355.FAMOUS NATURAL LANDSCAPES DATA ANALYSIS USING SAS DATA STEP | PROC SQL | PROC FREQ | PROC MEANS | PROC UNIVARIATE | MACROS | DATE FUNCTIONS (MDY - INTCK - INTNX) | MERGE | APPEND | TRANSPOSE

FAMOUS NATURAL LANDSCAPES DATA ANALYSIS USING SAS DATA STEP | PROC SQL | PROC FREQ | PROC MEANS | PROC UNIVARIATE | MACROS | DATE FUNCTIONS (MDY | INTCK | INTNX) | MERGE | APPEND | TRANSPOSE 

options nocenter;

1.LANDSCAPES BASE DATASET CREATION

data landscapes_base;

    length Landscape_Name $30 Location $25 Risk_Level $10;

    format Established_Date date9.;

    input Landscape_Name $ Location $ Height Area Tourists Risk_Level $ Established_Date :date9.;

    datalines;

MountEverest Nepal 8848 141 800 High 29MAY1953

GrandCanyon USA 2400 4926 6000 Medium 26FEB1919

NiagaraFalls USA_Canada 51 17 9000 Medium 01JAN1885

VictoriaFalls Zambia_Zimbabwe 108 1708 1500 High 18NOV1855

GreatBarrierReef Australia 0 344400 2000 Medium 11OCT1981

SaharaDesert Africa 450 9100000 1000 High 01JAN1920

AmazonRainforest Brazil 300 5500000 3500 High 05SEP1930

MountFuji Japan 3776 1227 3000 Medium 22JUN1934

YosemiteValley USA 1219 3029 4000 Low 01OCT1890

IguazuFalls Argentina_Brazil 82 2610 1400 Medium 02DEC1934

TableMountain SouthAfrica 1086 221 4500 Low 01JAN1923

DeadSea Israel_Jordan -430 605 1200 Medium 01JAN1948

;

run;

proc print data=landscapes_base;

run;

OUTPUT:

Obs	Landscape_Name	Location	Risk_Level	Established_Date	Height	Area	Tourists
1	MountEverest	Nepal	High	29MAY1953	8848	141	800
2	GrandCanyon	USA	Medium	26FEB1919	2400	4926	6000
3	NiagaraFalls	USA_Canada	Medium	01JAN1885	51	17	9000
4	VictoriaFalls	Zambia_Zimbabwe	High	18NOV1855	108	1708	1500
5	GreatBarrierReef	Australia	Medium	11OCT1981	0	344400	2000
6	SaharaDesert	Africa	High	01JAN1920	450	9100000	1000
7	AmazonRainforest	Brazil	High	05SEP1930	300	5500000	3500
8	MountFuji	Japan	Medium	22JUN1934	3776	1227	3000
9	YosemiteValley	USA	Low	01OCT1890	1219	3029	4000
10	IguazuFalls	Argentina_Brazil	Medium	02DEC1934	82	2610	1400
11	TableMountain	SouthAfrica	Low	01JAN1923	1086	221	4500
12	DeadSea	Israel_Jordan	Medium	01JAN1948	-430	605	1200

2.USING DATE FUNCTIONS (MDY, INTCK, INTNX)

2.1 Creating Derived Date Variables

data landscapes_dates;

    set landscapes_base;

    Review_Date = mdy(1,1,2025);

    Years_Since_Established = intck('year', Established_Date, Review_Date);

    Next_Review = intnx('year', Established_Date, 100, 'same');

    format Review_Date Next_Review date9.;

run;

proc print data=landscapes_dates;

run;

OUTPUT:

Obs	Landscape_Name	Location	Risk_Level	Established_Date	Height	Area	Tourists	Review_Date	Years_Since_Established	Next_Review
1	MountEverest	Nepal	High	29MAY1953	8848	141	800	01JAN2025	72	29MAY2053
2	GrandCanyon	USA	Medium	26FEB1919	2400	4926	6000	01JAN2025	106	26FEB2019
3	NiagaraFalls	USA_Canada	Medium	01JAN1885	51	17	9000	01JAN2025	140	01JAN1985
4	VictoriaFalls	Zambia_Zimbabwe	High	18NOV1855	108	1708	1500	01JAN2025	170	18NOV1955
5	GreatBarrierReef	Australia	Medium	11OCT1981	0	344400	2000	01JAN2025	44	11OCT2081
6	SaharaDesert	Africa	High	01JAN1920	450	9100000	1000	01JAN2025	105	01JAN2020
7	AmazonRainforest	Brazil	High	05SEP1930	300	5500000	3500	01JAN2025	95	05SEP2030
8	MountFuji	Japan	Medium	22JUN1934	3776	1227	3000	01JAN2025	91	22JUN2034
9	YosemiteValley	USA	Low	01OCT1890	1219	3029	4000	01JAN2025	135	01OCT1990
10	IguazuFalls	Argentina_Brazil	Medium	02DEC1934	82	2610	1400	01JAN2025	91	02DEC2034
11	TableMountain	SouthAfrica	Low	01JAN1923	1086	221	4500	01JAN2025	102	01JAN2023
12	DeadSea	Israel_Jordan	Medium	01JAN1948	-430	605	1200	01JAN2025	77	01JAN2048

3.PROC SQL – DATA SELECTION AND DERIVATIONS

proc sql;

    create table landscapes_sql as

    select Landscape_Name,Location,Height,Area,Tourists,Risk_Level,Years_Since_Established,

           case 

              when Tourists > 5000 then 'Very High'

              when Tourists between 2000 and 5000 then 'High'

              else 'Moderate'

           end as Tourist_Pressure

    from landscapes_dates;

quit;

proc print data=landscapes_sql;

run;

OUTPUT:

Obs	Landscape_Name	Location	Height	Area	Tourists	Risk_Level	Years_Since_Established	Tourist_Pressure
1	MountEverest	Nepal	8848	141	800	High	72	Moderate
2	GrandCanyon	USA	2400	4926	6000	Medium	106	Very High
3	NiagaraFalls	USA_Canada	51	17	9000	Medium	140	Very High
4	VictoriaFalls	Zambia_Zimbabwe	108	1708	1500	High	170	Moderate
5	GreatBarrierReef	Australia	0	344400	2000	Medium	44	High
6	SaharaDesert	Africa	450	9100000	1000	High	105	Moderate
7	AmazonRainforest	Brazil	300	5500000	3500	High	95	High
8	MountFuji	Japan	3776	1227	3000	Medium	91	High
9	YosemiteValley	USA	1219	3029	4000	Low	135	High
10	IguazuFalls	Argentina_Brazil	82	2610	1400	Medium	91	Moderate
11	TableMountain	SouthAfrica	1086	221	4500	Low	102	High
12	DeadSea	Israel_Jordan	-430	605	1200	Medium	77	Moderate

4.MACRO FOR SAFETY ZONE CLASSIFICATION

%macro safety_zone;

data landscapes_safety;

    set landscapes_sql;

    length Safety_Zone $12.;

    if Risk_Level='Low' and Tourists < 5000 then Safety_Zone='SAFE';

    else if Risk_Level='Medium' then Safety_Zone='CAUTION';

    else Safety_Zone='DANGER';

run;

proc print data=landscapes_safety;

run;

%mend;

%safety_zone;

OUTPUT:

Obs	Landscape_Name	Location	Height	Area	Tourists	Risk_Level	Years_Since_Established	Tourist_Pressure	SAFETY_ZONE
1	MountEverest	Nepal	8848	141	800	High	72	Moderate	DANGER
2	GrandCanyon	USA	2400	4926	6000	Medium	106	Very High	CAUTION
3	NiagaraFalls	USA_Canada	51	17	9000	Medium	140	Very High	CAUTION
4	VictoriaFalls	Zambia_Zimbabwe	108	1708	1500	High	170	Moderate	DANGER
5	GreatBarrierReef	Australia	0	344400	2000	Medium	44	High	CAUTION
6	SaharaDesert	Africa	450	9100000	1000	High	105	Moderate	DANGER
7	AmazonRainforest	Brazil	300	5500000	3500	High	95	High	DANGER
8	MountFuji	Japan	3776	1227	3000	Medium	91	High	CAUTION
9	YosemiteValley	USA	1219	3029	4000	Low	135	High	SAFE
10	IguazuFalls	Argentina_Brazil	82	2610	1400	Medium	91	Moderate	CAUTION
11	TableMountain	SouthAfrica	1086	221	4500	Low	102	High	SAFE
12	DeadSea	Israel_Jordan	-430	605	1200	Medium	77	Moderate	CAUTION

5.PROC FREQ – RISK & SAFETY DISTRIBUTION

proc freq data=landscapes_safety;

    tables Risk_Level Safety_Zone Tourist_Pressure;

run;

OUTPUT:

The FREQ Procedure

Risk_Level	Frequency	Percent	Cumulative Frequency	Cumulative Percent
High	4	33.33	4	33.33
Low	2	16.67	6	50.00
Medium	6	50.00	12	100.00

SAFETY_ZONE	Frequency	Percent	Cumulative Frequency	Cumulative Percent
CAUTION	6	50.00	6	50.00
DANGER	4	33.33	10	83.33
SAFE	2	16.67	12	100.00

Tourist_Pressure	Frequency	Percent	Cumulative Frequency	Cumulative Percent
High	5	41.67	5	41.67
Moderate	5	41.67	10	83.33
Very High	2	16.67	12	100.00

6.PROC MEANS – NUMERICAL SUMMARY

proc means data=landscapes_safety mean min max;

    var Height Area Tourists Years_Since_Established;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Height Area Tourists Years_Since_Established	1490.83 1246573.67 3158.33 102.3333333	-430.0000000 17.0000000 800.0000000 44.0000000	8848.00 9100000.00 9000.00 170.0000000

7.PROC UNIVARIATE – DISTRIBUTION ANALYSIS

proc univariate data=landscapes_safety;

    var Tourists Area;

    histogram Tourists Area;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Tourists

Moments
N	12	Sum Weights	12
Mean	3158.33333	Sum Observations	37900
Std Deviation	2451.14383	Variance	6008106.06
Skewness	1.35641286	Kurtosis	1.72775908
Uncorrected SS	185790000	Corrected SS	66089166.7
Coeff Variation	77.6087755	Std Error Mean	707.584274

Basic Statistical Measures
Location		Variability
Mean	3158.333	Std Deviation	2451
Median	2500.000	Variance	6008106
Mode	.	Range	8200
		Interquartile Range	2950

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	4.463544	Pr > |t|	0.0010
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	9000
99%	9000
95%	9000
90%	6000
75% Q3	4250
50% Median	2500
25% Q1	1300
10%	1000
5%	800
1%	800
0% Min	800

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
800	1	3500	7
1000	6	4000	9
1200	12	4500	11
1400	10	6000	2
1500	4	9000	3

The UNIVARIATE Procedure

The UNIVARIATE Procedure

Variable: Area

Moments
N	12	Sum Weights	12
Mean	1246573.67	Sum Observations	14958884
Std Deviation	2931510.71	Variance	8.59376E12
Skewness	2.35228821	Kurtosis	4.82890991
Uncorrected SS	1.13179E14	Corrected SS	9.45313E13
Coeff Variation	235.165461	Std Error Mean	846254.249

Basic Statistical Measures
Location		Variability
Mean	1246574	Std Deviation	2931511
Median	2159	Variance	8.59376E12
Mode	.	Range	9099983
		Interquartile Range	174250

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	1.473049	Pr > |t|	0.1688
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	9100000
99%	9100000
95%	9100000
90%	5500000
75% Q3	174663
50% Median	2159
25% Q1	413
10%	141
5%	17
1%	17
0% Min	17

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
17	3	3029	9
141	1	4926	2
221	11	344400	5
605	12	5500000	7
1227	8	9100000	6

The UNIVARIATE Procedure

8.PROC APPEND – ADDITIONAL LANDSCAPES

data landscapes_new;

    length Landscape_Name $30 Location $25 Risk_Level $10;

    input Landscape_Name $ Location $ Height Area Tourists Risk_Level $ Established_Date :date9.;

    datalines;

AngelFalls Venezuela 979 30000 900 Medium 16NOV1933

;

run;

proc print data=landscapes_new;

run;

OUTPUT:

Obs	Landscape_Name	Location	Risk_Level	Height	Area	Tourists	Established_Date
1	AngelFalls	Venezuela	Medium	979	30000	900	-9542

proc append base=landscapes_base 

            data=landscapes_new force;

run;

proc print data=landscapes_base;

run;

OUTPUT:

Obs	Landscape_Name	Location	Risk_Level	Established_Date	Height	Area	Tourists
1	MountEverest	Nepal	High	29MAY1953	8848	141	800
2	GrandCanyon	USA	Medium	26FEB1919	2400	4926	6000
3	NiagaraFalls	USA_Canada	Medium	01JAN1885	51	17	9000
4	VictoriaFalls	Zambia_Zimbabwe	High	18NOV1855	108	1708	1500
5	GreatBarrierReef	Australia	Medium	11OCT1981	0	344400	2000
6	SaharaDesert	Africa	High	01JAN1920	450	9100000	1000
7	AmazonRainforest	Brazil	High	05SEP1930	300	5500000	3500
8	MountFuji	Japan	Medium	22JUN1934	3776	1227	3000
9	YosemiteValley	USA	Low	01OCT1890	1219	3029	4000
10	IguazuFalls	Argentina_Brazil	Medium	02DEC1934	82	2610	1400
11	TableMountain	SouthAfrica	Low	01JAN1923	1086	221	4500
12	DeadSea	Israel_Jordan	Medium	01JAN1948	-430	605	1200
13	AngelFalls	Venezuela	Medium	16NOV1933	979	30000	900

9.MERGE – COMBINING SAFETY & DATE INFO

proc sort data=landscapes_base; by Landscape_Name; run;

proc print data=landscapes_base;

run;

OUTPUT:

Obs	Landscape_Name	Location	Risk_Level	Established_Date	Height	Area	Tourists
1	AmazonRainforest	Brazil	High	05SEP1930	300	5500000	3500
2	AngelFalls	Venezuela	Medium	16NOV1933	979	30000	900
3	DeadSea	Israel_Jordan	Medium	01JAN1948	-430	605	1200
4	GrandCanyon	USA	Medium	26FEB1919	2400	4926	6000
5	GreatBarrierReef	Australia	Medium	11OCT1981	0	344400	2000
6	IguazuFalls	Argentina_Brazil	Medium	02DEC1934	82	2610	1400
7	MountEverest	Nepal	High	29MAY1953	8848	141	800
8	MountFuji	Japan	Medium	22JUN1934	3776	1227	3000
9	NiagaraFalls	USA_Canada	Medium	01JAN1885	51	17	9000
10	SaharaDesert	Africa	High	01JAN1920	450	9100000	1000
11	TableMountain	SouthAfrica	Low	01JAN1923	1086	221	4500
12	VictoriaFalls	Zambia_Zimbabwe	High	18NOV1855	108	1708	1500
13	YosemiteValley	USA	Low	01OCT1890	1219	3029	4000

proc sort data=landscapes_safety; by Landscape_Name; run;

proc print data=landscapes_safety;

run;

OUTPUT:

Obs	Landscape_Name	Location	Height	Area	Tourists	Risk_Level	Years_Since_Established	Tourist_Pressure	SAFETY_ZONE
1	AmazonRainforest	Brazil	300	5500000	3500	High	95	High	DANGER
2	DeadSea	Israel_Jordan	-430	605	1200	Medium	77	Moderate	CAUTION
3	GrandCanyon	USA	2400	4926	6000	Medium	106	Very High	CAUTION
4	GreatBarrierReef	Australia	0	344400	2000	Medium	44	High	CAUTION
5	IguazuFalls	Argentina_Brazil	82	2610	1400	Medium	91	Moderate	CAUTION
6	MountEverest	Nepal	8848	141	800	High	72	Moderate	DANGER
7	MountFuji	Japan	3776	1227	3000	Medium	91	High	CAUTION
8	NiagaraFalls	USA_Canada	51	17	9000	Medium	140	Very High	CAUTION
9	SaharaDesert	Africa	450	9100000	1000	High	105	Moderate	DANGER
10	TableMountain	SouthAfrica	1086	221	4500	Low	102	High	SAFE
11	VictoriaFalls	Zambia_Zimbabwe	108	1708	1500	High	170	Moderate	DANGER
12	YosemiteValley	USA	1219	3029	4000	Low	135	High	SAFE

data landscapes_merged;

    merge landscapes_base(in=a) landscapes_safety(in=b);

    by Landscape_Name;

    if a;

run;

proc print data=landscapes_merged;

run;

OUTPUT:

Obs	Landscape_Name	Location	Risk_Level	Established_Date	Height	Area	Tourists	Years_Since_Established	Tourist_Pressure	SAFETY_ZONE
1	AmazonRainforest	Brazil	High	05SEP1930	300	5500000	3500	95	High	DANGER
2	AngelFalls	Venezuela	Medium	16NOV1933	979	30000	900	.
3	DeadSea	Israel_Jordan	Medium	01JAN1948	-430	605	1200	77	Moderate	CAUTION
4	GrandCanyon	USA	Medium	26FEB1919	2400	4926	6000	106	Very High	CAUTION
5	GreatBarrierReef	Australia	Medium	11OCT1981	0	344400	2000	44	High	CAUTION
6	IguazuFalls	Argentina_Brazil	Medium	02DEC1934	82	2610	1400	91	Moderate	CAUTION
7	MountEverest	Nepal	High	29MAY1953	8848	141	800	72	Moderate	DANGER
8	MountFuji	Japan	Medium	22JUN1934	3776	1227	3000	91	High	CAUTION
9	NiagaraFalls	USA_Canada	Medium	01JAN1885	51	17	9000	140	Very High	CAUTION
10	SaharaDesert	Africa	High	01JAN1920	450	9100000	1000	105	Moderate	DANGER
11	TableMountain	SouthAfrica	Low	01JAN1923	1086	221	4500	102	High	SAFE
12	VictoriaFalls	Zambia_Zimbabwe	High	18NOV1855	108	1708	1500	170	Moderate	DANGER
13	YosemiteValley	USA	Low	01OCT1890	1219	3029	4000	135	High	SAFE

10.PROC TRANSPOSE – SAFETY ZONE VIEW

proc transpose data=landscapes_safety

               out=safety_transposed;

    by Risk_Level NotSorted;

    var Tourists;

run;

proc print data=safety_transposed;

run;

OUTPUT:

Obs	Risk_Level	_NAME_	COL1	COL2	COL3	COL4
1	High	Tourists	3500	.	.	.
2	Medium	Tourists	1200	6000	2000	1400
3	High	Tourists	800	.	.	.
4	Medium	Tourists	3000	9000	.	.
5	High	Tourists	1000	.	.	.
6	Low	Tourists	4500	.	.	.
7	High	Tourists	1500	.	.	.
8	Low	Tourists	4000	.	.	.

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.