	24	24JUL2025	4	0.632	0.184	GOOD

10.PIE Chart

proc sgplot data=olympic_efficiency;

    pie Efficiency_Level / datalabel;

run;

/* Note: In practice above there is an Invalid in this code Find it,Correct it and Use it /*

OUTPUT:

To Visit My Previous Online Streaming Flatform Dataset:Click Here

To Visit My Previous Statistical Evaluation Of Clinical Trials:Click Here

To Visit My Previous Unlocking Retail Insights Dataset:Click Here

To Visit My Previous Sas Interview Questions-1:Click Here

Follow Us On : 

 

--- FOLLOW OUR BLOG FOR MORE INFORMATION.

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