Sunday, 11 January 2026

367.ZOO OPERATIONAL PERFORMANCE ANALYSIS USING SAS PROC SQL | PROC FREQ | PROC MEANS | PROC UNIVARIATE | MACROS | INTNX | INTCK | MDY | MERGE | APPEND | TRANSPOSE | SET | PROC SGPLOT

ZOO OPERATIONAL PERFORMANCE ANALYSIS USING SAS PROC SQL | PROC FREQ | PROC MEANS | PROC UNIVARIATE | MACROS | INTNX | INTCK | MDY | MERGE | APPEND | TRANSPOSE | SET | PROC SGPLOT


 options nocenter;

STEP 1 – CREATE MASTER ZOO DATASET

data zoo_master;

    format Open_Date Visit_Date date9.;

    input Zoo_ID Zoo_Name $ Animals_Count Workers Area Annual_Visitors Open_Date :date9.;

    Visit_Date = intnx('month', Open_Date, 6, 'same');

    Years_Operating = intck('year', Open_Date, today());

datalines;

1 NehruZoo 350 120 300 1200000 01JAN2000

2 DelhiZoo 420 140 350 1500000 15MAR1995

3 MysoreZoo 280 95 200 850000 10JUN2005

4 ArignarZoo 520 200 600 2100000 01FEB1990

5 IndoreZoo 260 90 180 780000 11AUG2008

6 BhopalZoo 300 110 240 950000 05JUL2003

7 SuratZoo 210 70 160 600000 19APR2010

8 JaipurZoo 390 130 320 1350000 25DEC1998

9 PatnaZoo 340 115 280 1100000 30JAN2002

10 KanpurZoo 310 105 260 1000000 17SEP2004

11 RanchiZoo 230 80 190 650000 14NOV2012

12 GuwahatiZoo 270 90 210 800000 03MAY2009

13 Nandankanan 550 210 650 2300000 01JAN1987

14 Bannerghatta 480 180 550 2000000 21JUN1996

15 AliporeZoo 360 125 300 1400000 09FEB1999

16 RajgirZoo 190 65 140 450000 12APR2015

;

run;

proc print data=zoo_master;

run;

OUTPUT:

ObsOpen_DateVisit_DateZoo_IDZoo_NameAnimals_CountWorkersAreaAnnual_VisitorsYears_Operating
101JAN200001JUL20001NehruZoo350120300120000026
215MAR199515SEP19952DelhiZoo420140350150000031
310JUN200510DEC20053MysoreZo2809520085000021
401FEB199001AUG19904ArignarZ520200600210000036
511AUG200811FEB20095IndoreZo2609018078000018
605JUL200305JAN20046BhopalZo30011024095000023
719APR201019OCT20107SuratZoo2107016060000016
825DEC199825JUN19998JaipurZo390130320135000028
930JAN200230JUL20029PatnaZoo340115280110000024
1017SEP200417MAR200510KanpurZo310105260100000022
1114NOV201214MAY201311RanchiZo2308019065000014
1203MAY200903NOV200912Guwahati2709021080000017
1301JAN198701JUL198713Nandanka550210650230000039
1421JUN199621DEC199614Bannergh480180550200000030
1509FEB199909AUG199915AliporeZ360125300140000027
1612APR201512OCT201516RajgirZo1906514045000011

Why these variables were chosen



STEP 2 – CREATE FINANCIAL DATA (USING SET & APPEND)

data zoo_finance_2024;

    set zoo_master;

    Revenue = Annual_Visitors * 12;

    Maintenance = Animals_Count * 2000;

    Year = 2024;

run;

proc print data=zoo_finance_2024;

run;

OUTPUT:

ObsOpen_DateVisit_DateZoo_IDZoo_NameAnimals_CountWorkersAreaAnnual_VisitorsYears_OperatingRevenueMaintenanceYear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data zoo_finance_2025;

    set zoo_master;

    Revenue = Annual_Visitors * 13;

    Maintenance = Animals_Count * 2200;

    Year = 2025;

run;

proc print data=zoo_finance_2025;

run;

OUTPUT:

ObsOpen_DateVisit_DateZoo_IDZoo_NameAnimals_CountWorkersAreaAnnual_VisitorsYears_OperatingRevenueMaintenanceYear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proc append base=zoo_finance_2024 

                     data=zoo_finance_2025;

run;

proc print data=zoo_finance_2024;

run;

OUTPUT:

ObsOpen_DateVisit_DateZoo_IDZoo_NameAnimals_CountWorkersAreaAnnual_VisitorsYears_OperatingRevenueMaintenanceYear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STEP 3 – MERGE OPERATIONAL AND FINANCIAL DATA

proc sort data=zoo_master; by Zoo_ID; run;

proc print data=zoo_master;

run;

OUTPUT:

ObsOpen_DateVisit_DateZoo_IDZoo_NameAnimals_CountWorkersAreaAnnual_VisitorsYears_Operating
101JAN200001JUL20001NehruZoo350120300120000026
215MAR199515SEP19952DelhiZoo420140350150000031
310JUN200510DEC20053MysoreZo2809520085000021
401FEB199001AUG19904ArignarZ520200600210000036
511AUG200811FEB20095IndoreZo2609018078000018
605JUL200305JAN20046BhopalZo30011024095000023
719APR201019OCT20107SuratZoo2107016060000016
825DEC199825JUN19998JaipurZo390130320135000028
930JAN200230JUL20029PatnaZoo340115280110000024
1017SEP200417MAR200510KanpurZo310105260100000022
1114NOV201214MAY201311RanchiZo2308019065000014
1203MAY200903NOV200912Guwahati2709021080000017
1301JAN198701JUL198713Nandanka550210650230000039
1421JUN199621DEC199614Bannergh480180550200000030
1509FEB199909AUG199915AliporeZ360125300140000027
1612APR201512OCT201516RajgirZo1906514045000011


proc sort data=zoo_finance_2024; by Zoo_ID; run;

proc print data=zoo_finance_2024;

run;

OUTPUT:

ObsOpen_DateVisit_DateZoo_IDZoo_NameAnimals_CountWorkersAreaAnnual_VisitorsYears_OperatingRevenueMaintenanceYear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data zoo_full;

    merge zoo_master zoo_finance_2024;

    by Zoo_ID;

run;

proc print data=zoo_full;

run;

OUTPUT:

ObsOpen_DateVisit_DateZoo_IDZoo_NameAnimals_CountWorkersAreaAnnual_VisitorsYears_OperatingRevenueMaintenanceYear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STEP 4 – CREATE EFFICIENCY METRICS

data zoo_metrics;

    set zoo_full;

    Visitors_per_Animal = Annual_Visitors / Animals_Count;

    Visitors_per_Worker = Annual_Visitors / Workers;

    Revenue_per_Acre = Revenue / Area;

run;

proc print data=zoo_metrics;

run;

OUTPUT:

ObsOpen_DateVisit_DateZoo_IDZoo_NameAnimals_CountWorkersAreaAnnual_VisitorsYears_OperatingRevenueMaintenanceYearVisitors_per_AnimalVisitors_per_WorkerRevenue_per_Acre
101JAN200001JUL20001NehruZoo3501203001200000261440000070000020243428.5710000.0048000.00
201JAN200001JUL20001NehruZoo3501203001200000261560000077000020253428.5710000.0052000.00
315MAR199515SEP19952DelhiZoo4201403501500000311800000084000020243571.4310714.2951428.57
415MAR199515SEP19952DelhiZoo4201403501500000311950000092400020253571.4310714.2955714.29
510JUN200510DEC20053MysoreZo28095200850000211020000056000020243035.718947.3751000.00
610JUN200510DEC20053MysoreZo28095200850000211105000061600020253035.718947.3755250.00
701FEB199001AUG19904ArignarZ52020060021000003625200000104000020244038.4610500.0042000.00
801FEB199001AUG19904ArignarZ52020060021000003627300000114400020254038.4610500.0045500.00
911AUG200811FEB20095IndoreZo2609018078000018936000052000020243000.008666.6752000.00
1011AUG200811FEB20095IndoreZo26090180780000181014000057200020253000.008666.6756333.33
1105JUL200305JAN20046BhopalZo300110240950000231140000060000020243166.678636.3647500.00
1205JUL200305JAN20046BhopalZo300110240950000231235000066000020253166.678636.3651458.33
1319APR201019OCT20107SuratZoo2107016060000016720000042000020242857.148571.4345000.00
1419APR201019OCT20107SuratZoo2107016060000016780000046200020252857.148571.4348750.00
1525DEC199825JUN19998JaipurZo3901303201350000281620000078000020243461.5410384.6250625.00
1625DEC199825JUN19998JaipurZo3901303201350000281755000085800020253461.5410384.6254843.75
1730JAN200230JUL20029PatnaZoo3401152801100000241320000068000020243235.299565.2247142.86
1830JAN200230JUL20029PatnaZoo3401152801100000241430000074800020253235.299565.2251071.43
1917SEP200417MAR200510KanpurZo3101052601000000221200000062000020243225.819523.8146153.85
2017SEP200417MAR200510KanpurZo3101052601000000221300000068200020253225.819523.8150000.00
2114NOV201214MAY201311RanchiZo2308019065000014780000046000020242826.098125.0041052.63
2214NOV201214MAY201311RanchiZo2308019065000014845000050600020252826.098125.0044473.68
2303MAY200903NOV200912Guwahati2709021080000017960000054000020242962.968888.8945714.29
2403MAY200903NOV200912Guwahati27090210800000171040000059400020252962.968888.8949523.81
2501JAN198701JUL198713Nandanka55021065023000003927600000110000020244181.8210952.3842461.54
2601JAN198701JUL198713Nandanka55021065023000003929900000121000020254181.8210952.3846000.00
2721JUN199621DEC199614Bannergh4801805502000000302400000096000020244166.6711111.1143636.36
2821JUN199621DEC199614Bannergh48018055020000003026000000105600020254166.6711111.1147272.73
2909FEB199909AUG199915AliporeZ3601253001400000271680000072000020243888.8911200.0056000.00
3009FEB199909AUG199915AliporeZ3601253001400000271820000079200020253888.8911200.0060666.67
3112APR201512OCT201516RajgirZo1906514045000011540000038000020242368.426923.0838571.43
3212APR201512OCT201516RajgirZo1906514045000011585000041800020252368.426923.0841785.71


STEP 5 – MACRO FOR ZOO EFFICIENCY GROUPING

%macro efficiency;

data zoo_efficiency;

    set zoo_metrics;

    if Visitors_per_Worker > 12000 then Efficiency = "EXCELLENT";

    else if Visitors_per_Worker > 8000 then Efficiency = "GOOD";

    else Efficiency = "LOW";

run;

proc print data=zoo_efficiency;

run;

%mend;


%efficiency;

OUTPUT:

ObsOpen_DateVisit_DateZoo_IDZoo_NameAnimals_CountWorkersAreaAnnual_VisitorsYears_OperatingRevenueMaintenanceYearVisitors_per_AnimalVisitors_per_WorkerRevenue_per_AcreEfficiency
101JAN200001JUL20001NehruZoo3501203001200000261440000070000020243428.5710000.0048000.00GOOD
201JAN200001JUL20001NehruZoo3501203001200000261560000077000020253428.5710000.0052000.00GOOD
315MAR199515SEP19952DelhiZoo4201403501500000311800000084000020243571.4310714.2951428.57GOOD
415MAR199515SEP19952DelhiZoo4201403501500000311950000092400020253571.4310714.2955714.29GOOD
510JUN200510DEC20053MysoreZo28095200850000211020000056000020243035.718947.3751000.00GOOD
610JUN200510DEC20053MysoreZo28095200850000211105000061600020253035.718947.3755250.00GOOD
701FEB199001AUG19904ArignarZ52020060021000003625200000104000020244038.4610500.0042000.00GOOD
801FEB199001AUG19904ArignarZ52020060021000003627300000114400020254038.4610500.0045500.00GOOD
911AUG200811FEB20095IndoreZo2609018078000018936000052000020243000.008666.6752000.00GOOD
1011AUG200811FEB20095IndoreZo26090180780000181014000057200020253000.008666.6756333.33GOOD
1105JUL200305JAN20046BhopalZo300110240950000231140000060000020243166.678636.3647500.00GOOD
1205JUL200305JAN20046BhopalZo300110240950000231235000066000020253166.678636.3651458.33GOOD
1319APR201019OCT20107SuratZoo2107016060000016720000042000020242857.148571.4345000.00GOOD
1419APR201019OCT20107SuratZoo2107016060000016780000046200020252857.148571.4348750.00GOOD
1525DEC199825JUN19998JaipurZo3901303201350000281620000078000020243461.5410384.6250625.00GOOD
1625DEC199825JUN19998JaipurZo3901303201350000281755000085800020253461.5410384.6254843.75GOOD
1730JAN200230JUL20029PatnaZoo3401152801100000241320000068000020243235.299565.2247142.86GOOD
1830JAN200230JUL20029PatnaZoo3401152801100000241430000074800020253235.299565.2251071.43GOOD
1917SEP200417MAR200510KanpurZo3101052601000000221200000062000020243225.819523.8146153.85GOOD
2017SEP200417MAR200510KanpurZo3101052601000000221300000068200020253225.819523.8150000.00GOOD
2114NOV201214MAY201311RanchiZo2308019065000014780000046000020242826.098125.0041052.63GOOD
2214NOV201214MAY201311RanchiZo2308019065000014845000050600020252826.098125.0044473.68GOOD
2303MAY200903NOV200912Guwahati2709021080000017960000054000020242962.968888.8945714.29GOOD
2403MAY200903NOV200912Guwahati27090210800000171040000059400020252962.968888.8949523.81GOOD
2501JAN198701JUL198713Nandanka55021065023000003927600000110000020244181.8210952.3842461.54GOOD
2601JAN198701JUL198713Nandanka55021065023000003929900000121000020254181.8210952.3846000.00GOOD
2721JUN199621DEC199614Bannergh4801805502000000302400000096000020244166.6711111.1143636.36GOOD
2821JUN199621DEC199614Bannergh48018055020000003026000000105600020254166.6711111.1147272.73GOOD
2909FEB199909AUG199915AliporeZ3601253001400000271680000072000020243888.8911200.0056000.00GOOD
3009FEB199909AUG199915AliporeZ3601253001400000271820000079200020253888.8911200.0060666.67GOOD
3112APR201512OCT201516RajgirZo1906514045000011540000038000020242368.426923.0838571.43LOW
3212APR201512OCT201516RajgirZo1906514045000011585000041800020252368.426923.0841785.71LOW


STEP 6 – PROC SQL ANALYSIS

proc sql;

    create table zoo_sql as

    select Zoo_Name,Animals_Count,Workers,Area,Annual_Visitors,Revenue,Efficiency

    from zoo_efficiency

    where Annual_Visitors > 900000;

quit;

proc print data=zoo_sql;

run;

OUTPUT:

ObsZoo_NameAnimals_CountWorkersAreaAnnual_VisitorsRevenueEfficiency
1NehruZoo350120300120000014400000GOOD
2NehruZoo350120300120000015600000GOOD
3DelhiZoo420140350150000018000000GOOD
4DelhiZoo420140350150000019500000GOOD
5ArignarZ520200600210000025200000GOOD
6ArignarZ520200600210000027300000GOOD
7BhopalZo30011024095000011400000GOOD
8BhopalZo30011024095000012350000GOOD
9JaipurZo390130320135000016200000GOOD
10JaipurZo390130320135000017550000GOOD
11PatnaZoo340115280110000013200000GOOD
12PatnaZoo340115280110000014300000GOOD
13KanpurZo310105260100000012000000GOOD
14KanpurZo310105260100000013000000GOOD
15Nandanka550210650230000027600000GOOD
16Nandanka550210650230000029900000GOOD
17Bannergh480180550200000024000000GOOD
18Bannergh480180550200000026000000GOOD
19AliporeZ360125300140000016800000GOOD
20AliporeZ360125300140000018200000GOOD


STEP 7 – FREQUENCY OF EFFICIENCY GROUPS

proc freq data=zoo_efficiency;

    tables Efficiency;

run;

OUTPUT:

The FREQ Procedure

EfficiencyFrequencyPercentCumulative
Frequency		Cumulative
Percent
GOOD3093.753093.75
LOW26.2532100.00

STEP 8 – MEANS ANALYSIS

proc means data=zoo_efficiency mean max min;

    var Animals_Count  Workers  Annual_Visitors  Revenue;

    class Efficiency;

run;

OUTPUT:

The MEANS Procedure

EfficiencyN ObsVariableMeanMaximumMinimum
GOOD30
Animals_Count
Workers
Annual_Visitors
Revenue
351.3333333
124.0000000
1238666.67
15483333.33
550.0000000
210.0000000
2300000.00
29900000.00
210.0000000
70.0000000
600000.00
7200000.00
LOW2
Animals_Count
Workers
Annual_Visitors
Revenue
190.0000000
65.0000000
450000.00
5625000.00
190.0000000
65.0000000
450000.00
5850000.00
190.0000000
65.0000000
450000.00
5400000.00

STEP 9 – UNIVARIATE (DISTRIBUTION CHECK)

proc univariate data=zoo_efficiency;

    var Annual_Visitors Revenue Visitors_per_Worker;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Annual_Visitors

Moments
N32Sum Weights32
Mean1189375Sum Observations38060000
Std Deviation545189.742Variance2.97232E11
Skewness0.74207559Kurtosis-0.4543229
Uncorrected SS5.44818E13Corrected SS9.21419E12
Coeff Variation45.8383388Std Error Mean96376.8409
Basic Statistical Measures
LocationVariability
Mean1189375Std Deviation545190
Median1050000Variance2.97232E11
Mode450000Range1850000
  Interquartile Range660000

Note: The mode displayed is the smallest of 16 modes with a count of 2.

Tests for Location: Mu0=0
TestStatisticp Value
Student's tt12.34088Pr > |t|<.0001
SignM16Pr >= |M|<.0001
Signed RankS264Pr >= |S|<.0001
Quantiles (Definition 5)
LevelQuantile
100% Max2300000
99%2300000
95%2300000
90%2100000
75% Q31450000
50% Median1050000
25% Q1790000
10%600000
5%450000
1%450000
0% Min450000
Extreme Observations
LowestHighest
ValueObsValueObs
45000032200000028
4500003121000007
6000001421000008
60000013230000025
65000022230000026

The UNIVARIATE Procedure

Variable: Revenue

Moments
N32Sum Weights32
Mean14867187.5Sum Observations475750000
Std Deviation6847031.96Variance4.68818E13
Skewness0.75720622Kurtosis-0.391413
Uncorrected SS8.5264E15Corrected SS1.45334E15
Coeff Variation46.0546553Std Error Mean1210395.68
Basic Statistical Measures
LocationVariability
Mean14867188Std Deviation6847032
Median13100000Variance4.68818E13
Mode7800000Range24500000
  Interquartile Range8230000
Tests for Location: Mu0=0
TestStatisticp Value
Student's tt12.28292Pr > |t|<.0001
SignM16Pr >= |M|<.0001
Signed RankS264Pr >= |S|<.0001
Quantiles (Definition 5)
LevelQuantile
100% Max29900000
99%29900000
95%27600000
90%26000000
75% Q318100000
50% Median13100000
25% Q19870000
10%7800000
5%5850000
1%5400000
0% Min5400000
Extreme Observations
LowestHighest
ValueObsValueObs
540000031252000007
5850000322600000028
720000013273000008
7800000212760000025
7800000142990000026

The UNIVARIATE Procedure

Variable: Visitors_per_Worker

Moments
N32Sum Weights32
Mean9544.38832Sum Observations305420.426
Std Deviation1207.18258Variance1457289.79
Skewness-0.3667328Kurtosis-0.5677197
Uncorrected SS2960227135Corrected SS45175983.5
Coeff Variation12.6480875Std Error Mean213.401748
Basic Statistical Measures
LocationVariability
Mean9544.388Std Deviation1207
Median9544.513Variance1457290
Mode6923.077Range4277
  Interquartile Range1956

Note: The mode displayed is the smallest of 16 modes with a count of 2.

Tests for Location: Mu0=0
TestStatisticp Value
Student's tt44.72498Pr > |t|<.0001
SignM16Pr >= |M|<.0001
Signed RankS264Pr >= |S|<.0001
Quantiles (Definition 5)
LevelQuantile
100% Max11200.00
99%11200.00
95%11200.00
90%11111.11
75% Q310607.14
50% Median9544.51
25% Q18651.52
10%8125.00
5%6923.08
1%6923.08
0% Min6923.08
Extreme Observations
LowestHighest
ValueObsValueObs
6923.083210952.426
6923.083111111.127
8125.002211111.128
8125.002111200.029
8571.431411200.030

STEP 10 – TRANSPOSE FOR REPORTING

proc transpose data=zoo_efficiency out=zoo_report;

    var Annual_Visitors Revenue Visitors_per_Worker;

    by Zoo_Name NotSorted;

run;

proc print data=zoo_report;

run;

OUTPUT:

ObsZoo_Name_NAME_COL1COL2
1NehruZooAnnual_Visitors1200000.001200000.00
2NehruZooRevenue14400000.0015600000.00
3NehruZooVisitors_per_Worker10000.0010000.00
4DelhiZooAnnual_Visitors1500000.001500000.00
5DelhiZooRevenue18000000.0019500000.00
6DelhiZooVisitors_per_Worker10714.2910714.29
7MysoreZoAnnual_Visitors850000.00850000.00
8MysoreZoRevenue10200000.0011050000.00
9MysoreZoVisitors_per_Worker8947.378947.37
10ArignarZAnnual_Visitors2100000.002100000.00
11ArignarZRevenue25200000.0027300000.00
12ArignarZVisitors_per_Worker10500.0010500.00
13IndoreZoAnnual_Visitors780000.00780000.00
14IndoreZoRevenue9360000.0010140000.00
15IndoreZoVisitors_per_Worker8666.678666.67
16BhopalZoAnnual_Visitors950000.00950000.00
17BhopalZoRevenue11400000.0012350000.00
18BhopalZoVisitors_per_Worker8636.368636.36
19SuratZooAnnual_Visitors600000.00600000.00
20SuratZooRevenue7200000.007800000.00
21SuratZooVisitors_per_Worker8571.438571.43
22JaipurZoAnnual_Visitors1350000.001350000.00
23JaipurZoRevenue16200000.0017550000.00
24JaipurZoVisitors_per_Worker10384.6210384.62
25PatnaZooAnnual_Visitors1100000.001100000.00
26PatnaZooRevenue13200000.0014300000.00
27PatnaZooVisitors_per_Worker9565.229565.22
28KanpurZoAnnual_Visitors1000000.001000000.00
29KanpurZoRevenue12000000.0013000000.00
30KanpurZoVisitors_per_Worker9523.819523.81
31RanchiZoAnnual_Visitors650000.00650000.00
32RanchiZoRevenue7800000.008450000.00
33RanchiZoVisitors_per_Worker8125.008125.00
34GuwahatiAnnual_Visitors800000.00800000.00
35GuwahatiRevenue9600000.0010400000.00
36GuwahatiVisitors_per_Worker8888.898888.89
37NandankaAnnual_Visitors2300000.002300000.00
38NandankaRevenue27600000.0029900000.00
39NandankaVisitors_per_Worker10952.3810952.38
40BannerghAnnual_Visitors2000000.002000000.00
41BannerghRevenue24000000.0026000000.00
42BannerghVisitors_per_Worker11111.1111111.11
43AliporeZAnnual_Visitors1400000.001400000.00
44AliporeZRevenue16800000.0018200000.00
45AliporeZVisitors_per_Worker11200.0011200.00
46RajgirZoAnnual_Visitors450000.00450000.00
47RajgirZoRevenue5400000.005850000.00
48RajgirZoVisitors_per_Worker6923.086923.08


STEP 11 - PROC SGPLOT VISUALIZATIONS

11.1 Visitors vs Animals

proc sgplot data=zoo_efficiency;

    scatter x=Animals_Count y=Annual_Visitors;

    title "Relationship Between Animals and Visitors";

run;

OUTPUT:

The SGPlot Procedure


11.2 Visitors per Worker by Zoo

proc sgplot data=zoo_efficiency;

    vbar Zoo_Name / response=Visitors_per_Worker;

    title "Staff Productivity by Zoo";

run;

OUTPUT:

The SGPlot Procedure


11.3 Revenue by Efficiency Group

proc sgplot data=zoo_efficiency;

    vbox Revenue / category=Efficiency;

    title "Revenue Distribution by Efficiency Group";

run;

OUTPUT:

The SGPlot Procedure


11.4 Visitors Trend by Zoo Age

proc sgplot data=zoo_efficiency;

    series x=Years_Operating y=Annual_Visitors;

    title "Zoo Maturity vs Visitor Count";

run;

OUTPUT:

The SGPlot Procedure



To Visit My Previous Different Types Of Oils Dataset:Click Here
To Visit My Previous Different Types Of Series 2025 Dataset:Click Here
To Visit My Previous Analyzing Yoga Asanas Worldwide Dataset:Click Here
To Visit My Previous Analyzing Indian Languages Dataset:Click Here  



Follow Us On : 


 


--->FOLLOW OUR BLOG FOR MORE INFORMATION.

--->PLEASE DO COMMENTS AND SHARE OUR BLOG.




on

No comments:

Post a Comment

Subscribe to: Post Comments (Atom)