374.How Can SAS Be Used to Analyze Planetary Habitability?

How Can SAS Be Used to Analyze Planetary Habitability?

HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS ---DATA STEP | PROC SQL | MACROS | SGPLOT | DATE FUNCTIONS (MDY-INTCK-INTNX) |  APPEND | TRANSPOSE | PROC UNIVARIATE

1. BUSINESS / SCIENTIFIC CONTEXT

Why a “Planet Dataset”?

Although SAS is heavily used in clinical trials, banking, and insurance, interviewers often test conceptual strength using neutral domains like astronomy, geography, or products.

A Planet Analytics dataset is excellent because:

• It is non-clinical
• It contains continuous + categorical variables
• It naturally supports:
• classification
• aggregation
• statistical analysis
• visualization
• macro automation

This project simulates how SAS would be used to:

• analyze planetary characteristics
• classify habitability
• study distance vs temperature
• prepare summary statistics
• automate business logic using macros

2. DATASET DESIGN

Variables Included

Variable	Description
Planet_Name	Name of the planet
Type	Terrestrial / Gas Giant / Ice Giant / Dwarf
Distance	Distance from Sun (Million km)
Gravity	Surface gravity (m/s²)
Temperature	Average temperature (°C)
Habitability_Index	Numeric score (0–100)
Discovery_Date	SAS date
Observation_Date	Derived using INTNX

3. COMPLETE SAS CODE

STEP 1: CREATE BASE PLANET DATASET (Using DATA STEP + MDY)

data planets_base;

    length Planet_Name $15 Type $12;

    format Discovery_Date Observation_Date date9.;

    input Planet_Name $ Type $ Distance Gravity Temperature Habitability_Index 

          Discovery_Date :date9.;

    Observation_Date = intnx('year', Discovery_Date, 5, 'same');

    datalines;

Mercury Terrestrial 57.9 3.7 167 5 01JAN1631

Venus Terrestrial 108.2 8.9 464 2 01JAN1610

Earth Terrestrial 149.6 9.8 15 95 01JAN0001

Mars Terrestrial 227.9 3.7 -65 60 01JAN1659

Jupiter GasGiant 778.5 24.8 -110 1 01JAN1610

Saturn GasGiant 1434 10.4 -140 1 01JAN1610

Uranus IceGiant 2871 8.7 -195 0 01JAN1781

Neptune IceGiant 4495 11.2 -200 0 01JAN1846

Pluto Dwarf 5906 0.6 -225 0 01JAN1930

Kepler22b Exoplanet 600000 11.0 22 85 05DEC2011

ProximaB Exoplanet 40100 11.0 -39 70 24AUG2016

TRAPPIST1e Exoplanet 39000 9.1 -1 88 22FEB2017

TRAPPIST1f Exoplanet 39000 8.3 -27 75 22FEB2017

TRAPPIST1g Exoplanet 39000 8.9 -75 65 22FEB2017

Kepler452b Exoplanet 1400000 19.6 10 90 23JUL2015

Gliese667Cc Exoplanet 230000 14.3 -25 80 21NOV2011

;

run;

proc print data=planets_base;

run;

OUTPUT:

Obs	Planet_Name	Type	Discovery_Date	Observation_Date	Distance	Gravity	Temperature	Habitability_Index
1	Mercury	Terrestrial	01JAN1631	01JAN1636	57.9	3.7	167	5
2	Venus	Terrestrial	01JAN1610	01JAN1615	108.2	8.9	464	2
3	Earth	Terrestrial	01JAN2001	01JAN2006	149.6	9.8	15	95
4	Mars	Terrestrial	01JAN1659	01JAN1664	227.9	3.7	-65	60
5	Jupiter	GasGiant	01JAN1610	01JAN1615	778.5	24.8	-110	1
6	Saturn	GasGiant	01JAN1610	01JAN1615	1434.0	10.4	-140	1
7	Uranus	IceGiant	01JAN1781	01JAN1786	2871.0	8.7	-195	0
8	Neptune	IceGiant	01JAN1846	01JAN1851	4495.0	11.2	-200	0
9	Pluto	Dwarf	01JAN1930	01JAN1935	5906.0	0.6	-225	0
10	Kepler22b	Exoplanet	05DEC2011	05DEC2016	600000.0	11.0	22	85
11	ProximaB	Exoplanet	24AUG2016	24AUG2021	40100.0	11.0	-39	70
12	TRAPPIST1e	Exoplanet	22FEB2017	22FEB2022	39000.0	9.1	-1	88
13	TRAPPIST1f	Exoplanet	22FEB2017	22FEB2022	39000.0	8.3	-27	75
14	TRAPPIST1g	Exoplanet	22FEB2017	22FEB2022	39000.0	8.9	-75	65
15	Kepler452b	Exoplanet	23JUL2015	23JUL2020	1400000.0	19.6	10	90
16	Gliese667Cc	Exoplanet	21NOV2011	21NOV2016	230000.0	14.3	-25	80

Why DATA step here?

·       Full control over:

o   variable lengths

o   formats

o   date logic

·       Faster than SQL for row-level derivations

Each observation is read, processed, and written sequentially.

Intnx Used to:

·       shift dates forward/backward

·       maintain alignment (same, beginning, end)

STEP 2: CREATE ADDITIONAL DATASET & APPEND

data planets_new;

    length Planet_Name $15 Type $12;

    format Discovery_Date date9.;

    Planet_Name = "LHS1140b";

    Type = "Exoplanet";

    Distance = 41000;

    Gravity = 15.0;

    Temperature = -10;

    Habitability_Index = 82;

    Discovery_Date = mdy(4,19,2017);

run;

proc print data=planets_new;

run;

OUTPUT:

Obs	Planet_Name	Type	Discovery_Date	Distance	Gravity	Temperature	Habitability_Index
1	LHS1140b	Exoplanet	19APR2017	41000	15	-10	82

MDY() Used to:

·  Converts month/day/year → SAS date

·  Stored as number of days since 01JAN1960

proc append base=planets_base 

            data=planets_new force;

run;

proc print data=planets_base;

run;

OUTPUT:

Obs	Planet_Name	Type	Discovery_Date	Observation_Date	Distance	Gravity	Temperature	Habitability_Index
1	Mercury	Terrestrial	01JAN1631	01JAN1636	57.9	3.7	167	5
2	Venus	Terrestrial	01JAN1610	01JAN1615	108.2	8.9	464	2
3	Earth	Terrestrial	01JAN2001	01JAN2006	149.6	9.8	15	95
4	Mars	Terrestrial	01JAN1659	01JAN1664	227.9	3.7	-65	60
5	Jupiter	GasGiant	01JAN1610	01JAN1615	778.5	24.8	-110	1
6	Saturn	GasGiant	01JAN1610	01JAN1615	1434.0	10.4	-140	1
7	Uranus	IceGiant	01JAN1781	01JAN1786	2871.0	8.7	-195	0
8	Neptune	IceGiant	01JAN1846	01JAN1851	4495.0	11.2	-200	0
9	Pluto	Dwarf	01JAN1930	01JAN1935	5906.0	0.6	-225	0
10	Kepler22b	Exoplanet	05DEC2011	05DEC2016	600000.0	11.0	22	85
11	ProximaB	Exoplanet	24AUG2016	24AUG2021	40100.0	11.0	-39	70
12	TRAPPIST1e	Exoplanet	22FEB2017	22FEB2022	39000.0	9.1	-1	88
13	TRAPPIST1f	Exoplanet	22FEB2017	22FEB2022	39000.0	8.3	-27	75
14	TRAPPIST1g	Exoplanet	22FEB2017	22FEB2022	39000.0	8.9	-75	65
15	Kepler452b	Exoplanet	23JUL2015	23JUL2020	1400000.0	19.6	10	90
16	Gliese667Cc	Exoplanet	21NOV2011	21NOV2016	230000.0	14.3	-25	80
17	LHS1140b	Exoplanet	19APR2017	.	41000.0	15.0	-10	82

APPEND

·       Faster than SET

·       Does NOT read base dataset

·       Common in production pipelines

STEP 3: HABITABLE ZONE CLASSIFICATION MACRO

%macro habitable_zone(input=, output=);

data &output;

    set &input;

    length Habitable_Zone $17;

    if Habitability_Index >= 80 then Habitable_Zone = "Highly Habitable";

    else if Habitability_Index >= 50 then Habitable_Zone = "Potential";

    else Habitable_Zone = "Non-Habitable";

run;

proc print data=&output;

run;

%mend habitable_zone;

%habitable_zone(input=planets_base, output=planets_classified);

OUTPUT:

Obs	Planet_Name	Type	Discovery_Date	Observation_Date	Distance	Gravity	Temperature	Habitability_Index	Habitable_Zone
1	Mercury	Terrestrial	01JAN1631	01JAN1636	57.9	3.7	167	5	Non-Habitable
2	Venus	Terrestrial	01JAN1610	01JAN1615	108.2	8.9	464	2	Non-Habitable
3	Earth	Terrestrial	01JAN2001	01JAN2006	149.6	9.8	15	95	Highly Habitable
4	Mars	Terrestrial	01JAN1659	01JAN1664	227.9	3.7	-65	60	Potential
5	Jupiter	GasGiant	01JAN1610	01JAN1615	778.5	24.8	-110	1	Non-Habitable
6	Saturn	GasGiant	01JAN1610	01JAN1615	1434.0	10.4	-140	1	Non-Habitable
7	Uranus	IceGiant	01JAN1781	01JAN1786	2871.0	8.7	-195	0	Non-Habitable
8	Neptune	IceGiant	01JAN1846	01JAN1851	4495.0	11.2	-200	0	Non-Habitable
9	Pluto	Dwarf	01JAN1930	01JAN1935	5906.0	0.6	-225	0	Non-Habitable
10	Kepler22b	Exoplanet	05DEC2011	05DEC2016	600000.0	11.0	22	85	Highly Habitable
11	ProximaB	Exoplanet	24AUG2016	24AUG2021	40100.0	11.0	-39	70	Potential
12	TRAPPIST1e	Exoplanet	22FEB2017	22FEB2022	39000.0	9.1	-1	88	Highly Habitable
13	TRAPPIST1f	Exoplanet	22FEB2017	22FEB2022	39000.0	8.3	-27	75	Potential
14	TRAPPIST1g	Exoplanet	22FEB2017	22FEB2022	39000.0	8.9	-75	65	Potential
15	Kepler452b	Exoplanet	23JUL2015	23JUL2020	1400000.0	19.6	10	90	Highly Habitable
16	Gliese667Cc	Exoplanet	21NOV2011	21NOV2016	230000.0	14.3	-25	80	Highly Habitable
17	LHS1140b	Exoplanet	19APR2017	.	41000.0	15.0	-10	82	Highly Habitable

Macro:

• avoids repetition
• enforces consistency
• allows parameter-driven logic

STEP 4: PROC SQL – ANALYTICS

proc sql;

    create table planet_summary as

    select 

        Type,

        count(*) as Planet_Count,

        avg(Gravity) as Avg_Gravity format=6.2,

        avg(Temperature) as Avg_Temp format=6.1

    from planets_classified

    group by Type;

quit;

proc print data=planet_summary;

run;

OUTPUT:

Obs	Type	Planet_Count	Avg_Gravity	Avg_Temp
1	Dwarf	1	0.60	-225.0
2	Exoplanet	8	12.15	-18.1
3	GasGiant	2	17.60	-125.0
4	IceGiant	2	9.95	-197.5
5	Terrestrial	4	6.53	145.3

PROC SQL VS DATA STEP

PROC SQL	DATA STEP
Set-based	Row-based
Aggregation	Derivation
Joins	Conditional logic
Easier grouping	More control

·       In interviews, saying “I choose SQL for summarization and DATA step for derivation” is a strong answer.

STEP 5: DATE ANALYSIS USING INTCK

data planets_dates;

    set planets_classified;

    Years_Since_Discovery = intck('year', Discovery_Date, today());

run;

proc print data=planets_dates;

run;

OUTPUT:

Obs	Planet_Name	Type	Discovery_Date	Observation_Date	Distance	Gravity	Temperature	Habitability_Index	Habitable_Zone	Years_Since_Discovery
1	Mercury	Terrestrial	01JAN1631	01JAN1636	57.9	3.7	167	5	Non-Habitable	395
2	Venus	Terrestrial	01JAN1610	01JAN1615	108.2	8.9	464	2	Non-Habitable	416
3	Earth	Terrestrial	01JAN2001	01JAN2006	149.6	9.8	15	95	Highly Habitable	25
4	Mars	Terrestrial	01JAN1659	01JAN1664	227.9	3.7	-65	60	Potential	367
5	Jupiter	GasGiant	01JAN1610	01JAN1615	778.5	24.8	-110	1	Non-Habitable	416
6	Saturn	GasGiant	01JAN1610	01JAN1615	1434.0	10.4	-140	1	Non-Habitable	416
7	Uranus	IceGiant	01JAN1781	01JAN1786	2871.0	8.7	-195	0	Non-Habitable	245
8	Neptune	IceGiant	01JAN1846	01JAN1851	4495.0	11.2	-200	0	Non-Habitable	180
9	Pluto	Dwarf	01JAN1930	01JAN1935	5906.0	0.6	-225	0	Non-Habitable	96
10	Kepler22b	Exoplanet	05DEC2011	05DEC2016	600000.0	11.0	22	85	Highly Habitable	15
11	ProximaB	Exoplanet	24AUG2016	24AUG2021	40100.0	11.0	-39	70	Potential	10
12	TRAPPIST1e	Exoplanet	22FEB2017	22FEB2022	39000.0	9.1	-1	88	Highly Habitable	9
13	TRAPPIST1f	Exoplanet	22FEB2017	22FEB2022	39000.0	8.3	-27	75	Potential	9
14	TRAPPIST1g	Exoplanet	22FEB2017	22FEB2022	39000.0	8.9	-75	65	Potential	9
15	Kepler452b	Exoplanet	23JUL2015	23JUL2020	1400000.0	19.6	10	90	Highly Habitable	11
16	Gliese667Cc	Exoplanet	21NOV2011	21NOV2016	230000.0	14.3	-25	80	Highly Habitable	15
17	LHS1140b	Exoplanet	19APR2017	.	41000.0	15.0	-10	82	Highly Habitable	9

Intck Counts:

• number of year boundaries crossed
• commonly used in:
• age calculation
• study duration
• exposure time

STEP 7: PROC UNIVARIATE

proc univariate data=planets_dates;

    var Habitability_Index;

    histogram / normal;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Habitability_Index

Moments
N	17	Sum Weights	17
Mean	47	Sum Observations	799
Std Deviation	40.346933	Variance	1627.875
Skewness	-0.2550592	Kurtosis	-2.0020696
Uncorrected SS	63599	Corrected SS	26046
Coeff Variation	85.8445382	Std Error Mean	9.78556861

Basic Statistical Measures
Location		Variability
Mean	47.00000	Std Deviation	40.34693
Median	65.00000	Variance	1628
Mode	0.00000	Range	95.00000
		Interquartile Range	81.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	4.802991	Pr > |t|	0.0002
Sign	M	7	Pr >= |M|	0.0001
Signed Rank	S	52.5	Pr >= |S|	0.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	95
99%	95
95%	95
90%	90
75% Q3	82
50% Median	65
25% Q1	1
10%	0
5%	0
1%	0
0% Min	0

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
0	9	82	17
0	8	85	10
0	7	88	12
1	6	90	15
1	5	95	3

The UNIVARIATE Procedure

The UNIVARIATE Procedure

Fitted Normal Distribution for Habitability_Index

Parameters for Normal Distribution
Parameter	Symbol	Estimate
Mean	Mu	47
Std Dev	Sigma	40.34693

Goodness-of-Fit Tests for Normal Distribution
Test	Statistic		p Value
Kolmogorov-Smirnov	D	0.26282028	Pr > D	<0.010
Cramer-von Mises	W-Sq	0.26981561	Pr > W-Sq	<0.005
Anderson-Darling	A-Sq	1.64457364	Pr > A-Sq	<0.005

Quantiles for Normal Distribution
Percent	Quantile
	Observed	Estimated
1.0	0.0000	-46.86100
5.0	0.0000	-19.36480
10.0	0.0000	-4.70668
25.0	1.0000	19.78641
50.0	65.0000	47.00000
75.0	82.0000	74.21359
90.0	90.0000	98.70668
95.0	95.0000	113.36480
99.0	95.0000	140.86100

PROC UNIVARIATE

·       Distribution analysis

·       Normality testing

·       Outlier detection

STEP 8: PROC SGPLOT – VISUALIZATION

proc sgplot data=planets_dates;

    scatter x=Distance y=Temperature / group=Habitable_Zone;

    xaxis label="Distance from Sun (Million km)";

    yaxis label="Average Temperature (°C)";

    title "Planet Distance vs Temperature by Habitability";

run;

OUTPUT:

Scatter plot helps answer:

• Are distant planets colder?
• Do habitable planets cluster?

Visualization is increasingly important in:

• clinical reviews
• management reporting
• regulatory storytelling

STEP 9: PROC TRANSPOSE

proc transpose data=planet_summary out=planet_summary_t;

    by Type NotSorted;

    var Planet_Count Avg_Gravity Avg_Temp;

run;

proc print data=planet_summary_t;

run;

OUTPUT:

Obs	Type	_NAME_	COL1
1	Dwarf	Planet_Count	1.000
2	Dwarf	Avg_Gravity	0.600
3	Dwarf	Avg_Temp	-225.000
4	Exoplanet	Planet_Count	8.000
5	Exoplanet	Avg_Gravity	12.150
6	Exoplanet	Avg_Temp	-18.125
7	GasGiant	Planet_Count	2.000
8	GasGiant	Avg_Gravity	17.600
9	GasGiant	Avg_Temp	-125.000
10	IceGiant	Planet_Count	2.000
11	IceGiant	Avg_Gravity	9.950
12	IceGiant	Avg_Temp	-197.500
13	Terrestrial	Planet_Count	4.000
14	Terrestrial	Avg_Gravity	6.525
15	Terrestrial	Avg_Temp	145.250

Transpose:

• Converts rows → columns
• Required for:
• reporting tables
• shells
• regulatory outputs

4. INTERVIEW QUESTIONS THIS PROJECT PREPARES YOU FOR

1.     Difference between INTNX and INTCK?

2.     When to use PROC SQL vs DATA step?

3.     Why use macros?

4.     Difference between SET and APPEND?

5.     How SAS handles dates internally?

6.     How to classify data dynamically?

7.     How to perform EDA in SAS?

8.     How to visualize relationships?

9.     How to structure reusable code?




Conclusion:

This project demonstrated an end-to-end SAS workflow using a planetary dataset, covering data creation, date handling, macros, SQL-based summarization, statistical analysis, and visualization. By integrating multiple SAS procedures and functions in a single, coherent use case, it highlights how complex business or scientific logic can be automated, analyzed, and clearly presented—skills directly transferable to real-world SAS programming and interview scenarios.

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 real Planetory data.

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