352.SAFARI ZONES DATA ANALYSIS USING SAS DATA STEP | PROC SQL | PROC MEANS | PROC UNIVARIATE | PROC SGPLOT | MACROS | DATE FUNCTIONS | APPEND | MERGE | TRANSPOSE

SAFARI ZONES DATA ANALYSIS USING SAS DATA STEP | PROC SQL | PROC MEANS | PROC UNIVARIATE | PROC SGPLOT | MACROS | DATE FUNCTIONS | APPEND | MERGE | TRANSPOSE

 options nocenter;

1.CREATING THE SAFARI ZONES DATASET 

data safari_zones;

    length Zone_Name $25;

    format Start_Date Review_Date date9.;

    input Zone_Name $ Animals_Count Tourists Area_Size Revenue Safety_Index 

          Start_Date :date9.;

          Review_Date = intnx('year', Start_Date, 1, 'same');

    datalines;

Ranthambore 1200 450000 392 85 82 01JAN2015

JimCorbett 1500 520000 520 95 88 15MAR2014

Kaziranga 1800 610000 430 110 90 20FEB2016

GirForest 900 380000 1412 70 85 10APR2013

Bandipur 1100 410000 874 78 80 05MAY2015

Sundarbans 1600 470000 4262 105 75 12JUN2012

Periyar 950 290000 925 60 83 18JUL2016

Tadoba 1250 340000 625 72 86 09AUG2017

Nagarhole 1150 360000 643 74 84 23SEP2014

Pench 980 310000 758 65 81 11OCT2015

Manas 1350 330000 950 68 78 14NOV2013

Satpura 1050 270000 524 55 79 19DEC2016

;

run;

proc print data=safari_zones;

run;

OUTPUT:

Obs	Zone_Name	Start_Date	Review_Date	Animals_Count	Tourists	Area_Size	Revenue	Safety_Index
1	Ranthambore	01JAN2015	01JAN2016	1200	450000	392	85	82
2	JimCorbett	15MAR2014	15MAR2015	1500	520000	520	95	88
3	Kaziranga	20FEB2016	20FEB2017	1800	610000	430	110	90
4	GirForest	10APR2013	10APR2014	900	380000	1412	70	85
5	Bandipur	05MAY2015	05MAY2016	1100	410000	874	78	80
6	Sundarbans	12JUN2012	12JUN2013	1600	470000	4262	105	75
7	Periyar	18JUL2016	18JUL2017	950	290000	925	60	83
8	Tadoba	09AUG2017	09AUG2018	1250	340000	625	72	86
9	Nagarhole	23SEP2014	23SEP2015	1150	360000	643	74	84
10	Pench	11OCT2015	11OCT2016	980	310000	758	65	81
11	Manas	14NOV2013	14NOV2014	1350	330000	950	68	78
12	Satpura	19DEC2016	19DEC2017	1050	270000	524	55	79

2.USING MDY AND INTCK FOR DATE ANALYSIS

data safari_dates;

    set safari_zones;

    Fiscal_Start = mdy(4,1,year(Start_Date));

    Years_Active = intck('year', Start_Date, today());

    format Fiscal_Start date9.;

run;

proc print data=safari_dates;

run;

OUTPUT:

Obs	Zone_Name	Start_Date	Review_Date	Animals_Count	Tourists	Area_Size	Revenue	Safety_Index	Fiscal_Start	Years_Active
1	Ranthambore	01JAN2015	01JAN2016	1200	450000	392	85	82	01APR2015	10
2	JimCorbett	15MAR2014	15MAR2015	1500	520000	520	95	88	01APR2014	11
3	Kaziranga	20FEB2016	20FEB2017	1800	610000	430	110	90	01APR2016	9
4	GirForest	10APR2013	10APR2014	900	380000	1412	70	85	01APR2013	12
5	Bandipur	05MAY2015	05MAY2016	1100	410000	874	78	80	01APR2015	10
6	Sundarbans	12JUN2012	12JUN2013	1600	470000	4262	105	75	01APR2012	13
7	Periyar	18JUL2016	18JUL2017	950	290000	925	60	83	01APR2016	9
8	Tadoba	09AUG2017	09AUG2018	1250	340000	625	72	86	01APR2017	8
9	Nagarhole	23SEP2014	23SEP2015	1150	360000	643	74	84	01APR2014	11
10	Pench	11OCT2015	11OCT2016	980	310000	758	65	81	01APR2015	10
11	Manas	14NOV2013	14NOV2014	1350	330000	950	68	78	01APR2013	12
12	Satpura	19DEC2016	19DEC2017	1050	270000	524	55	79	01APR2016	9

3.DATA VALIDATION USING PROC CONTENTS AND PRINT

proc contents data=safari_dates;

run;

OUTPUT:

The CONTENTS Procedure

Data Set Name	WORK.SAFARI_DATES	Observations	12
Member Type	DATA	Variables	10
Engine	V9	Indexes	0
Created	12/28/2025 08:05:23	Observation Length	104
Last Modified	12/28/2025 08:05:23	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	SOLARIS_X86_64, LINUX_X86_64, ALPHA_TRU64, LINUX_IA64
Encoding	utf-8 Unicode (UTF-8)

Engine/Host Dependent Information
Data Set Page Size	131072
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	1258
Obs in First Data Page	12
Number of Data Set Repairs	0
Filename	/saswork/SAS_work3EB20001C611_odaws01-apse1-2.oda.sas.com/SAS_work5F2E0001C611_odaws01-apse1-2.oda.sas.com/safari_dates.sas7bdat
Release Created	9.0401M8
Host Created	Linux
Inode Number	1207862
Access Permission	rw-r--r--
Owner Name	u63247146
File Size	256KB
File Size (bytes)	262144

Alphabetic List of Variables and Attributes
#	Variable	Type	Len	Format
4	Animals_Count	Num	8
6	Area_Size	Num	8
9	Fiscal_Start	Num	8	DATE9.
7	Revenue	Num	8
3	Review_Date	Num	8	DATE9.
8	Safety_Index	Num	8
2	Start_Date	Num	8	DATE9.
5	Tourists	Num	8
10	Years_Active	Num	8
1	Zone_Name	Char	25

proc print data=safari_dates(obs=5);

run;

OUTPUT:

Obs	Zone_Name	Start_Date	Review_Date	Animals_Count	Tourists	Area_Size	Revenue	Safety_Index	Fiscal_Start	Years_Active
1	Ranthambore	01JAN2015	01JAN2016	1200	450000	392	85	82	01APR2015	10
2	JimCorbett	15MAR2014	15MAR2015	1500	520000	520	95	88	01APR2014	11
3	Kaziranga	20FEB2016	20FEB2017	1800	610000	430	110	90	01APR2016	9
4	GirForest	10APR2013	10APR2014	900	380000	1412	70	85	01APR2013	12
5	Bandipur	05MAY2015	05MAY2016	1100	410000	874	78	80	01APR2015	10

4.DATA ANALYSIS USING PROC SQL

proc sql;

    create table safari_sql as

    select Zone_Name,Animals_Count,Tourists,Area_Size,Revenue,Safety_Index,Years_Active,

            (Revenue / Area_Size) as Revenue_Density

    from safari_dates

    where Safety_Index > 80

    order by Revenue desc;

quit;

proc print data=safari_sql;

run;

OUTPUT:

Obs	Zone_Name	Animals_Count	Tourists	Area_Size	Revenue	Safety_Index	Years_Active	Revenue_Density
1	Kaziranga	1800	610000	430	110	90	9	0.25581
2	JimCorbett	1500	520000	520	95	88	11	0.18269
3	Ranthambore	1200	450000	392	85	82	10	0.21684
4	Nagarhole	1150	360000	643	74	84	11	0.11509
5	Tadoba	1250	340000	625	72	86	8	0.11520
6	GirForest	900	380000	1412	70	85	12	0.04958
7	Pench	980	310000	758	65	81	10	0.08575
8	Periyar	950	290000	925	60	83	9	0.06486

5.STATISTICAL SUMMARY USING PROC MEANS

proc means data=safari_zones mean min max sum;

    var Animals_Count Tourists Area_Size Revenue Safety_Index;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum	Sum
Animals_Count Tourists Area_Size Revenue Safety_Index	1235.83 395000.00 1026.25 78.0833333 82.5833333	900.0000000 270000.00 392.0000000 55.0000000 75.0000000	1800.00 610000.00 4262.00 110.0000000 90.0000000	14830.00 4740000.00 12315.00 937.0000000 991.0000000

6.DISTRIBUTION ANALYSIS USING PROC UNIVARIATE

proc univariate data=safari_zones;

    var Revenue Safety_Index;

    histogram Revenue Safety_Index;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Revenue

Moments
N	12	Sum Weights	12
Mean	78.0833333	Sum Observations	937
Std Deviation	17.3962291	Variance	302.628788
Skewness	0.72290943	Kurtosis	-0.4490746
Uncorrected SS	76493	Corrected SS	3328.91667
Coeff Variation	22.2790554	Std Error Mean	5.02185879

Basic Statistical Measures
Location		Variability
Mean	78.08333	Std Deviation	17.39623
Median	73.00000	Variance	302.62879
Mode	.	Range	55.00000
		Interquartile Range	23.50000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	15.54869	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	110.0
99%	110.0
95%	110.0
90%	105.0
75% Q3	90.0
50% Median	73.0
25% Q1	66.5
10%	60.0
5%	55.0
1%	55.0
0% Min	55.0

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
55	12	78	5
60	7	85	1
65	10	95	2
68	11	105	6
70	4	110	3

The UNIVARIATE Procedure

The UNIVARIATE Procedure

Variable: Safety_Index

Moments
N	12	Sum Weights	12
Mean	82.5833333	Sum Observations	991
Std Deviation	4.31610795	Variance	18.6287879
Skewness	0.03258173	Kurtosis	-0.3893496
Uncorrected SS	82045	Corrected SS	204.916667
Coeff Variation	5.22636685	Std Error Mean	1.24595304

Basic Statistical Measures
Location		Variability
Mean	82.58333	Std Deviation	4.31611
Median	82.50000	Variance	18.62879
Mode	.	Range	15.00000
		Interquartile Range	6.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	66.28126	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	90.0
99%	90.0
95%	90.0
90%	88.0
75% Q3	85.5
50% Median	82.5
25% Q1	79.5
10%	78.0
5%	75.0
1%	75.0
0% Min	75.0

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
75	6	84	9
78	11	85	4
79	12	86	8
80	5	88	2
81	10	90	3

The UNIVARIATE Procedure

7.VISUAL ANALYSIS USING PROC SGPLOT

proc sgplot data=safari_zones;

    scatter x=Area_Size y=Revenue;

    title "Revenue vs Area Size for Safari Zones";

run;

OUTPUT:

8.MACRO FOR SAFARI ZONE RATING

%macro zone_rating;

data safari_rated;

    set safari_zones;

    length Zone_Rating $12;

    if Safety_Index >= 85 then Zone_Rating = "EXCELLENT";

    else if Safety_Index >= 80 then Zone_Rating = "GOOD";

    else if Safety_Index >= 75 then Zone_Rating = "MODERATE";

    else Zone_Rating = "RISKY";

run;

proc print data=safari_rated;

run;

%mend;

%zone_rating;

OUTPUT:

Obs	Zone_Name	Start_Date	Review_Date	Animals_Count	Tourists	Area_Size	Revenue	Safety_Index	Zone_Rating
1	Ranthambore	01JAN2015	01JAN2016	1200	450000	392	85	82	GOOD
2	JimCorbett	15MAR2014	15MAR2015	1500	520000	520	95	88	EXCELLENT
3	Kaziranga	20FEB2016	20FEB2017	1800	610000	430	110	90	EXCELLENT
4	GirForest	10APR2013	10APR2014	900	380000	1412	70	85	EXCELLENT
5	Bandipur	05MAY2015	05MAY2016	1100	410000	874	78	80	GOOD
6	Sundarbans	12JUN2012	12JUN2013	1600	470000	4262	105	75	MODERATE
7	Periyar	18JUL2016	18JUL2017	950	290000	925	60	83	GOOD
8	Tadoba	09AUG2017	09AUG2018	1250	340000	625	72	86	EXCELLENT
9	Nagarhole	23SEP2014	23SEP2015	1150	360000	643	74	84	GOOD
10	Pench	11OCT2015	11OCT2016	980	310000	758	65	81	GOOD
11	Manas	14NOV2013	14NOV2014	1350	330000	950	68	78	MODERATE
12	Satpura	19DEC2016	19DEC2017	1050	270000	524	55	79	MODERATE

9.DATA APPEND OPERATION

data new_zone;

    length Zone_Name $25;

    format Start_Date date9.;

    Zone_Name="Valmiki";

    Animals_Count=850;

    Tourists=220000;

    Area_Size=335;

    Revenue=48;

    Safety_Index=77;

    Start_Date='01JAN2018'd;

run;

proc print data=new_zone;

run;

OUTPUT:

Obs	Zone_Name	Start_Date	Animals_Count	Tourists	Area_Size	Revenue	Safety_Index
1	Valmiki	01JAN2018	850	220000	335	48	77

proc append base=safari_zones 

            data=new_zone force;

run;

proc print data=safari_zones;

run;

OUTPUT:

Obs	Zone_Name	Start_Date	Review_Date	Animals_Count	Tourists	Area_Size	Revenue	Safety_Index
1	Ranthambore	01JAN2015	01JAN2016	1200	450000	392	85	82
2	JimCorbett	15MAR2014	15MAR2015	1500	520000	520	95	88
3	Kaziranga	20FEB2016	20FEB2017	1800	610000	430	110	90
4	GirForest	10APR2013	10APR2014	900	380000	1412	70	85
5	Bandipur	05MAY2015	05MAY2016	1100	410000	874	78	80
6	Sundarbans	12JUN2012	12JUN2013	1600	470000	4262	105	75
7	Periyar	18JUL2016	18JUL2017	950	290000	925	60	83
8	Tadoba	09AUG2017	09AUG2018	1250	340000	625	72	86
9	Nagarhole	23SEP2014	23SEP2015	1150	360000	643	74	84
10	Pench	11OCT2015	11OCT2016	980	310000	758	65	81
11	Manas	14NOV2013	14NOV2014	1350	330000	950	68	78
12	Satpura	19DEC2016	19DEC2017	1050	270000	524	55	79
13	Valmiki	01JAN2018	.	850	220000	335	48	77

10.MERGE OPERATION

proc sort data=safari_zones; by Zone_Name; run;

proc print data=safari_zones;

run;

OUTPUT:

Obs	Zone_Name	Start_Date	Review_Date	Animals_Count	Tourists	Area_Size	Revenue	Safety_Index
1	Bandipur	05MAY2015	05MAY2016	1100	410000	874	78	80
2	GirForest	10APR2013	10APR2014	900	380000	1412	70	85
3	JimCorbett	15MAR2014	15MAR2015	1500	520000	520	95	88
4	Kaziranga	20FEB2016	20FEB2017	1800	610000	430	110	90
5	Manas	14NOV2013	14NOV2014	1350	330000	950	68	78
6	Nagarhole	23SEP2014	23SEP2015	1150	360000	643	74	84
7	Pench	11OCT2015	11OCT2016	980	310000	758	65	81
8	Periyar	18JUL2016	18JUL2017	950	290000	925	60	83
9	Ranthambore	01JAN2015	01JAN2016	1200	450000	392	85	82
10	Satpura	19DEC2016	19DEC2017	1050	270000	524	55	79
11	Sundarbans	12JUN2012	12JUN2013	1600	470000	4262	105	75
12	Tadoba	09AUG2017	09AUG2018	1250	340000	625	72	86
13	Valmiki	01JAN2018	.	850	220000	335	48	77

proc sort data=safari_rated; by Zone_Name; run;

proc print data=safari_rated;

run;

OUTPUT:

Obs	Zone_Name	Start_Date	Review_Date	Animals_Count	Tourists	Area_Size	Revenue	Safety_Index	Zone_Rating
1	Bandipur	05MAY2015	05MAY2016	1100	410000	874	78	80	GOOD
2	GirForest	10APR2013	10APR2014	900	380000	1412	70	85	EXCELLENT
3	JimCorbett	15MAR2014	15MAR2015	1500	520000	520	95	88	EXCELLENT
4	Kaziranga	20FEB2016	20FEB2017	1800	610000	430	110	90	EXCELLENT
5	Manas	14NOV2013	14NOV2014	1350	330000	950	68	78	MODERATE
6	Nagarhole	23SEP2014	23SEP2015	1150	360000	643	74	84	GOOD
7	Pench	11OCT2015	11OCT2016	980	310000	758	65	81	GOOD
8	Periyar	18JUL2016	18JUL2017	950	290000	925	60	83	GOOD
9	Ranthambore	01JAN2015	01JAN2016	1200	450000	392	85	82	GOOD
10	Satpura	19DEC2016	19DEC2017	1050	270000	524	55	79	MODERATE
11	Sundarbans	12JUN2012	12JUN2013	1600	470000	4262	105	75	MODERATE
12	Tadoba	09AUG2017	09AUG2018	1250	340000	625	72	86	EXCELLENT

data safari_merged;

    merge safari_zones safari_rated;

    by Zone_Name;

run;

proc print data=safari_merged;

run;

OUTPUT:

Obs	Zone_Name	Start_Date	Review_Date	Animals_Count	Tourists	Area_Size	Revenue	Safety_Index	Zone_Rating
1	Bandipur	05MAY2015	05MAY2016	1100	410000	874	78	80	GOOD
2	GirForest	10APR2013	10APR2014	900	380000	1412	70	85	EXCELLENT
3	JimCorbett	15MAR2014	15MAR2015	1500	520000	520	95	88	EXCELLENT
4	Kaziranga	20FEB2016	20FEB2017	1800	610000	430	110	90	EXCELLENT
5	Manas	14NOV2013	14NOV2014	1350	330000	950	68	78	MODERATE
6	Nagarhole	23SEP2014	23SEP2015	1150	360000	643	74	84	GOOD
7	Pench	11OCT2015	11OCT2016	980	310000	758	65	81	GOOD
8	Periyar	18JUL2016	18JUL2017	950	290000	925	60	83	GOOD
9	Ranthambore	01JAN2015	01JAN2016	1200	450000	392	85	82	GOOD
10	Satpura	19DEC2016	19DEC2017	1050	270000	524	55	79	MODERATE
11	Sundarbans	12JUN2012	12JUN2013	1600	470000	4262	105	75	MODERATE
12	Tadoba	09AUG2017	09AUG2018	1250	340000	625	72	86	EXCELLENT
13	Valmiki	01JAN2018	.	850	220000	335	48	77

11.TRANSPOSE OPERATION

proc transpose data=safari_zones out=safari_transposed prefix=Zone_;

    var Revenue;

    id Zone_Name;

run;

proc print data=safari_transposed;

run;

OUTPUT:

Obs	_NAME_	Zone_Bandipur	Zone_GirForest	Zone_JimCorbett	Zone_Kaziranga	Zone_Manas	Zone_Nagarhole	Zone_Pench	Zone_Periyar	Zone_Ranthambore	Zone_Satpura	Zone_Sundarbans	Zone_Tadoba	Zone_Valmiki
1	Revenue	78	70	95	110	68	74	65	60	85	55	105	72	48

12.PROC MEANS

proc means data=safari_zones;

    var Animals_Count Tourists Revenue;

    group by Zone_Name;

run;

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

OUTPUT:

The MEANS Procedure

Zone_Name	N Obs	Variable	N	Mean	Std Dev	Minimum	Maximum
Bandipur	1	Animals_Count Tourists Revenue	1 1 1	1100.00 410000.00 78.0000000	. . .	1100.00 410000.00 78.0000000	1100.00 410000.00 78.0000000
GirForest	1	Animals_Count Tourists Revenue	1 1 1	900.0000000 380000.00 70.0000000	. . .	900.0000000 380000.00 70.0000000	900.0000000 380000.00 70.0000000
JimCorbett	1	Animals_Count Tourists Revenue	1 1 1	1500.00 520000.00 95.0000000	. . .	1500.00 520000.00 95.0000000	1500.00 520000.00 95.0000000
Kaziranga	1	Animals_Count Tourists Revenue	1 1 1	1800.00 610000.00 110.0000000	. . .	1800.00 610000.00 110.0000000	1800.00 610000.00 110.0000000
Manas	1	Animals_Count Tourists Revenue	1 1 1	1350.00 330000.00 68.0000000	. . .	1350.00 330000.00 68.0000000	1350.00 330000.00 68.0000000
Nagarhole	1	Animals_Count Tourists Revenue	1 1 1	1150.00 360000.00 74.0000000	. . .	1150.00 360000.00 74.0000000	1150.00 360000.00 74.0000000
Pench	1	Animals_Count Tourists Revenue	1 1 1	980.0000000 310000.00 65.0000000	. . .	980.0000000 310000.00 65.0000000	980.0000000 310000.00 65.0000000
Periyar	1	Animals_Count Tourists Revenue	1 1 1	950.0000000 290000.00 60.0000000	. . .	950.0000000 290000.00 60.0000000	950.0000000 290000.00 60.0000000
Ranthambore	1	Animals_Count Tourists Revenue	1 1 1	1200.00 450000.00 85.0000000	. . .	1200.00 450000.00 85.0000000	1200.00 450000.00 85.0000000
Satpura	1	Animals_Count Tourists Revenue	1 1 1	1050.00 270000.00 55.0000000	. . .	1050.00 270000.00 55.0000000	1050.00 270000.00 55.0000000
Sundarbans	1	Animals_Count Tourists Revenue	1 1 1	1600.00 470000.00 105.0000000	. . .	1600.00 470000.00 105.0000000	1600.00 470000.00 105.0000000
Tadoba	1	Animals_Count Tourists Revenue	1 1 1	1250.00 340000.00 72.0000000	. . .	1250.00 340000.00 72.0000000	1250.00 340000.00 72.0000000
Valmiki	1	Animals_Count Tourists Revenue	1 1 1	850.0000000 220000.00 48.0000000	. . .	850.0000000 220000.00 48.0000000	850.0000000 220000.00 48.0000000

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