313.Are We Losing Wildlife Faster Than We Think? – Animal Conservation Analysis Using SAS

Are We Losing Wildlife Faster Than We Think? – Animal Conservation Analysis Using SAS

options fmtsearch=(work.formats) nocenter nodate nonumber;

1. Proc Format

proc format library=work.formats;

  /* Standardize continent labels */

  value $continentfmt

    'AF' = 'Africa'

    'AS' = 'Asia'

    'EU' = 'Europe'

    'NA' = 'North America'

    'SA' = 'South America'

    'OC' = 'Oceania'

    'AN' = 'Antarctica'

    other = 'Unknown';

LOG:

NOTE: Format $CONTINENTFMT has been output.

  /* Diet categories */

  value $dietfmt

    'Herbivore'   = 'Herbivore'

    'Carnivore'   = 'Carnivore'

    'Omnivore'    = 'Omnivore'

    'Insectivore' = 'Insectivore'

    other         = 'Other/Unknown';

LOG:

NOTE: Format $DIETFMT has been output.

  /* Conservation status  */

  value $consfmt

    'LC' = 'Least Concern'

    'NT' = 'Near Threatened'

    'VU' = 'Vulnerable'

    'EN' = 'Endangered'

    'CR' = 'Critically Endangered'

    'EW' = 'Extinct in Wild'

    'EX' = 'Extinct'

    other = 'Unknown';

LOG:

NOTE: Format $CONSFMT has been output.

  /* Population bins using numeric format (for PROC FREQ / charts) */

  value popcat

    low - <1000 = 'Tiny (<1k)'

    1000 - <10000 = 'Small (1k-10k)'

    10000 - <100000 = 'Medium (10k-100k)'

    100000 - <1000000 = 'Large (100k-1M)'

    1000000 - high = 'Very Large (>1M)';

run;

LOG:

NOTE: Format POPCAT has been output.

2. Create the dataset using PROC SQL 

proc sql;

    create table work.animals as

    select 

        "African Elephant"      as Species,

        "AF"                              as Continent,

        415000                            as Population,

        60                                    as Lifespan,

        "Herbivore"                  as Diet,

        "EN"                              as Conservation_Status,

        '15FEB2022'd               as Last_Survey_Date,

        '01JAN2022'd               as Population_Estimate_Date

    from sashelp.class(obs=1)

    union all

    select "Bengal Tiger",        "AS", 2500,     15, "Carnivore", "EN", '10DEC2023'd, '01JAN2023'd

    from sashelp.class(obs=1)

    union all

    select "Giant Panda",         "AS", 1864,     20, "Herbivore", "VU", '05MAY2021'd, '01JAN2021'd

    from sashelp.class(obs=1)

    union all

    select "Blue Whale",          "OC", 10000,    90, "Carnivore", "EN", '20JUN2020'd, '01JAN2020'd

    from sashelp.class(obs=1)

    union all

    select "Red Kangaroo",        "OC", 11500000, 22, "Herbivore", "LC", '12NOV2022'd, '01JAN2022'd

    from sashelp.class(obs=1)

    union all

    select "Galapagos Tortoise",  "SA", 15000,    100, "Herbivore","EN",'02APR2019'd,'01JAN2019'd

    from sashelp.class(obs=1)

    union all

    select "Polar Bear",          "AN", 26000,    25, "Carnivore", "VU",'30SEP2022'd,'01JAN2022'd

    from sashelp.class(obs=1)

    union all

    select "Grizzly Bear",        "NA", 55000,    25, "Omnivore", "LC",'18JUL2023'd,'01JAN2023'd

    from sashelp.class(obs=1)

    union all

    select "Emperor Penguin",     "AN", 595000,   20, "Carnivore","NT",'05AUG2021'd,'01JAN2021'd

   from sashelp.class(obs=1)

    union all

    select "Mountain Gorilla",    "AF", 1005,     35, "Herbivore","CR",'22NOV2023'd,'01JAN2023'd

    from sashelp.class(obs=1)

    union all

    select "Komodo Dragon",       "OC", 3000,     30, "Carnivore","VU",'11MAR2022'd,'01JAN2022'd

    from sashelp.class(obs=1)

    union all

    select "European Hedgehog",   "EU", 1200000,  8,  "Omnivore","LC",'03OCT2023'd,'01JAN2023'd

    from sashelp.class(obs=1)

    ;

quit;

Proc print data=work.animals;

run;

OUTPUT:

Obs	Species	Continent	Population	Lifespan	Diet	Conservation_Status	Last_Survey_Date	Population_Estimate_Date
1	African Elephant	AF	415000	60	Herbivore	EN	22691	22646
2	Bengal Tiger	AS	2500	15	Carnivore	EN	23354	23011
3	Giant Panda	AS	1864	20	Herbivore	VU	22405	22281
4	Blue Whale	OC	10000	90	Carnivore	EN	22086	21915
5	Red Kangaroo	OC	11500000	22	Herbivore	LC	22961	22646
6	Galapagos Tortoise	SA	15000	100	Herbivore	EN	21641	21550
7	Polar Bear	AN	26000	25	Carnivore	VU	22918	22646
8	Grizzly Bear	NA	55000	25	Omnivore	LC	23209	23011
9	Emperor Penguin	AN	595000	20	Carnivore	NT	22497	22281
10	Mountain Gorilla	AF	1005	35	Herbivore	CR	23336	23011
11	Komodo Dragon	OC	3000	30	Carnivore	VU	22715	22646
12	European Hedgehog	EU	1200000	8	Omnivore	LC	23286	23011

3. Add labels and formats 

data work.animals;

  set work.animals;

  label Species = "Species (Common Name)"

        Continent = "Continent Code"

        Population = "Estimated Population (count)"

        Lifespan = "Average Lifespan (years)"

        Diet = "Primary Diet"

        Conservation_Status = "Conservation Status (code)"

        Last_Survey_Date = "Last Survey Date"

        Population_Estimate_Date = "Population Estimate Date";

  format Continent $continentfmt. Diet $dietfmt. Conservation_Status $consfmt.

         Population popcat. Last_Survey_Date date9. Population_Estimate_Date date9.;

run;

proc print data=work.animals (obs=12) label noobs;

  title "WORK.ANIMALS - First 12 Observations";

run;

OUTPUT:

WORK.ANIMALS - First 12 Observations

Species (Common Name)	Continent Code	Estimated Population (count)	Average Lifespan (years)	Primary Diet	Conservation Status (code)	Last Survey Date	Population Estimate Date
African Elephant	Africa	Large (100k-1M)	60	Herbivore	Endangered	15FEB2022	01JAN2022
Bengal Tiger	Asia	Small (1k-10k)	15	Carnivore	Endangered	10DEC2023	01JAN2023
Giant Panda	Asia	Small (1k-10k)	20	Herbivore	Vulnerable	05MAY2021	01JAN2021
Blue Whale	Oceania	Medium (10k-100k)	90	Carnivore	Endangered	20JUN2020	01JAN2020
Red Kangaroo	Oceania	Very Large (>1M)	22	Herbivore	Least Concern	12NOV2022	01JAN2022
Galapagos Tortoise	South America	Medium (10k-100k)	100	Herbivore	Endangered	02APR2019	01JAN2019
Polar Bear	Antarctica	Medium (10k-100k)	25	Carnivore	Vulnerable	30SEP2022	01JAN2022
Grizzly Bear	North America	Medium (10k-100k)	25	Omnivore	Least Concern	18JUL2023	01JAN2023
Emperor Penguin	Antarctica	Large (100k-1M)	20	Carnivore	Near Threatened	05AUG2021	01JAN2021
Mountain Gorilla	Africa	Small (1k-10k)	35	Herbivore	Critically Endangered	22NOV2023	01JAN2023
Komodo Dragon	Oceania	Small (1k-10k)	30	Carnivore	Vulnerable	11MAR2022	01JAN2022
European Hedgehog	Europe	Very Large (>1M)	8	Omnivore	Least Concern	03OCT2023	01JAN2023

4. Frequency distributions 

proc freq data=work.animals;

  tables Continent Conservation_Status Diet Population / nocum nopercent;

  title "Frequencies of Continent, Status, Diet and Population Bins";

run;

OUTPUT:

Frequencies of Continent, Status, Diet and Population Bins

The FREQ Procedure

Continent Code
Continent	Frequency
Africa	2
Antarctica	2
Asia	2
Europe	1
North America	1
Oceania	3
South America	1

Conservation Status (code)
Conservation_Status	Frequency
Critically Endangered	1
Endangered	4
Least Concern	3
Near Threatened	1
Vulnerable	3

Primary Diet
Diet	Frequency
Carnivore	5
Herbivore	5
Omnivore	2

Estimated Population (count)
Population	Frequency
Small (1k-10k)	4
Medium (10k-100k)	4
Large (100k-1M)	2
Very Large (>1M)	2

5. Numeric summaries 

proc means data=work.animals n mean std min max median maxdec=1;

  var Population Lifespan;

  title "Numeric Summary: Population & Lifespan";

run;

OUTPUT:

Numeric Summary: Population & Lifespan

The MEANS Procedure

Variable	Label	N	Mean	Std Dev	Minimum	Maximum	Median
Population Lifespan	Estimated Population (count) Average Lifespan (years)	12 12	1152030.8 37.5	3279331.9 29.8	1005.0 8.0	11500000.0 100.0	20500.0 25.0

6. Univariate diagnostics for Population 

proc univariate data=work.animals;

  var Population;

  histogram Population / midpoints=0 to 12000000 by 1000000;

  inset mean std min max / position=ne;

  title "Population Distribution (Univariate)";

run;

OUTPUT:

Population Distribution (Univariate)

The UNIVARIATE Procedure

Variable: Population (Estimated Population (count))

Moments
N	12	Sum Weights	12
Mean	1152030.75	Sum Observations	13824369
Std Deviation	3279331.94	Variance	1.0754E13
Skewness	3.38941694	Kurtosis	11.6086323
Uncorrected SS	1.3422E14	Corrected SS	1.18294E14
Coeff Variation	284.656632	Std Error Mean	946661.589

Basic Statistical Measures
Location		Variability
Mean	1152031	Std Deviation	3279332
Median	20500	Variance	1.0754E13
Mode	.	Range	11498995
		Interquartile Range	502250

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	1.21694	Pr > |t|	0.2491
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	11500000
99%	11500000
95%	11500000
90%	1200000
75% Q3	505000
50% Median	20500
25% Q1	2750
10%	1864
5%	1005
1%	1005
0% Min	1005

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
1005	10	55000	8
1864	3	415000	1
2500	2	595000	9
3000	11	1200000	12
10000	4	11500000	5

Population Distribution (Univariate)

The UNIVARIATE Procedure

7. Tabulate cross-tab summary by Continent and Conservation_Status 

proc tabulate data=work.animals format=8.0;

  class Continent Conservation_Status;

  var Population;

  table Continent all,

        Conservation_Status * (n='Count' colpctn='Pct within Continent') / box='Continent by Conservation Status';

  title "Tabulation: Continent x Conservation Status";

run;

OUTPUT:

Tabulation: Continent x Conservation Status

Continent by Conservation Status	Conservation Status (code)
	Critically Endangered		Endangered		Least Concern		Near Threatened		Vulnerable
	Count	Pct within Continent	Count	Pct within Continent	Count	Pct within Continent	Count	Pct within Continent	Count	Pct within Continent
Continent Code	1	100	1	25	.	.	.	.	.	.
Africa
Antarctica	.	.	.	.	.	.	1	100	1	33
Asia	.	.	1	25	.	.	.	.	1	33
Europe	.	.	.	.	1	33	.	.	.	.
North America	.	.	.	.	1	33	.	.	.	.
Oceania	.	.	1	25	1	33	.	.	1	33
South America	.	.	1	25	.	.	.	.	.	.
All	1	100	4	100	3	100	1	100	3	100

8. Generate a basic chart using PROC GCHART (bar chart) 

goptions reset=all device=png htext=1.2;

proc gchart data=work.animals;

  vbar Conservation_Status / discrete sumvar=Population subgroup=Continent

       raxis=axis1 maxis=axis2

       coutline=black

       midpoints='LC' 'NT' 'VU' 'EN' 'CR' 'EW' 'EX';

  title "Total Population by Conservation Status (bars) with Continent subgrouping";

run;

quit;

OUTPUT:

9. Macro: create conservation summary dataset and print + chart 

%macro conservation_report(outds=work.cons_summary, chart=YES, minpop=0);

  /* Aggregate counts and population sums by status and continent */

  proc sql;

    create table &outds as

    select Conservation_Status,

           put(Conservation_Status,$consfmt.) as Status_Label,

           Continent,

           put(Continent,$continentfmt.) as Continent_Label,

           count(*) as Species_Count,

           sum(Population) as Total_Population,

           mean(Lifespan) as Mean_Lifespan

    from work.animals

    where Population >= &minpop

    group by Conservation_Status, Continent

    order by Conservation_Status, Continent;

  quit;

  title "Conservation Summary (Population >= &minpop)";

  proc print data=&outds noobs label;

    var Conservation_Status Status_Label Continent_Label Species_Count Total_Population Mean_Lifespan;

  run;

  %if %upcase(&chart)=YES %then %do;

    /* simple pie of species counts by conservation status */

    proc gchart data=&outds;

      pie Conservation_Status / sumvar=Species_Count

          other=0

          percent=outside

          value=none;

      title "Species Count by Conservation Status (Pie)";

    run;

    quit;

  %end;

%mend conservation_report;

%conservation_report(outds=work.cons_summary_all, chart=YES, minpop=0);

OUTPUT:

Conservation Summary (Population >= 0)

Conservation Status (code)	Status_Label	Continent_Label	Species_Count	Total_Population	Mean_Lifespan
Critically Endangered	Critically Endangered	Africa	1	1005	35
Endangered	Endangered	Africa	1	415000	60
Endangered	Endangered	Asia	1	2500	15
Endangered	Endangered	Oceania	1	10000	90
Endangered	Endangered	South America	1	15000	100
Least Concern	Least Concern	Europe	1	1200000	8
Least Concern	Least Concern	North America	1	55000	25
Least Concern	Least Concern	Oceania	1	11500000	22
Near Threatened	Near Threatened	Antarctica	1	595000	20
Vulnerable	Vulnerable	Antarctica	1	26000	25
Vulnerable	Vulnerable	Asia	1	1864	20
Vulnerable	Vulnerable	Oceania	1	3000	30

---

10. Macro: Create population bins and produce summary report 

%macro population_binning(in=work.animals, out=work.animals_binned);

  /* create a dataset with population bin labels using the popcat format */

  data &out;

    set &in;

    Population_Category = put(Population, popcat.);

    format Population_Category $30.;

  run;

  proc freq data=&out;

    tables Population_Category / nocum nopercent;

    title "Population Categories (based on popcat format)";

  run;

  proc means data=&out n mean median min max;

    class Population_Category;

    var Population Lifespan;

    title "Summary by Population Category";

  run;

%mend population_binning;

%population_binning(in=work.animals, out=work.animals_binned);

OUTPUT:

Population Categories (based on popcat format)

The FREQ Procedure

Population_Category	Frequency
Large (100k-1M)	2
Medium (10k-100k)	4
Small (1k-10k)	4
Very Large (>1M)	2

Summary by Population Category

The MEANS Procedure

Population_Category	N Obs	Variable	Label	N	Mean	Median	Minimum	Maximum
Large (100k-1M)	2	Population Lifespan	Estimated Population (count) Average Lifespan (years)	2 2	505000.00 40.0000000	505000.00 40.0000000	415000.00 20.0000000	595000.00 60.0000000
Medium (10k-100k)	4	Population Lifespan	Estimated Population (count) Average Lifespan (years)	4 4	26500.00 60.0000000	20500.00 57.5000000	10000.00 25.0000000	55000.00 100.0000000
Small (1k-10k)	4	Population Lifespan	Estimated Population (count) Average Lifespan (years)	4 4	2092.25 25.0000000	2182.00 25.0000000	1005.00 15.0000000	3000.00 35.0000000
Very Large (>1M)	2	Population Lifespan	Estimated Population (count) Average Lifespan (years)	2 2	6350000.00 15.0000000	6350000.00 15.0000000	1200000.00 8.0000000	11500000.00 22.0000000

11.Using PROC REPORT for a clean summary table 

proc report data=work.cons_summary_all nowd;

  columns Status_Label Continent_Label Species_Count Total_Population Mean_Lifespan;

  define Status_Label / group 'Conservation Status';

  define Continent_Label / group 'Continent';

  define Species_Count / analysis 'No. Species';

  define Total_Population / analysis sum format=comma12. 'Total Population';

  define Mean_Lifespan / analysis mean format=6.1 'Avg Lifespan (yrs)';

  title "Formatted Report: Conservation Summary";

run;

OUTPUT:

Formatted Report: Conservation Summary

Conservation Status	Continent	No. Species	Total Population	Avg Lifespan (yrs)
Critically Endangered	Africa	1	1,005	35.0
Endangered	Africa	1	415,000	60.0
	Asia	1	2,500	15.0
	Oceania	1	10,000	90.0
	South America	1	15,000	100.0
Least Concern	Europe	1	1,200,000	8.0
	North America	1	55,000	25.0
	Oceania	1	11,500,000	22.0
Near Threatened	Antarctica	1	595,000	20.0
Vulnerable	Antarctica	1	26,000	25.0
	Asia	1	1,864	20.0
	Oceania	1	3,000	30.0

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