335.FITNESS DATASET CREATION AND ANALYSIS USING PROC SQL | PROC MEANS | PROC UNIVARIATE | PROC SGPLOT | PROC FREQ | PROC FORMAT | PROC RANK | PROC REPORT | PROC CORR (WITH MACROS AND DATE FUNCTIONS LIKE INTCK | INTNX)

FITNESS DATASET CREATION AND ANALYSIS USING PROC SQL | PROC MEANS | PROC UNIVARIATE | PROC SGPLOT | PROC FREQ | PROC FORMAT | PROC RANK | PROC REPORT | PROC CORR (WITH MACROS AND DATE FUNCTIONS LIKE INTCK | INTNX)

options nocenter;

1. Create raw table with explicit dates and sample values 

data work.fitness_raw;

  format Baseline_Date Measurement_Date Next_Check_Date date9.;

  /* Next_Check_Date placeholder - will derive later */

  infile datalines dsd dlm=',';

  input Person_ID :$8. Steps_Per_Day :comma32. Calories_Burned :comma32. BMI :8.2

        Heart_Rate :8. Baseline_Date :date9. Measurement_Date :date9.;

  datalines;

P001,  12000,  3200,  22.5,  62,  01JAN2024,  01JUL2025

P002,   3500,  2100,  29.8,  82,  01FEB2024,  10JUL2025

P003,   8000,  2500,  24.3,  72,  15MAR2024,  25JUN2025

P004,   4000,  2300,  27.1,  78,  01APR2024,  05JUL2025

P005,  15000,  3400,  21.0,  58,  20MAY2024,  20JUN2025

P006,   6000,  2400,  26.5,  75,  10JUN2024,  30JUN2025

P007,  10000,  3000,  23.4,  66,  01JUL2024,  01JUL2025

P008,   2000,  1900,  31.2,  88,  15JUL2024,  15JUL2025

P009,   9000,  2800,  25.0,  70,  01AUG2024,  10JUL2025

P010,  11000,  3100,  22.0,  64,  05SEP2024,  05JUL2025

P011,   4500,  2250,  28.4,  79,  10OCT2024,  10JUL2025

P012,   7000,  2600,  24.9,  71,  20NOV2024,  20JUL2025

;

run;

proc print data=work.fitness_raw ;

  title "Raw fitness_raw dataset - initial check";

run;

OUTPUT:

Raw fitness_raw dataset - initial check

Obs	Baseline_Date	Measurement_Date	Next_Check_Date	Person_ID	Steps_Per_Day	Calories_Burned	BMI	Heart_Rate
1	01JAN2024	01JUL2025	.	P001	12000	3200	22.5	62
2	01FEB2024	10JUL2025	.	P002	3500	2100	29.8	82
3	15MAR2024	25JUN2025	.	P003	8000	2500	24.3	72
4	01APR2024	05JUL2025	.	P004	4000	2300	27.1	78
5	20MAY2024	20JUN2025	.	P005	15000	3400	21.0	58
6	10JUN2024	30JUN2025	.	P006	6000	2400	26.5	75
7	01JUL2024	01JUL2025	.	P007	10000	3000	23.4	66
8	15JUL2024	15JUL2025	.	P008	2000	1900	31.2	88
9	01AUG2024	10JUL2025	.	P009	9000	2800	25.0	70
10	05SEP2024	05JUL2025	.	P010	11000	3100	22.0	64
11	10OCT2024	10JUL2025	.	P011	4500	2250	28.4	79
12	20NOV2024	20JUL2025	.	P012	7000	2600	24.9	71

2. Derive additional date-based and health variables using DATA step 

data work.fitness;

  set work.fitness_raw;

  /* Compute days between Baseline and Measurement (inclusive/exclusive choice: exclusive) */

  Duration_Days = intck('day', Baseline_Date, Measurement_Date);

  /* Compute Next_Check_Date as 3 months after Measurement (INTNX with 'same' alignment) */

  Next_Check_Date = intnx('month', Measurement_Date, 3, 'same');

  /* Step score: cap at 20000 for scaling */

  Steps_Capped = min(Steps_Per_Day,20000);

  Step_Score = (Steps_Capped / 10000) * 30; /* 0-60 scaled to 30 baseline */

  /* BMI score: best at 21.75-24.99 -> use gaussian-ish penalty (simple) */

  if BMI <= 18.5 then BMI_Score = 10;

  else if 18.5 < BMI <= 24.99 then BMI_Score = 30;

  else if 25 <= BMI <= 29.99 then BMI_Score = 20;

  else BMI_Score = 10; /* obese range */

  /* Heart rate score: lower is better (but not extremely low) */

  if 50 <= Heart_Rate <= 70 then HR_Score = 30;

  else if 71 <= Heart_Rate <= 80 then HR_Score = 20;

  else if Heart_Rate < 50 then HR_Score = 15;

  else HR_Score = 10;

  /* Calories score: approximate expected burn (higher with more activity) */

  Cal_Score = min((Calories_Burned / 3000) * 20, 20);

  /* Composite score out of 100 */

  Composite_Score = Step_Score + BMI_Score + HR_Score + Cal_Score;

  /* Keep composite in 0-100 boundary */

  if Composite_Score > 100 then Composite_Score = 100;

  drop Steps_Capped;

run;

proc print data=work.fitness;

run;

OUTPUT:

Obs	Baseline_Date	Measurement_Date	Next_Check_Date	Person_ID	Steps_Per_Day	Calories_Burned	BMI	Heart_Rate	Duration_Days	Step_Score	BMI_Score	HR_Score	Cal_Score	Composite_Score
1	01JAN2024	01JUL2025	01OCT2025	P001	12000	3200	22.5	62	547	36.0	30	30	20.0000	100.000
2	01FEB2024	10JUL2025	10OCT2025	P002	3500	2100	29.8	82	525	10.5	20	10	14.0000	54.500
3	15MAR2024	25JUN2025	25SEP2025	P003	8000	2500	24.3	72	467	24.0	30	20	16.6667	90.667
4	01APR2024	05JUL2025	05OCT2025	P004	4000	2300	27.1	78	460	12.0	20	20	15.3333	67.333
5	20MAY2024	20JUN2025	20SEP2025	P005	15000	3400	21.0	58	396	45.0	30	30	20.0000	100.000
6	10JUN2024	30JUN2025	30SEP2025	P006	6000	2400	26.5	75	385	18.0	20	20	16.0000	74.000
7	01JUL2024	01JUL2025	01OCT2025	P007	10000	3000	23.4	66	365	30.0	30	30	20.0000	100.000
8	15JUL2024	15JUL2025	15OCT2025	P008	2000	1900	31.2	88	365	6.0	10	10	12.6667	38.667
9	01AUG2024	10JUL2025	10OCT2025	P009	9000	2800	25.0	70	343	27.0	20	30	18.6667	95.667
10	05SEP2024	05JUL2025	05OCT2025	P010	11000	3100	22.0	64	303	33.0	30	30	20.0000	100.000
11	10OCT2024	10JUL2025	10OCT2025	P011	4500	2250	28.4	79	273	13.5	20	20	15.0000	68.500
12	20NOV2024	20JUL2025	20OCT2025	P012	7000	2600	24.9	71	242	21.0	30	20	17.3333	88.333

3. Macro for automatic classification based on Composite_Score and BMI thresholds 

%macro classify_health(in=, out=);

  /* Creates a new dataset with Fitness_Level variable assigned */

  data &out.;

    set &in.;

    length Fitness_Level $12.;

    if Composite_Score >= 80 then Fitness_Level = 'Excellent';

    else if Composite_Score >= 60 then Fitness_Level = 'Good';

    else if Composite_Score >= 40 then Fitness_Level = 'Average';

    else Fitness_Level = 'Poor';

    /* Make a short textual note for borderline cases (example of derived variable) */

    if (BMI >= 25 and Composite_Score >= 60) then Fitness_Comment = 'High BMI but good activity';

    else if (BMI < 18.5 and Composite_Score < 40) then Fitness_Comment = 'Underweight & low score';

    else Fitness_Comment = 'No major note';

    format Baseline_Date Measurement_Date Next_Check_Date date9.;

  run;

  proc print data=&out. ;

  run;

%mend classify_health;

%classify_health(in=work.fitness, out=work.fitness_final);

OUTPUT:

Obs	Baseline_Date	Measurement_Date	Next_Check_Date	Person_ID	Steps_Per_Day	Calories_Burned	BMI	Heart_Rate	Duration_Days	Step_Score	BMI_Score	HR_Score	Cal_Score	Composite_Score	Fitness_Level	Fitness_Comment
1	01JAN2024	01JUL2025	01OCT2025	P001	12000	3200	22.5	62	547	36.0	30	30	20.0000	100.000	Excellent	No major note
2	01FEB2024	10JUL2025	10OCT2025	P002	3500	2100	29.8	82	525	10.5	20	10	14.0000	54.500	Average	No major note
3	15MAR2024	25JUN2025	25SEP2025	P003	8000	2500	24.3	72	467	24.0	30	20	16.6667	90.667	Excellent	No major note
4	01APR2024	05JUL2025	05OCT2025	P004	4000	2300	27.1	78	460	12.0	20	20	15.3333	67.333	Good	High BMI but good activity
5	20MAY2024	20JUN2025	20SEP2025	P005	15000	3400	21.0	58	396	45.0	30	30	20.0000	100.000	Excellent	No major note
6	10JUN2024	30JUN2025	30SEP2025	P006	6000	2400	26.5	75	385	18.0	20	20	16.0000	74.000	Good	High BMI but good activity
7	01JUL2024	01JUL2025	01OCT2025	P007	10000	3000	23.4	66	365	30.0	30	30	20.0000	100.000	Excellent	No major note
8	15JUL2024	15JUL2025	15OCT2025	P008	2000	1900	31.2	88	365	6.0	10	10	12.6667	38.667	Poor	No major note
9	01AUG2024	10JUL2025	10OCT2025	P009	9000	2800	25.0	70	343	27.0	20	30	18.6667	95.667	Excellent	High BMI but good activity
10	05SEP2024	05JUL2025	05OCT2025	P010	11000	3100	22.0	64	303	33.0	30	30	20.0000	100.000	Excellent	No major note
11	10OCT2024	10JUL2025	10OCT2025	P011	4500	2250	28.4	79	273	13.5	20	20	15.0000	68.500	Good	High BMI but good activity
12	20NOV2024	20JUL2025	20OCT2025	P012	7000	2600	24.9	71	242	21.0	30	20	17.3333	88.333	Excellent	No major note

4. Use PROC SQL to create a summary table and to demonstrate SQL skills 

proc sql noprint;

  create table work.fitness_summary_sql as

  select

    count(Person_ID) as N,

    mean(Steps_Per_Day) as Mean_Steps format=8.1,

    median(Steps_Per_Day) as Median_Steps,

    min(Steps_Per_Day) as Min_Steps,

    max(Steps_Per_Day) as Max_Steps,

    mean(BMI) as Mean_BMI format=8.2,

    mean(Heart_Rate) as Mean_HR format=8.2

  from work.fitness_final;

quit;

proc print data=work.fitness_summary_sql noobs;

  title "Summary statistics (PROC SQL)";

run;

OUTPUT:

Summary statistics (PROC SQL)

N	Mean_Steps	Median_Steps	Min_Steps	Max_Steps	Mean_BMI	Mean_HR
12	7666.7	7500	2000	15000	25.51	72.08

5. PROC MEANS: numeric summaries by Fitness_Level 

proc means data=work.fitness_final n mean median std min max;

  class Fitness_Level;

  var Steps_Per_Day Calories_Burned BMI Heart_Rate Composite_Score Duration_Days;

  title "PROC MEANS by Fitness_Level";

run;

OUTPUT:

PROC MEANS by Fitness_Level

The MEANS Procedure

Fitness_Level	N Obs	Variable	N	Mean	Median	Std Dev	Minimum	Maximum
Average	1	Steps_Per_Day Calories_Burned BMI Heart_Rate Composite_Score Duration_Days	1 1 1 1 1 1	3500.00 2100.00 29.8000000 82.0000000 54.5000000 525.0000000	3500.00 2100.00 29.8000000 82.0000000 54.5000000 525.0000000	. . . . . .	3500.00 2100.00 29.8000000 82.0000000 54.5000000 525.0000000	3500.00 2100.00 29.8000000 82.0000000 54.5000000 525.0000000
Excellent	7	Steps_Per_Day Calories_Burned BMI Heart_Rate Composite_Score Duration_Days	7 7 7 7 7 7	10285.71 2942.86 23.3000000 66.1428571 96.3809524 380.4285714	10000.00 3000.00 23.4000000 66.0000000 100.0000000 365.0000000	2690.37 325.8688021 1.5318834 5.1777914 5.0052882 101.8820513	7000.00 2500.00 21.0000000 58.0000000 88.3333333 242.0000000	15000.00 3400.00 25.0000000 72.0000000 100.0000000 547.0000000
Good	3	Steps_Per_Day Calories_Burned BMI Heart_Rate Composite_Score Duration_Days	3 3 3 3 3 3	4833.33 2316.67 27.3333333 77.3333333 69.9444444 372.6666667	4500.00 2300.00 27.1000000 78.0000000 68.5000000 385.0000000	1040.83 76.3762616 0.9712535 2.0816660 3.5603267 94.1080939	4000.00 2250.00 26.5000000 75.0000000 67.3333333 273.0000000	6000.00 2400.00 28.4000000 79.0000000 74.0000000 460.0000000
Poor	1	Steps_Per_Day Calories_Burned BMI Heart_Rate Composite_Score Duration_Days	1 1 1 1 1 1	2000.00 1900.00 31.2000000 88.0000000 38.6666667 365.0000000	2000.00 1900.00 31.2000000 88.0000000 38.6666667 365.0000000	. . . . . .	2000.00 1900.00 31.2000000 88.0000000 38.6666667 365.0000000	2000.00 1900.00 31.2000000 88.0000000 38.6666667 365.0000000

6. PROC UNIVARIATE: check distribution of Composite_Score and Steps 

proc univariate data=work.fitness_final cibasic;

  var Composite_Score Steps_Per_Day;

  histogram Composite_Score / normal;

  inset mean median std / pos = ne;

  title "PROC UNIVARIATE: Composite_Score and Steps distribution";

run;

OUTPUT:

PROC UNIVARIATE: Composite_Score and Steps distribution

The UNIVARIATE Procedure

Variable: Composite_Score

Moments
N	12	Sum Weights	12
Mean	81.4722222	Sum Observations	977.666667
Std Deviation	20.6486766	Variance	426.367845
Skewness	-0.9179529	Kurtosis	-0.1506143
Uncorrected SS	84342.7222	Corrected SS	4690.0463
Coeff Variation	25.3444377	Std Error Mean	5.9607595

Basic Statistical Measures
Location		Variability
Mean	81.4722	Std Deviation	20.64868
Median	89.5000	Variance	426.36785
Mode	100.0000	Range	61.33333
		Interquartile Range	32.08333

Basic Confidence Limits Assuming Normality
Parameter	Estimate	95% Confidence Limits
Mean	81.47222	68.35268	94.59177
Std Deviation	20.64868	14.62742	35.05893
Variance	426.36785	213.96149	1229

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	13.66809	Pr > |t|	<.0001
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	100.0000
99%	100.0000
95%	100.0000
90%	100.0000
75% Q3	100.0000
50% Median	89.5000
25% Q1	67.9167
10%	54.5000
5%	38.6667
1%	38.6667
0% Min	38.6667

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
38.6667	8	95.6667	9
54.5000	2	100.0000	1
67.3333	4	100.0000	5
68.5000	11	100.0000	7
74.0000	6	100.0000	10

PROC UNIVARIATE: Composite_Score and Steps distribution

The UNIVARIATE Procedure

PROC UNIVARIATE: Composite_Score and Steps distribution

The UNIVARIATE Procedure

Fitted Normal Distribution for Composite_Score

Parameters for Normal Distribution
Parameter	Symbol	Estimate
Mean	Mu	81.47222
Std Dev	Sigma	20.64868

Goodness-of-Fit Tests for Normal Distribution
Test	Statistic		p Value
Kolmogorov-Smirnov	D	0.21349384	Pr > D	0.133
Cramer-von Mises	W-Sq	0.10996326	Pr > W-Sq	0.076
Anderson-Darling	A-Sq	0.66797269	Pr > A-Sq	0.063

Quantiles for Normal Distribution
Percent	Quantile
	Observed	Estimated
1.0	38.6667	33.4362
5.0	38.6667	47.5082
10.0	54.5000	55.0099
25.0	67.9167	67.5449
50.0	89.5000	81.4722
75.0	100.0000	95.3995
90.0	100.0000	107.9346
95.0	100.0000	115.4363
99.0	100.0000	129.5082

PROC UNIVARIATE: Composite_Score and Steps distribution

The UNIVARIATE Procedure

Variable: Steps_Per_Day

Moments
N	12	Sum Weights	12
Mean	7666.66667	Sum Observations	92000
Std Deviation	3898.32909	Variance	15196969.7
Skewness	0.34547102	Kurtosis	-0.6063581
Uncorrected SS	872500000	Corrected SS	167166667
Coeff Variation	50.8477707	Std Error Mean	1125.35067

Basic Statistical Measures
Location		Variability
Mean	7666.667	Std Deviation	3898
Median	7500.000	Variance	15196970
Mode	.	Range	13000
		Interquartile Range	6250

Basic Confidence Limits Assuming Normality
Parameter	Estimate	95% Confidence Limits
Mean	7667	5190	10144
Std Deviation	3898	2762	6619
Variance	15196970	7626199	43809669

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	6.812691	Pr > |t|	<.0001
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	15000
99%	15000
95%	15000
90%	12000
75% Q3	10500
50% Median	7500
25% Q1	4250
10%	3500
5%	2000
1%	2000
0% Min	2000

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
2000	8	9000	9
3500	2	10000	7
4000	4	11000	10
4500	11	12000	1
6000	6	15000	5

7. PROC FREQ for categorical distribution of Fitness_Level 

proc freq data=work.fitness_final;

  tables Fitness_Level / nocum norow nocol;

  title "Distribution of Fitness_Level (PROC FREQ)";

run;

OUTPUT:

Distribution of Fitness_Level (PROC FREQ)

The FREQ Procedure

Fitness_Level	Frequency	Percent
Average	1	8.33
Excellent	7	58.33
Good	3	25.00
Poor	1	8.33

8. PROC FORMAT for nice reporting 

proc format;

  value scorefmt

    low - <40 = 'Low (<40)'

    40 - <60 = 'Below Average (40-59)'

    60 - <80 = 'Good (60-79)'

    80 - high = 'Excellent (80+)';

run;

LOG:

NOTE: Format SCOREFMT has been output.

9. PROC RANK: rank individuals by Steps_Per_Day 

proc rank data=work.fitness_final out=work.fitness_rank ties=low descending;

  var Steps_Per_Day;

  ranks Steps_Rank;

run;

proc print data=work.fitness_rank noobs;

  var Person_ID Steps_Per_Day Steps_Rank Fitness_Level Composite_Score;

  title "Ranked by Steps_Per_Day";

run;

OUTPUT:

Ranked by Steps_Per_Day

Person_ID	Steps_Per_Day	Steps_Rank	Fitness_Level	Composite_Score
P001	12000	2	Excellent	100.000
P002	3500	11	Average	54.500
P003	8000	6	Excellent	90.667
P004	4000	10	Good	67.333
P005	15000	1	Excellent	100.000
P006	6000	8	Good	74.000
P007	10000	4	Excellent	100.000
P008	2000	12	Poor	38.667
P009	9000	5	Excellent	95.667
P010	11000	3	Excellent	100.000
P011	4500	9	Good	68.500
P012	7000	7	Excellent	88.333

10. PROC CORR: check correlation between Steps, Calories, BMI, HR, Composite 

proc corr data=work.fitness_final pearson nosimple;

  var Steps_Per_Day Calories_Burned BMI Heart_Rate Composite_Score;

  title "Correlation matrix (PROC CORR)";

run;

OUTPUT:

Correlation matrix (PROC CORR)

The CORR Procedure

5 Variables:	Steps_Per_Day Calories_Burned BMI Heart_Rate Composite_Score

Pearson Correlation Coefficients, N = 12 Prob > |r| under H0: Rho=0
	Steps_Per_Day	Calories_Burned	BMI	Heart_Rate	Composite_Score
Steps_Per_Day	1.00000	0.98446 <.0001	-0.95596 <.0001	-0.98147 <.0001	0.90224 <.0001
Calories_Burned	0.98446 <.0001	1.00000	-0.96049 <.0001	-0.98886 <.0001	0.91810 <.0001
BMI	-0.95596 <.0001	-0.96049 <.0001	1.00000	0.98401 <.0001	-0.96037 <.0001
Heart_Rate	-0.98147 <.0001	-0.98886 <.0001	0.98401 <.0001	1.00000	-0.94707 <.0001
Composite_Score	0.90224 <.0001	0.91810 <.0001	-0.96037 <.0001	-0.94707 <.0001	1.00000

11. PROC SGPLOT: scatter and series to visualize Steps vs Composite_Score 

proc sgplot data=work.fitness_final;

  scatter x=Steps_Per_Day y=Composite_Score / markerattrs=(symbol=CircleFilled);

  reg x=Steps_Per_Day y=Composite_Score / cli clm; /* regression line with CI */

  xaxis label="Steps Per Day";

  yaxis label="Composite Score";

  title "Steps vs Composite Score (Scatter + Regression)";

run;

OUTPUT:

12. PROC REPORT: polished report with formatted scores 

proc report data=work.fitness_final nowd;

  columns Person_ID Measurement_Date Steps_Per_Day Calories_Burned BMI Heart_Rate

          Composite_Score Fitness_Level;

  define Measurement_Date / display format=date9. 'Measurement';

  define Composite_Score / format=8.1 'Composite';

  title "Detailed fitness report (PROC REPORT)";

run;

title;

OUTPUT:

Detailed fitness report (PROC REPORT)

Person_ID	Measurement	Steps_Per_Day	Calories_Burned	BMI	Heart_Rate	Composite	Fitness_Level
P001	01JUL2025	12000	3200	22.5	62	100.0	Excellent
P002	10JUL2025	3500	2100	29.8	82	54.5	Average
P003	25JUN2025	8000	2500	24.3	72	90.7	Excellent
P004	05JUL2025	4000	2300	27.1	78	67.3	Good
P005	20JUN2025	15000	3400	21	58	100.0	Excellent
P006	30JUN2025	6000	2400	26.5	75	74.0	Good
P007	01JUL2025	10000	3000	23.4	66	100.0	Excellent
P008	15JUL2025	2000	1900	31.2	88	38.7	Poor
P009	10JUL2025	9000	2800	25	70	95.7	Excellent
P010	05JUL2025	11000	3100	22	64	100.0	Excellent
P011	10JUL2025	4500	2250	28.4	79	68.5	Good
P012	20JUL2025	7000	2600	24.9	71	88.3	Excellent

13. Example QC: flag outliers in steps (simple rule: <2500 or >20000) 

data work.fitness_qc;

  set work.fitness_final;

  if Steps_Per_Day < 2500 or Steps_Per_Day > 20000 then Steps_Flag = 'Outlier';

  else Steps_Flag = 'OK';

run;

proc print data=work.fitness_qc;

run;

OUTPUT:

Obs	Baseline_Date	Measurement_Date	Next_Check_Date	Person_ID	Steps_Per_Day	Calories_Burned	BMI	Heart_Rate	Duration_Days	Step_Score	BMI_Score	HR_Score	Cal_Score	Composite_Score	Fitness_Level	Fitness_Comment	Steps_Flag
1	01JAN2024	01JUL2025	01OCT2025	P001	12000	3200	22.5	62	547	36.0	30	30	20.0000	100.000	Excellent	No major note	OK
2	01FEB2024	10JUL2025	10OCT2025	P002	3500	2100	29.8	82	525	10.5	20	10	14.0000	54.500	Average	No major note	OK
3	15MAR2024	25JUN2025	25SEP2025	P003	8000	2500	24.3	72	467	24.0	30	20	16.6667	90.667	Excellent	No major note	OK
4	01APR2024	05JUL2025	05OCT2025	P004	4000	2300	27.1	78	460	12.0	20	20	15.3333	67.333	Good	High BMI but good activity	OK
5	20MAY2024	20JUN2025	20SEP2025	P005	15000	3400	21.0	58	396	45.0	30	30	20.0000	100.000	Excellent	No major note	OK
6	10JUN2024	30JUN2025	30SEP2025	P006	6000	2400	26.5	75	385	18.0	20	20	16.0000	74.000	Good	High BMI but good activity	OK
7	01JUL2024	01JUL2025	01OCT2025	P007	10000	3000	23.4	66	365	30.0	30	30	20.0000	100.000	Excellent	No major note	OK
8	15JUL2024	15JUL2025	15OCT2025	P008	2000	1900	31.2	88	365	6.0	10	10	12.6667	38.667	Poor	No major note	Outlier
9	01AUG2024	10JUL2025	10OCT2025	P009	9000	2800	25.0	70	343	27.0	20	30	18.6667	95.667	Excellent	High BMI but good activity	OK
10	05SEP2024	05JUL2025	05OCT2025	P010	11000	3100	22.0	64	303	33.0	30	30	20.0000	100.000	Excellent	No major note	OK
11	10OCT2024	10JUL2025	10OCT2025	P011	4500	2250	28.4	79	273	13.5	20	20	15.0000	68.500	Good	High BMI but good activity	OK
12	20NOV2024	20JUL2025	20OCT2025	P012	7000	2600	24.9	71	242	21.0	30	20	17.3333	88.333	Excellent	No major note	OK

14. Average of  Steps_Per_Day

proc avg data=work.fitness_final;

  var Steps_Per_Day;

run;

OUTPUT:

Obs	_TYPE_	_FREQ_	Avg_Steps
1	0	12	7666.67

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

YESTERDAY INVALID CODE ANSWER

9) Utility macro to list top N by a chosen metric 

%macro top_n(data=work.startups_cat, var=Growth_Rate, n=5);

  proc sort data=&data out=_top descending;

    by descending &var;

  run;

Descending has to place in by statement in PROC SORT;

  title "Top &n Startups by &var";

  proc print data=_top (obs=&n);

    var Startup_Name Industry &var Country Funding Employees Growth_Rate;

  run;

%mend top_n;

%top_n(data=work.startups_cat, var=Growth_Rate, n=5);

OUTPUT:

Top 5 Startups by Growth_Rate

Obs	Startup_Name	Industry	Growth_Rate	Country	Funding	Employees	Growth_Rate
1	NanoHealth Bio	Biotech	38.90	Germany	$25,000,000	200	38.90
2	MediSoft Labs	Healthcare IT	32.10	Canada	$12,000,000	120	32.10
3	BlockWave Systems	Blockchain	27.00	Singapore	$4,600,000	35	27.00
4	AeroScale Innovations	Aerospace	24.50	United States	$5,500,000	45	24.50
5	CyberGuard AI	Cybersecurity	23.20	Israel	$6,100,000	52	23.20

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

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