372.CHEMICAL ELEMENTS ANALYSIS USING SAS

CHEMICAL ELEMENTS ANALYSIS USING SAS

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

1. Project Overview

Chemical elements form the foundation of chemistry, materials science, pharmaceuticals, metallurgy, and electronics. Each element has unique physical and chemical properties such as atomic number, atomic weight, melting point, and industrial or medical usage.

In this project, we create a custom chemical elements dataset and analyze it using SAS programming techniques, focusing on:

·       Data creation and structuring

·       Date handling and time intelligence

·       Statistical analysis

·       Classification using macros

·       Dataset transformation and reshaping

·       Real-world reporting readiness

This project is intentionally designed to simulate enterprise-level SAS work, not just academic examples.

2. Dataset Design

2.1 Variables Included

Variable Name	Description
Element_Name	Name of the chemical element
Atomic_Number	Number of protons
Weight	Atomic weight
Melting_Point	Melting point in °C
Usage	Primary industrial / scientific usage
Discovery_Date	Date the element was discovered

3. Creating the Base Dataset (DATA Step)

data elements_raw;

    length Element_Name $15 Usage $40;

    format Discovery_Date date9.;

    input Element_Name $ Atomic_Number Weight Melting_Point Usage $

          Discovery_Date :date9.;

datalines;

Hydrogen 1 1.008 -259 Fuel 01JAN1766

Helium 2 4.0026 -272 Cryogenics 01JAN1895

Lithium 3 6.94 180 Batteries 01JAN1817

Carbon 6 12.011 3550 OrganicChem 01JAN0000

Nitrogen 7 14.007 -210 Fertilizers 01JAN1772

Oxygen 8 15.999 -218 MedicalUse 01JAN1774

Sodium 11 22.99 98 SaltProduction 01JAN1807

Magnesium 12 24.305 650 Alloys 01JAN1755

Aluminum 13 26.98 660 Construction 01JAN1825

Silicon 14 28.085 1414 Electronics 01JAN1824

Phosphorus 15 30.974 44 Agriculture 01JAN1669

Sulfur 16 32.06 115 Chemicals 01JAN1777

Chlorine 17 35.45 -101 WaterTreatment 01JAN1774

Iron 26 55.845 1538 Steel 01JAN0000

Copper 29 63.546 1085 Wiring 01JAN0000

Zinc 30 65.38 420 Galvanization 01JAN1746

Silver 47 107.8682 962 Jewelry 01JAN0000

Gold 79 196.97 1064 Investment 01JAN0000

;

run;

proc print data=elements_raw;

run;

OUTPUT:

Obs	Element_Name	Usage	Discovery_Date	Atomic_Number	Weight	Melting_Point
1	Hydrogen	Fuel	01JAN1766	1	1.008	-259
2	Helium	Cryogenics	01JAN1895	2	4.003	-272
3	Lithium	Batteries	01JAN1817	3	6.940	180
4	Carbon	OrganicChem	01JAN2000	6	12.011	3550
5	Nitrogen	Fertilizers	01JAN1772	7	14.007	-210
6	Oxygen	MedicalUse	01JAN1774	8	15.999	-218
7	Sodium	SaltProduction	01JAN1807	11	22.990	98
8	Magnesium	Alloys	01JAN1755	12	24.305	650
9	Aluminum	Construction	01JAN1825	13	26.980	660
10	Silicon	Electronics	01JAN1824	14	28.085	1414
11	Phosphorus	Agriculture	01JAN1669	15	30.974	44
12	Sulfur	Chemicals	01JAN1777	16	32.060	115
13	Chlorine	WaterTreatment	01JAN1774	17	35.450	-101
14	Iron	Steel	01JAN2000	26	55.845	1538
15	Copper	Wiring	01JAN2000	29	63.546	1085
16	Zinc	Galvanization	01JAN1746	30	65.380	420
17	Silver	Jewelry	01JAN2000	47	107.868	962
18	Gold	Investment	01JAN2000	79	196.970	1064

Key Concepts Learned

·       Variable length control

·       Informats and formats

·       Date literal handling

·       Realistic domain data modeling

4. Date Intelligence and Derived Variables

4.1 Adding Study Dates Using MDY, INTNX, INTCK

data elements_dates;

    set elements_raw;

    Reference_Date = mdy(1,1,2025);

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

    Next_Review_Date = intnx('year', Discovery_Date, 300, 'same');

    format Reference_Date Next_Review_Date date9.;

run;

proc print data=elements_dates;

run;

OUTPUT:

Obs	Element_Name	Usage	Discovery_Date	Atomic_Number	Weight	Melting_Point	Reference_Date	Years_Since_Discovery	Next_Review_Date
1	Hydrogen	Fuel	01JAN1766	1	1.008	-259	01JAN2025	259	01JAN2066
2	Helium	Cryogenics	01JAN1895	2	4.003	-272	01JAN2025	130	01JAN2195
3	Lithium	Batteries	01JAN1817	3	6.940	180	01JAN2025	208	01JAN2117
4	Carbon	OrganicChem	01JAN2000	6	12.011	3550	01JAN2025	25	01JAN2300
5	Nitrogen	Fertilizers	01JAN1772	7	14.007	-210	01JAN2025	253	01JAN2072
6	Oxygen	MedicalUse	01JAN1774	8	15.999	-218	01JAN2025	251	01JAN2074
7	Sodium	SaltProduction	01JAN1807	11	22.990	98	01JAN2025	218	01JAN2107
8	Magnesium	Alloys	01JAN1755	12	24.305	650	01JAN2025	270	01JAN2055
9	Aluminum	Construction	01JAN1825	13	26.980	660	01JAN2025	200	01JAN2125
10	Silicon	Electronics	01JAN1824	14	28.085	1414	01JAN2025	201	01JAN2124
11	Phosphorus	Agriculture	01JAN1669	15	30.974	44	01JAN2025	356	01JAN1969
12	Sulfur	Chemicals	01JAN1777	16	32.060	115	01JAN2025	248	01JAN2077
13	Chlorine	WaterTreatment	01JAN1774	17	35.450	-101	01JAN2025	251	01JAN2074
14	Iron	Steel	01JAN2000	26	55.845	1538	01JAN2025	25	01JAN2300
15	Copper	Wiring	01JAN2000	29	63.546	1085	01JAN2025	25	01JAN2300
16	Zinc	Galvanization	01JAN1746	30	65.380	420	01JAN2025	279	01JAN2046
17	Silver	Jewelry	01JAN2000	47	107.868	962	01JAN2025	25	01JAN2300
18	Gold	Investment	01JAN2000	79	196.970	1064	01JAN2025	25	01JAN2300

Explanation

·       MDY() constructs dates explicitly

·       INTCK() calculates elapsed time

·       INTNX() projects future dates

·       These functions are heavily tested in interviews

5. PROC SQL – Data Querying & Business Logic

5.1 Creating a Filtered Table

proc sql;

    create table high_temp_elements as

    select Element_Name,Atomic_Number,Melting_Point,Usage

    from elements_dates

    where Melting_Point > 1000;

quit;

proc print data=high_temp_elements;

run;

OUTPUT:

Obs	Element_Name	Atomic_Number	Melting_Point	Usage
1	Carbon	6	3550	OrganicChem
2	Silicon	14	1414	Electronics
3	Iron	26	1538	Steel
4	Copper	29	1085	Wiring
5	Gold	79	1064	Investment

What This Demonstrates

·       SQL-style filtering

·       Dataset creation

·       Conditional logic

·       Industry relevance (high-temperature materials)

6. Statistical Analysis

6.1 PROC MEANS

proc means data=elements_dates mean min max std;

    var Weight Melting_Point Years_Since_Discovery;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum	Std Dev
Weight Melting_Point Years_Since_Discovery	41.3567111 595.5555556 180.5000000	1.0080000 -272.0000000 25.0000000	196.9700000 3550.00 356.0000000	47.0411336 944.0932296 108.6934437

Why PROC MEANS Matters

·       Central tendency

·       Variability analysis

·       Mandatory for clinical & industrial analytics

6.2 PROC UNIVARIATE

proc univariate data=elements_dates;

    var Melting_Point;

    histogram Melting_Point;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Melting_Point

Moments
N	18	Sum Weights	18
Mean	595.555556	Sum Observations	10720
Std Deviation	944.09323	Variance	891312.026
Skewness	1.91009777	Kurtosis	4.82690524
Uncorrected SS	21536660	Corrected SS	15152304.4
Coeff Variation	158.523117	Std Error Mean	222.524908

Basic Statistical Measures
Location		Variability
Mean	595.5556	Std Deviation	944.09323
Median	300.0000	Variance	891312
Mode	.	Range	3822
		Interquartile Range	1165

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	2.676355	Pr > |t|	0.0159
Sign	M	4	Pr >= |M|	0.0963
Signed Rank	S	52.5	Pr >= |S|	0.0208

Quantiles (Definition 5)
Level	Quantile
100% Max	3550
99%	3550
95%	3550
90%	1538
75% Q3	1064
50% Median	300
25% Q1	-101
10%	-259
5%	-272
1%	-272
0% Min	-272

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
-272	2	1064	18
-259	1	1085	15
-218	6	1414	10
-210	5	1538	14
-101	13	3550	4

The UNIVARIATE Procedure

What This Adds

·       Distribution understanding

·       Outlier detection

·       Regulatory-grade statistics

7. Macro Programming – Classification Logic

7.1 Macro to Classify Elements by Melting Point

%macro classify_element;

    data classified_elements;

        set elements_dates;

        if Melting_Point < 0 then Category = "Gas or Cryogenic";

        else if Melting_Point < 500 then Category = "Low Melting";

        else if Melting_Point < 1000 then Category = "Medium Melting";

        else Category = "High Melting";

    run;

proc print data=classified_elements;

run;

%mend;

%classify_element;

OUTPUT:

Obs	Element_Name	Usage	Discovery_Date	Atomic_Number	Weight	Melting_Point	Reference_Date	Years_Since_Discovery	Next_Review_Date	Category
1	Hydrogen	Fuel	01JAN1766	1	1.008	-259	01JAN2025	259	01JAN2066	Gas or Cryogenic
2	Helium	Cryogenics	01JAN1895	2	4.003	-272	01JAN2025	130	01JAN2195	Gas or Cryogenic
3	Lithium	Batteries	01JAN1817	3	6.940	180	01JAN2025	208	01JAN2117	Low Melting
4	Carbon	OrganicChem	01JAN2000	6	12.011	3550	01JAN2025	25	01JAN2300	High Melting
5	Nitrogen	Fertilizers	01JAN1772	7	14.007	-210	01JAN2025	253	01JAN2072	Gas or Cryogenic
6	Oxygen	MedicalUse	01JAN1774	8	15.999	-218	01JAN2025	251	01JAN2074	Gas or Cryogenic
7	Sodium	SaltProduction	01JAN1807	11	22.990	98	01JAN2025	218	01JAN2107	Low Melting
8	Magnesium	Alloys	01JAN1755	12	24.305	650	01JAN2025	270	01JAN2055	Medium Melting
9	Aluminum	Construction	01JAN1825	13	26.980	660	01JAN2025	200	01JAN2125	Medium Melting
10	Silicon	Electronics	01JAN1824	14	28.085	1414	01JAN2025	201	01JAN2124	High Melting
11	Phosphorus	Agriculture	01JAN1669	15	30.974	44	01JAN2025	356	01JAN1969	Low Melting
12	Sulfur	Chemicals	01JAN1777	16	32.060	115	01JAN2025	248	01JAN2077	Low Melting
13	Chlorine	WaterTreatment	01JAN1774	17	35.450	-101	01JAN2025	251	01JAN2074	Gas or Cryogenic
14	Iron	Steel	01JAN2000	26	55.845	1538	01JAN2025	25	01JAN2300	High Melting
15	Copper	Wiring	01JAN2000	29	63.546	1085	01JAN2025	25	01JAN2300	High Melting
16	Zinc	Galvanization	01JAN1746	30	65.380	420	01JAN2025	279	01JAN2046	Low Melting
17	Silver	Jewelry	01JAN2000	47	107.868	962	01JAN2025	25	01JAN2300	Medium Melting
18	Gold	Investment	01JAN2000	79	196.970	1064	01JAN2025	25	01JAN2300	High Melting

Why Macros Are Critical

·       Reusability

·       Automation

·       Interview favorite topic

·       Production efficiency

8. Dataset Combination Techniques

8.1 SET Statement

data combined_set;

    set elements_raw 

        high_temp_elements;

run;

proc print data=combined_set;

run;

OUTPUT:

Obs	Element_Name	Usage	Discovery_Date	Atomic_Number	Weight	Melting_Point
1	Hydrogen	Fuel	01JAN1766	1	1.008	-259
2	Helium	Cryogenics	01JAN1895	2	4.003	-272
3	Lithium	Batteries	01JAN1817	3	6.940	180
4	Carbon	OrganicChem	01JAN2000	6	12.011	3550
5	Nitrogen	Fertilizers	01JAN1772	7	14.007	-210
6	Oxygen	MedicalUse	01JAN1774	8	15.999	-218
7	Sodium	SaltProduction	01JAN1807	11	22.990	98
8	Magnesium	Alloys	01JAN1755	12	24.305	650
9	Aluminum	Construction	01JAN1825	13	26.980	660
10	Silicon	Electronics	01JAN1824	14	28.085	1414
11	Phosphorus	Agriculture	01JAN1669	15	30.974	44
12	Sulfur	Chemicals	01JAN1777	16	32.060	115
13	Chlorine	WaterTreatment	01JAN1774	17	35.450	-101
14	Iron	Steel	01JAN2000	26	55.845	1538
15	Copper	Wiring	01JAN2000	29	63.546	1085
16	Zinc	Galvanization	01JAN1746	30	65.380	420
17	Silver	Jewelry	01JAN2000	47	107.868	962
18	Gold	Investment	01JAN2000	79	196.970	1064
19	Carbon	OrganicChem	.	6	.	3550
20	Silicon	Electronics	.	14	.	1414
21	Iron	Steel	.	26	.	1538
22	Copper	Wiring	.	29	.	1085
23	Gold	Investment	.	79	.	1064

Interview Insight

Difference between SET and MERGE is a classic SAS interview question.

8.2 APPEND

proc append base=elements_raw

            data=high_temp_elements force;

run;

proc print data=elements_raw;

run;

OUTPUT:

Obs	Element_Name	Usage	Discovery_Date	Atomic_Number	Weight	Melting_Point
1	Hydrogen	Fuel	01JAN1766	1	1.008	-259
2	Helium	Cryogenics	01JAN1895	2	4.003	-272
3	Lithium	Batteries	01JAN1817	3	6.940	180
4	Carbon	OrganicChem	01JAN2000	6	12.011	3550
5	Nitrogen	Fertilizers	01JAN1772	7	14.007	-210
6	Oxygen	MedicalUse	01JAN1774	8	15.999	-218
7	Sodium	SaltProduction	01JAN1807	11	22.990	98
8	Magnesium	Alloys	01JAN1755	12	24.305	650
9	Aluminum	Construction	01JAN1825	13	26.980	660
10	Silicon	Electronics	01JAN1824	14	28.085	1414
11	Phosphorus	Agriculture	01JAN1669	15	30.974	44
12	Sulfur	Chemicals	01JAN1777	16	32.060	115
13	Chlorine	WaterTreatment	01JAN1774	17	35.450	-101
14	Iron	Steel	01JAN2000	26	55.845	1538
15	Copper	Wiring	01JAN2000	29	63.546	1085
16	Zinc	Galvanization	01JAN1746	30	65.380	420
17	Silver	Jewelry	01JAN2000	47	107.868	962
18	Gold	Investment	01JAN2000	79	196.970	1064
19	Carbon	OrganicChem	.	6	.	3550
20	Silicon	Electronics	.	14	.	1414
21	Iron	Steel	.	26	.	1538
22	Copper	Wiring	.	29	.	1085
23	Gold	Investment	.	79	.	1064

8.3 MERGE

proc sort data=elements_raw; by Element_Name; run;

proc print data=elements_raw;

run;

OUTPUT:

Obs	Element_Name	Usage	Discovery_Date	Atomic_Number	Weight	Melting_Point
1	Aluminum	Construction	01JAN1825	13	26.980	660
2	Carbon	OrganicChem	01JAN2000	6	12.011	3550
3	Carbon	OrganicChem	.	6	.	3550
4	Chlorine	WaterTreatment	01JAN1774	17	35.450	-101
5	Copper	Wiring	01JAN2000	29	63.546	1085
6	Copper	Wiring	.	29	.	1085
7	Gold	Investment	01JAN2000	79	196.970	1064
8	Gold	Investment	.	79	.	1064
9	Helium	Cryogenics	01JAN1895	2	4.003	-272
10	Hydrogen	Fuel	01JAN1766	1	1.008	-259
11	Iron	Steel	01JAN2000	26	55.845	1538
12	Iron	Steel	.	26	.	1538
13	Lithium	Batteries	01JAN1817	3	6.940	180
14	Magnesium	Alloys	01JAN1755	12	24.305	650
15	Nitrogen	Fertilizers	01JAN1772	7	14.007	-210
16	Oxygen	MedicalUse	01JAN1774	8	15.999	-218
17	Phosphorus	Agriculture	01JAN1669	15	30.974	44
18	Silicon	Electronics	01JAN1824	14	28.085	1414
19	Silicon	Electronics	.	14	.	1414
20	Silver	Jewelry	01JAN2000	47	107.868	962
21	Sodium	SaltProduction	01JAN1807	11	22.990	98
22	Sulfur	Chemicals	01JAN1777	16	32.060	115
23	Zinc	Galvanization	01JAN1746	30	65.380	420

proc sort data=classified_elements; by Element_Name; run;

proc print data=classified_elements;

run;

OUTPUT:

Obs	Element_Name	Usage	Discovery_Date	Atomic_Number	Weight	Melting_Point	Reference_Date	Years_Since_Discovery	Next_Review_Date	Category
1	Aluminum	Construction	01JAN1825	13	26.980	660	01JAN2025	200	01JAN2125	Medium Melting
2	Carbon	OrganicChem	01JAN2000	6	12.011	3550	01JAN2025	25	01JAN2300	High Melting
3	Chlorine	WaterTreatment	01JAN1774	17	35.450	-101	01JAN2025	251	01JAN2074	Gas or Cryogenic
4	Copper	Wiring	01JAN2000	29	63.546	1085	01JAN2025	25	01JAN2300	High Melting
5	Gold	Investment	01JAN2000	79	196.970	1064	01JAN2025	25	01JAN2300	High Melting
6	Helium	Cryogenics	01JAN1895	2	4.003	-272	01JAN2025	130	01JAN2195	Gas or Cryogenic
7	Hydrogen	Fuel	01JAN1766	1	1.008	-259	01JAN2025	259	01JAN2066	Gas or Cryogenic
8	Iron	Steel	01JAN2000	26	55.845	1538	01JAN2025	25	01JAN2300	High Melting
9	Lithium	Batteries	01JAN1817	3	6.940	180	01JAN2025	208	01JAN2117	Low Melting
10	Magnesium	Alloys	01JAN1755	12	24.305	650	01JAN2025	270	01JAN2055	Medium Melting
11	Nitrogen	Fertilizers	01JAN1772	7	14.007	-210	01JAN2025	253	01JAN2072	Gas or Cryogenic
12	Oxygen	MedicalUse	01JAN1774	8	15.999	-218	01JAN2025	251	01JAN2074	Gas or Cryogenic
13	Phosphorus	Agriculture	01JAN1669	15	30.974	44	01JAN2025	356	01JAN1969	Low Melting
14	Silicon	Electronics	01JAN1824	14	28.085	1414	01JAN2025	201	01JAN2124	High Melting
15	Silver	Jewelry	01JAN2000	47	107.868	962	01JAN2025	25	01JAN2300	Medium Melting
16	Sodium	SaltProduction	01JAN1807	11	22.990	98	01JAN2025	218	01JAN2107	Low Melting
17	Sulfur	Chemicals	01JAN1777	16	32.060	115	01JAN2025	248	01JAN2077	Low Melting
18	Zinc	Galvanization	01JAN1746	30	65.380	420	01JAN2025	279	01JAN2046	Low Melting

data merged_elements;

    merge elements_raw classified_elements;

    by Element_Name;

run;

proc print data=merged_elements;

run;

OUTPUT:

Obs	Element_Name	Usage	Discovery_Date	Atomic_Number	Weight	Melting_Point	Reference_Date	Years_Since_Discovery	Next_Review_Date	Category
1	Aluminum	Construction	01JAN1825	13	26.980	660	01JAN2025	200	01JAN2125	Medium Melting
2	Carbon	OrganicChem	01JAN2000	6	12.011	3550	01JAN2025	25	01JAN2300	High Melting
3	Carbon	OrganicChem	.	6	.	3550	01JAN2025	25	01JAN2300	High Melting
4	Chlorine	WaterTreatment	01JAN1774	17	35.450	-101	01JAN2025	251	01JAN2074	Gas or Cryogenic
5	Copper	Wiring	01JAN2000	29	63.546	1085	01JAN2025	25	01JAN2300	High Melting
6	Copper	Wiring	.	29	.	1085	01JAN2025	25	01JAN2300	High Melting
7	Gold	Investment	01JAN2000	79	196.970	1064	01JAN2025	25	01JAN2300	High Melting
8	Gold	Investment	.	79	.	1064	01JAN2025	25	01JAN2300	High Melting
9	Helium	Cryogenics	01JAN1895	2	4.003	-272	01JAN2025	130	01JAN2195	Gas or Cryogenic
10	Hydrogen	Fuel	01JAN1766	1	1.008	-259	01JAN2025	259	01JAN2066	Gas or Cryogenic
11	Iron	Steel	01JAN2000	26	55.845	1538	01JAN2025	25	01JAN2300	High Melting
12	Iron	Steel	.	26	.	1538	01JAN2025	25	01JAN2300	High Melting
13	Lithium	Batteries	01JAN1817	3	6.940	180	01JAN2025	208	01JAN2117	Low Melting
14	Magnesium	Alloys	01JAN1755	12	24.305	650	01JAN2025	270	01JAN2055	Medium Melting
15	Nitrogen	Fertilizers	01JAN1772	7	14.007	-210	01JAN2025	253	01JAN2072	Gas or Cryogenic
16	Oxygen	MedicalUse	01JAN1774	8	15.999	-218	01JAN2025	251	01JAN2074	Gas or Cryogenic
17	Phosphorus	Agriculture	01JAN1669	15	30.974	44	01JAN2025	356	01JAN1969	Low Melting
18	Silicon	Electronics	01JAN1824	14	28.085	1414	01JAN2025	201	01JAN2124	High Melting
19	Silicon	Electronics	.	14	.	1414	01JAN2025	201	01JAN2124	High Melting
20	Silver	Jewelry	01JAN2000	47	107.868	962	01JAN2025	25	01JAN2300	Medium Melting
21	Sodium	SaltProduction	01JAN1807	11	22.990	98	01JAN2025	218	01JAN2107	Low Melting
22	Sulfur	Chemicals	01JAN1777	16	32.060	115	01JAN2025	248	01JAN2077	Low Melting
23	Zinc	Galvanization	01JAN1746	30	65.380	420	01JAN2025	279	01JAN2046	Low Melting

9. PROC TRANSPOSE – Reshaping Data

proc transpose data=elements_dates

               out=elements_transposed;

    by Element_Name NotSorted;

    var Weight Melting_Point;

run;

proc print data=elements_transposed;

run;

OUTPUT:

Obs	Element_Name	_NAME_	COL1
1	Hydrogen	Weight	1.01
2	Hydrogen	Melting_Point	-259.00
3	Helium	Weight	4.00
4	Helium	Melting_Point	-272.00
5	Lithium	Weight	6.94
6	Lithium	Melting_Point	180.00
7	Carbon	Weight	12.01
8	Carbon	Melting_Point	3550.00
9	Nitrogen	Weight	14.01
10	Nitrogen	Melting_Point	-210.00
11	Oxygen	Weight	16.00
12	Oxygen	Melting_Point	-218.00
13	Sodium	Weight	22.99
14	Sodium	Melting_Point	98.00
15	Magnesium	Weight	24.31
16	Magnesium	Melting_Point	650.00
17	Aluminum	Weight	26.98
18	Aluminum	Melting_Point	660.00
19	Silicon	Weight	28.09
20	Silicon	Melting_Point	1414.00
21	Phosphorus	Weight	30.97
22	Phosphorus	Melting_Point	44.00
23	Sulfur	Weight	32.06
24	Sulfur	Melting_Point	115.00
25	Chlorine	Weight	35.45
26	Chlorine	Melting_Point	-101.00
27	Iron	Weight	55.85
28	Iron	Melting_Point	1538.00
29	Copper	Weight	63.55
30	Copper	Melting_Point	1085.00
31	Zinc	Weight	65.38
32	Zinc	Melting_Point	420.00
33	Silver	Weight	107.87
34	Silver	Melting_Point	962.00
35	Gold	Weight	196.97
36	Gold	Melting_Point	1064.00

Why This Is Important

·       Reporting formats

·       TLF creation

·       SDTM / ADaM reshaping logic

10. Additional SAS Statements Used

Statement	Purpose
DATA	Dataset creation
SET	Row-wise concatenation
MERGE	Column-wise joining
APPEND	Efficient dataset expansion
FORMAT	Presentation control
INPUT	Structured data reading
WHERE	Conditional filtering
11.Business Interpretation  High Melting Elements ·       Iron, Silicon, Gold ·       Used in construction, electronics, and finance Low Melting / Cryogenic Elements ·       Helium, Hydrogen ·       Used in aerospace and medicine

12. What You Learn From This Project

Technical Skills

·       Base SAS mastery

·       PROC SQL proficiency

·       Macro automation

·       Date intelligence

Analytical Thinking

·       Classification logic

·       Statistical interpretation

·       Real-world data modeling

Interview Readiness

·       End-to-end SAS workflow

·       Dataset transformation

·       Production-level coding standards

13. How This Helps Your Career

This project can be confidently used as:

·       Interview explanation project

·       Portfolio project

·       Blog or tutorial content

·       Training demonstration

Especially valuable for roles like:

·       SAS Programmer

·       Clinical Data Analyst

·       Data Scientist (SAS track)

·       Statistical Programmer

14. Conclusion

This Chemical Elements SAS Project demonstrates how raw scientific data can be transformed into actionable insights using professional SAS programming practices.

It mirrors real enterprise workflows, covers most frequently tested SAS topics, and proves your capability to handle complex datasets with confidence.

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 chemical elements data.

Our Mission:

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

This project is suitable for:

SAS Programmer Interviews

SAS Programmer Job Seekers

SAS Analysts

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