368.GLOBAL NATURAL DISASTER IMPACT DATA ANALYSIS USING SAS | PROC SQL | PROC MEANS | PROC UNIVARIATE | PROC SGPLOT | PROC TRANSPOSE | PROC APPEND | MACROS | DATE FUNCTIONS (INTCK-INTNX-MDY) | MERGE | SET

GLOBAL NATURAL DISASTER IMPACT DATA ANALYSIS USING SAS | PROC SQL | PROC MEANS | PROC UNIVARIATE | PROC SGPLOT | PROC TRANSPOSE | PROC APPEND | MACROS | DATE FUNCTIONS (INTCK-INTNX-MDY) | MERGE | SET

 options nocenter;

1. DATASET CREATION

data disasters_raw;

    format Disaster_Date Recovery_Date date9.;

    input Disaster_ID Disaster_Type:$12. Country $ Casualties Cost_Million Severity

          Disaster_Date : date9. Recovery_Date : date9.;

    datalines;

1 Earthquake India 8500 4200 9 01JAN2023 01JAN2024

2 Flood India 1200 1100 6 10FEB2023 10AUG2023

3 Cyclone USA 950 2200 7 05MAR2023 05DEC2023

4 Wildfire Australia 600 1800 8 12APR2023 12JAN2024

5 Drought Kenya 2000 950 5 01MAY2023 01MAY2024

6 Tsunami Japan 12000 9000 10 15JUN2023 15JUN2024

7 Landslide Nepal 450 600 6 20JUL2023 20NOV2023

8 Flood China 5000 3500 8 02AUG2023 02FEB2024

9 Hurricane Mexico 1100 2400 7 15SEP2023 15MAR2024

10 Earthquake Turkey 10000 7500 9 10OCT2023 10OCT2024

11 Tornado USA 300 400 4 01NOV2023 01FEB2024

12 Wildfire Canada 800 1500 7 15NOV2023 15MAY2024

13 Flood Bangladesh 4000 2800 8 01DEC2023 01JUN2024

14 Volcano Indonesia 600 1200 6 15DEC2023 15JUL2024

15 Drought Ethiopia 2500 1300 5 01JAN2024 01JAN2025

;

run;

proc print data=disasters_raw;

run;

OUTPUT:

Obs	Disaster_Date	Recovery_Date	Disaster_ID	Disaster_Type	Country	Casualties	Cost_Million	Severity
1	01JAN2023	01JAN2024	1	Earthquake	India	8500	4200	9
2	10FEB2023	10AUG2023	2	Flood	India	1200	1100	6
3	05MAR2023	05DEC2023	3	Cyclone	USA	950	2200	7
4	12APR2023	12JAN2024	4	Wildfire	Australi	600	1800	8
5	01MAY2023	01MAY2024	5	Drought	Kenya	2000	950	5
6	15JUN2023	15JUN2024	6	Tsunami	Japan	12000	9000	10
7	20JUL2023	20NOV2023	7	Landslide	Nepal	450	600	6
8	02AUG2023	02FEB2024	8	Flood	China	5000	3500	8
9	15SEP2023	15MAR2024	9	Hurricane	Mexico	1100	2400	7
10	10OCT2023	10OCT2024	10	Earthquake	Turkey	10000	7500	9
11	01NOV2023	01FEB2024	11	Tornado	USA	300	400	4
12	15NOV2023	15MAY2024	12	Wildfire	Canada	800	1500	7
13	01DEC2023	01JUN2024	13	Flood	Banglade	4000	2800	8
14	15DEC2023	15JUL2024	14	Volcano	Indonesi	600	1200	6
15	01JAN2024	01JAN2025	15	Drought	Ethiopia	2500	1300	5

Why These Variables Matter:

Variable	Why it is needed
Disaster_Type	Groups risk types (earthquake vs flood vs wildfire)
Country	Regional impact analysis
Casualties	Human loss measurement
Cost_Million	Economic impact
Severity	Disaster strength
Recovery_Time	Rehabilitation burden
Risk_Level	Derived macro-driven classification

 

2. DATE HANDLING — INTCK, INTNX, MDY

data disasters_dates;

    set disasters_raw;

    Recovery_Time = intck('month', Disaster_Date, Recovery_Date);

    Next_Review = intnx('month', Disaster_Date, 3);

    format Next_Review date9.;

run;

proc print data=disasters_dates;

run;

OUTPUT:

Obs	Disaster_Date	Recovery_Date	Disaster_ID	Disaster_Type	Country	Casualties	Cost_Million	Severity	Recovery_Time	Next_Review
1	01JAN2023	01JAN2024	1	Earthquake	India	8500	4200	9	12	01APR2023
2	10FEB2023	10AUG2023	2	Flood	India	1200	1100	6	6	01MAY2023
3	05MAR2023	05DEC2023	3	Cyclone	USA	950	2200	7	9	01JUN2023
4	12APR2023	12JAN2024	4	Wildfire	Australi	600	1800	8	9	01JUL2023
5	01MAY2023	01MAY2024	5	Drought	Kenya	2000	950	5	12	01AUG2023
6	15JUN2023	15JUN2024	6	Tsunami	Japan	12000	9000	10	12	01SEP2023
7	20JUL2023	20NOV2023	7	Landslide	Nepal	450	600	6	4	01OCT2023
8	02AUG2023	02FEB2024	8	Flood	China	5000	3500	8	6	01NOV2023
9	15SEP2023	15MAR2024	9	Hurricane	Mexico	1100	2400	7	6	01DEC2023
10	10OCT2023	10OCT2024	10	Earthquake	Turkey	10000	7500	9	12	01JAN2024
11	01NOV2023	01FEB2024	11	Tornado	USA	300	400	4	3	01FEB2024
12	15NOV2023	15MAY2024	12	Wildfire	Canada	800	1500	7	6	01FEB2024
13	01DEC2023	01JUN2024	13	Flood	Banglade	4000	2800	8	6	01MAR2024
14	15DEC2023	15JUL2024	14	Volcano	Indonesi	600	1200	6	7	01MAR2024
15	01JAN2024	01JAN2025	15	Drought	Ethiopia	2500	1300	5	12	01APR2024

Why INTCK,INTNX,MDY Used:

In disaster analytics, time is as critical as cost.

·       INTCK → how long did rebuilding take

·       INTNX → future review scheduling

·       MDY → standardized disaster dating

This mimics how clinical trials use RFSTDTC, RFENDTC, AVAL dates.

3. MACRO – SEVERITY CLASSIFICATION

%macro classify_severity;

data disasters_classified;

    length Risk_Level $9.;

    set disasters_dates;

    if Severity >= 9 then Risk_Level = "EXTREME";

    else if Severity >= 7 then Risk_Level = "HIGH";

    else if Severity >= 5 then Risk_Level = "MODERATE";

    else Risk_Level = "LOW";

run;

proc print data=disasters_classified;

run;

%mend;

%classify_severity;

OUTPUT:

Obs	Risk_Level	Disaster_Date	Recovery_Date	Disaster_ID	Disaster_Type	Country	Casualties	Cost_Million	Severity	Recovery_Time	Next_Review
1	EXTREME	01JAN2023	01JAN2024	1	Earthquake	India	8500	4200	9	12	01APR2023
2	MODERATE	10FEB2023	10AUG2023	2	Flood	India	1200	1100	6	6	01MAY2023
3	HIGH	05MAR2023	05DEC2023	3	Cyclone	USA	950	2200	7	9	01JUN2023
4	HIGH	12APR2023	12JAN2024	4	Wildfire	Australi	600	1800	8	9	01JUL2023
5	MODERATE	01MAY2023	01MAY2024	5	Drought	Kenya	2000	950	5	12	01AUG2023
6	EXTREME	15JUN2023	15JUN2024	6	Tsunami	Japan	12000	9000	10	12	01SEP2023
7	MODERATE	20JUL2023	20NOV2023	7	Landslide	Nepal	450	600	6	4	01OCT2023
8	HIGH	02AUG2023	02FEB2024	8	Flood	China	5000	3500	8	6	01NOV2023
9	HIGH	15SEP2023	15MAR2024	9	Hurricane	Mexico	1100	2400	7	6	01DEC2023
10	EXTREME	10OCT2023	10OCT2024	10	Earthquake	Turkey	10000	7500	9	12	01JAN2024
11	LOW	01NOV2023	01FEB2024	11	Tornado	USA	300	400	4	3	01FEB2024
12	HIGH	15NOV2023	15MAY2024	12	Wildfire	Canada	800	1500	7	6	01FEB2024
13	HIGH	01DEC2023	01JUN2024	13	Flood	Banglade	4000	2800	8	6	01MAR2024
14	MODERATE	15DEC2023	15JUL2024	14	Volcano	Indonesi	600	1200	6	7	01MAR2024
15	MODERATE	01JAN2024	01JAN2025	15	Drought	Ethiopia	2500	1300	5	12	01APR2024

4. PROC SQL – ANALYTICAL SUMMARIES

proc sql;

    create table country_cost as

    select Country,

           count(*) as Disaster_Count,

           sum(Cost_Million) as Total_Cost,

           sum(Casualties) as Total_Casualties

    from disasters_classified

    group by Country;

quit;

proc print data=country_cost;

run;

OUTPUT:

Obs	Country	Disaster_Count	Total_Cost	Total_Casualties
1	Australi	1	1800	600
2	Banglade	1	2800	4000
3	Canada	1	1500	800
4	China	1	3500	5000
5	Ethiopia	1	1300	2500
6	India	2	5300	9700
7	Indonesi	1	1200	600
8	Japan	1	9000	12000
9	Kenya	1	950	2000
10	Mexico	1	2400	1100
11	Nepal	1	600	450
12	Turkey	1	7500	10000
13	USA	2	2600	1250

Why Proc SQL Is Used:

SQL allows relational analytics:

• Country level cost
• Risk-wise burden
• Disaster frequency

This is exactly how safety summaries are done in ADaM.

5. PROC MEANS – STATISTICAL SUMMARY

proc means data=disasters_classified mean max min sum;

    var Casualties Cost_Million Recovery_Time;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Maximum	Minimum	Sum
Casualties Cost_Million Recovery_Time	3333.33 2696.67 8.1333333	12000.00 9000.00 12.0000000	300.0000000 400.0000000 3.0000000	50000.00 40450.00 122.0000000

6. PROC UNIVARIATE – DISTRIBUTION ANALYSIS

proc univariate data=disasters_classified;

    var Casualties Cost_Million;

    histogram Casualties Cost_Million;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Casualties

Moments
N	15	Sum Weights	15
Mean	3333.33333	Sum Observations	50000
Std Deviation	3836.27734	Variance	14717023.8
Skewness	1.37773304	Kurtosis	0.65781048
Uncorrected SS	372705000	Corrected SS	206038333
Coeff Variation	115.08832	Std Error Mean	990.522549

Basic Statistical Measures
Location		Variability
Mean	3333.333	Std Deviation	3836
Median	1200.000	Variance	14717024
Mode	600.000	Range	11700
		Interquartile Range	4400

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	3.365227	Pr > |t|	0.0046
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	12000
99%	12000
95%	12000
90%	10000
75% Q3	5000
50% Median	1200
25% Q1	600
10%	450
5%	300
1%	300
0% Min	300

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
300	11	4000	13
450	7	5000	8
600	14	8500	1
600	4	10000	10
800	12	12000	6

The UNIVARIATE Procedure

The UNIVARIATE Procedure

Variable: Cost_Million

Moments
N	15	Sum Weights	15
Mean	2696.66667	Sum Observations	40450
Std Deviation	2503.60217	Variance	6268023.81
Skewness	1.71106246	Kurtosis	2.35911263
Uncorrected SS	196832500	Corrected SS	87752333.3
Coeff Variation	92.8406242	Std Error Mean	646.4273

Basic Statistical Measures
Location		Variability
Mean	2696.667	Std Deviation	2504
Median	1800.000	Variance	6268024
Mode	.	Range	8600
		Interquartile Range	2400

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	4.171647	Pr > |t|	0.0009
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	9000
99%	9000
95%	9000
90%	7500
75% Q3	3500
50% Median	1800
25% Q1	1100
10%	600
5%	400
1%	400
0% Min	400

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
400	11	2800	13
600	7	3500	8
950	5	4200	1
1100	2	7500	10
1200	14	9000	6

The UNIVARIATE Procedure

Why Proc Means And Univariate:

These identify:
·       Outlier disasters
·       Worst cost events
·       Casualty distributions
In pharma, this equals lab and AE distribution checks

7. PROC SGPLOT – VISUAL ANALYTICS

proc sgplot data=disasters_classified;

    vbar Disaster_Type / response=Casualties stat=sum;

run;

OUTPUT:

proc sgplot data=disasters_classified;

    vbar Country / response=Cost_Million stat=sum;

run;

OUTPUT:

8. PROC TRANSPOSE

proc transpose data=disasters_classified out=disaster_transposed;

    by Country NotSorted;

    id Disaster_Type;

    var Cost_Million;

run;

proc print data=disaster_transposed;

run;

OUTPUT:

Obs	Country	_NAME_	Earthquake	Flood	Cyclone	Wildfire	Drought	Tsunami	Landslide	Hurricane	Tornado	Volcano
1	India	Cost_Million	4200	1100	.	.	.	.	.	.	.	.
2	USA	Cost_Million	.	.	2200	.	.	.	.	.	.	.
3	Australi	Cost_Million	.	.	.	1800	.	.	.	.	.	.
4	Kenya	Cost_Million	.	.	.	.	950	.	.	.	.	.
5	Japan	Cost_Million	.	.	.	.	.	9000	.	.	.	.
6	Nepal	Cost_Million	.	.	.	.	.	.	600	.	.	.
7	China	Cost_Million	.	3500	.	.	.	.	.	.	.	.
8	Mexico	Cost_Million	.	.	.	.	.	.	.	2400	.	.
9	Turkey	Cost_Million	7500	.	.	.	.	.	.	.	.	.
10	USA	Cost_Million	.	.	.	.	.	.	.	.	400	.
11	Canada	Cost_Million	.	.	.	1500	.	.	.	.	.	.
12	Banglade	Cost_Million	.	2800	.	.	.	.	.	.	.	.
13	Indonesi	Cost_Million	.	.	.	.	.	.	.	.	.	1200
14	Ethiopia	Cost_Million	.	.	.	.	1300	.	.	.	.	.

9. DATA MERGE

proc sort data=disasters_classified;

 by Country;

run;

proc print data=disasters_classified;

run;

OUTPUT:

Obs	Risk_Level	Disaster_Date	Recovery_Date	Disaster_ID	Disaster_Type	Country	Casualties	Cost_Million	Severity	Recovery_Time	Next_Review
1	HIGH	12APR2023	12JAN2024	4	Wildfire	Australi	600	1800	8	9	01JUL2023
2	HIGH	01DEC2023	01JUN2024	13	Flood	Banglade	4000	2800	8	6	01MAR2024
3	HIGH	15NOV2023	15MAY2024	12	Wildfire	Canada	800	1500	7	6	01FEB2024
4	HIGH	02AUG2023	02FEB2024	8	Flood	China	5000	3500	8	6	01NOV2023
5	MODERATE	01JAN2024	01JAN2025	15	Drought	Ethiopia	2500	1300	5	12	01APR2024
6	EXTREME	01JAN2023	01JAN2024	1	Earthquake	India	8500	4200	9	12	01APR2023
7	MODERATE	10FEB2023	10AUG2023	2	Flood	India	1200	1100	6	6	01MAY2023
8	MODERATE	15DEC2023	15JUL2024	14	Volcano	Indonesi	600	1200	6	7	01MAR2024
9	EXTREME	15JUN2023	15JUN2024	6	Tsunami	Japan	12000	9000	10	12	01SEP2023
10	MODERATE	01MAY2023	01MAY2024	5	Drought	Kenya	2000	950	5	12	01AUG2023
11	HIGH	15SEP2023	15MAR2024	9	Hurricane	Mexico	1100	2400	7	6	01DEC2023
12	MODERATE	20JUL2023	20NOV2023	7	Landslide	Nepal	450	600	6	4	01OCT2023
13	EXTREME	10OCT2023	10OCT2024	10	Earthquake	Turkey	10000	7500	9	12	01JAN2024
14	HIGH	05MAR2023	05DEC2023	3	Cyclone	USA	950	2200	7	9	01JUN2023
15	LOW	01NOV2023	01FEB2024	11	Tornado	USA	300	400	4	3	01FEB2024

proc sort data=country_cost;

 by Country;

run;

proc print data=country_cost;

run;

OUTPUT:

Obs	Country	Disaster_Count	Total_Cost	Total_Casualties
1	Australi	1	1800	600
2	Banglade	1	2800	4000
3	Canada	1	1500	800
4	China	1	3500	5000
5	Ethiopia	1	1300	2500
6	India	2	5300	9700
7	Indonesi	1	1200	600
8	Japan	1	9000	12000
9	Kenya	1	950	2000
10	Mexico	1	2400	1100
11	Nepal	1	600	450
12	Turkey	1	7500	10000
13	USA	2	2600	1250

data merged_data;

    merge disasters_classified 

          country_cost;

    by Country;

run;

proc print data=merged_data;

run;

OUTPUT:

Obs	Risk_Level	Disaster_Date	Recovery_Date	Disaster_ID	Disaster_Type	Country	Casualties	Cost_Million	Severity	Recovery_Time	Next_Review	Disaster_Count	Total_Cost	Total_Casualties
1	HIGH	12APR2023	12JAN2024	4	Wildfire	Australi	600	1800	8	9	01JUL2023	1	1800	600
2	HIGH	01DEC2023	01JUN2024	13	Flood	Banglade	4000	2800	8	6	01MAR2024	1	2800	4000
3	HIGH	15NOV2023	15MAY2024	12	Wildfire	Canada	800	1500	7	6	01FEB2024	1	1500	800
4	HIGH	02AUG2023	02FEB2024	8	Flood	China	5000	3500	8	6	01NOV2023	1	3500	5000
5	MODERATE	01JAN2024	01JAN2025	15	Drought	Ethiopia	2500	1300	5	12	01APR2024	1	1300	2500
6	EXTREME	01JAN2023	01JAN2024	1	Earthquake	India	8500	4200	9	12	01APR2023	2	5300	9700
7	MODERATE	10FEB2023	10AUG2023	2	Flood	India	1200	1100	6	6	01MAY2023	2	5300	9700
8	MODERATE	15DEC2023	15JUL2024	14	Volcano	Indonesi	600	1200	6	7	01MAR2024	1	1200	600
9	EXTREME	15JUN2023	15JUN2024	6	Tsunami	Japan	12000	9000	10	12	01SEP2023	1	9000	12000
10	MODERATE	01MAY2023	01MAY2024	5	Drought	Kenya	2000	950	5	12	01AUG2023	1	950	2000
11	HIGH	15SEP2023	15MAR2024	9	Hurricane	Mexico	1100	2400	7	6	01DEC2023	1	2400	1100
12	MODERATE	20JUL2023	20NOV2023	7	Landslide	Nepal	450	600	6	4	01OCT2023	1	600	450
13	EXTREME	10OCT2023	10OCT2024	10	Earthquake	Turkey	10000	7500	9	12	01JAN2024	1	7500	10000
14	HIGH	05MAR2023	05DEC2023	3	Cyclone	USA	950	2200	7	9	01JUN2023	2	2600	1250
15	LOW	01NOV2023	01FEB2024	11	Tornado	USA	300	400	4	3	01FEB2024	2	2600	1250

10. PROC APPEND

data new_disasters;

    input Disaster_ID Disaster_Type:$12. Country $ Casualties Cost_Million Severity 

          Disaster_Date : date9. Recovery_Date : date9.;

    datalines;

16 Flood SriLanka 900 750 6 10JAN2024 10JUL2024

17 Earthquake Peru 4500 3200 8 01FEB2024 01FEB2025

;

run;

proc print data=new_disasters;

run;

OUTPUT:

Obs	Disaster_ID	Disaster_Type	Country	Casualties	Cost_Million	Severity	Disaster_Date	Recovery_Date
1	16	Flood	SriLanka	900	750	6	23385	23567
2	17	Earthquake	Peru	4500	3200	8	23407	23773

proc append base=disasters_raw 

            data=new_disasters force;

run;

proc print data=disasters_raw;

run;

OUTPUT:

Obs	Disaster_Date	Recovery_Date	Disaster_ID	Disaster_Type	Country	Casualties	Cost_Million	Severity
1	01JAN2023	01JAN2024	1	Earthquake	India	8500	4200	9
2	10FEB2023	10AUG2023	2	Flood	India	1200	1100	6
3	05MAR2023	05DEC2023	3	Cyclone	USA	950	2200	7
4	12APR2023	12JAN2024	4	Wildfire	Australi	600	1800	8
5	01MAY2023	01MAY2024	5	Drought	Kenya	2000	950	5
6	15JUN2023	15JUN2024	6	Tsunami	Japan	12000	9000	10
7	20JUL2023	20NOV2023	7	Landslide	Nepal	450	600	6
8	02AUG2023	02FEB2024	8	Flood	China	5000	3500	8
9	15SEP2023	15MAR2024	9	Hurricane	Mexico	1100	2400	7
10	10OCT2023	10OCT2024	10	Earthquake	Turkey	10000	7500	9
11	01NOV2023	01FEB2024	11	Tornado	USA	300	400	4
12	15NOV2023	15MAY2024	12	Wildfire	Canada	800	1500	7
13	01DEC2023	01JUN2024	13	Flood	Banglade	4000	2800	8
14	15DEC2023	15JUL2024	14	Volcano	Indonesi	600	1200	6
15	01JAN2024	01JAN2025	15	Drought	Ethiopia	2500	1300	5
16	10JAN2024	10JUL2024	16	Flood	SriLanka	900	750	6
17	01FEB2024	01FEB2025	17	Earthquake	Peru	4500	3200	8

11. SET Statement

data full_disasters;

    set disasters_classified 

        new_disasters;

run;

proc print data=full_disasters;

run;

OUTPUT:

Obs	Risk_Level	Disaster_Date	Recovery_Date	Disaster_ID	Disaster_Type	Country	Casualties	Cost_Million	Severity	Recovery_Time	Next_Review
1	HIGH	12APR2023	12JAN2024	4	Wildfire	Australi	600	1800	8	9	01JUL2023
2	HIGH	01DEC2023	01JUN2024	13	Flood	Banglade	4000	2800	8	6	01MAR2024
3	HIGH	15NOV2023	15MAY2024	12	Wildfire	Canada	800	1500	7	6	01FEB2024
4	HIGH	02AUG2023	02FEB2024	8	Flood	China	5000	3500	8	6	01NOV2023
5	MODERATE	01JAN2024	01JAN2025	15	Drought	Ethiopia	2500	1300	5	12	01APR2024
6	EXTREME	01JAN2023	01JAN2024	1	Earthquake	India	8500	4200	9	12	01APR2023
7	MODERATE	10FEB2023	10AUG2023	2	Flood	India	1200	1100	6	6	01MAY2023
8	MODERATE	15DEC2023	15JUL2024	14	Volcano	Indonesi	600	1200	6	7	01MAR2024
9	EXTREME	15JUN2023	15JUN2024	6	Tsunami	Japan	12000	9000	10	12	01SEP2023
10	MODERATE	01MAY2023	01MAY2024	5	Drought	Kenya	2000	950	5	12	01AUG2023
11	HIGH	15SEP2023	15MAR2024	9	Hurricane	Mexico	1100	2400	7	6	01DEC2023
12	MODERATE	20JUL2023	20NOV2023	7	Landslide	Nepal	450	600	6	4	01OCT2023
13	EXTREME	10OCT2023	10OCT2024	10	Earthquake	Turkey	10000	7500	9	12	01JAN2024
14	HIGH	05MAR2023	05DEC2023	3	Cyclone	USA	950	2200	7	9	01JUN2023
15	LOW	01NOV2023	01FEB2024	11	Tornado	USA	300	400	4	3	01FEB2024
16		10JAN2024	10JUL2024	16	Flood	SriLanka	900	750	6	.	.
17		01FEB2024	01FEB2025	17	Earthquake	Peru	4500	3200	8	.	.

Why TRANSPOSE, MERGE, APPEND, SET:

These replicate:

·       SDTM → ADaM reshaping

·       Incremental updates

·       Database refresh cycles

Just like you do in ADSL, ADAE, ADLB.

12. ADVANCED SQL — RISK-WISE COST

proc sql;

    select Risk_Level,

           sum(Cost_Million) as Total_Cost,

           avg(Casualties) as Avg_Casualties

    from disasters_classified

    group by Risk_Level;

quit;

OUTPUT:

Risk_Level	Total_Cost	Avg_Casualties
EXTREME	20700	10166.67
HIGH	14200	2075
LOW	400	300
MODERATE	5150	1350

13. TIME-BASED ANALYSIS

proc sql;

    select Disaster_Type,

           avg(Recovery_Time) as Avg_Recovery_Months

    from disasters_classified

    group by Disaster_Type;

quit;

OUTPUT:

Disaster_Type	Avg_Recovery_Months
Cyclone	9
Drought	12
Earthquake	12
Flood	6
Hurricane	6
Landslide	4
Tornado	3
Tsunami	12
Volcano	7
Wildfire	7.5

To Visit My Previous Electronic Data Analysis:Click Here

To Visit My Previous IPS Toppers Dataset:Click Here

To Visit My Previous Global Money Loan Trends Dataset:Click Here

To Visit My Previous Statewise Population Analysis:Click Here

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