307.INDIAN RIVERS DATA ANALYSIS USING DATA STEP | PROC SQL | PROC MEANS | PROC SORT | PROC UNIVARIATE | PROC SGPLOT AND MACRO AUTOMATION

INDIAN RIVERS DATA ANALYSIS USING DATA STEP | PROC SQL | PROC MEANS | PROC SORT | PROC UNIVARIATE | PROC SGPLOT AND MACRO AUTOMATION

1. CREATING THE DATASET USING THE DATA STEP

options nocenter nodate nonumber;

data work.indian_rivers;

    length River_Name $20 Origin_State $20 End_Location $25;

    infile datalines dsd;

    input River_Name $ Length_KM Origin_State $ End_Location $ No_of_States 

          Annual_Flow_BCM;

    datalines;

Ganga,2525,Uttarakhand,Bay_of_Bengal,11,525

Godavari,1465,Maharashtra,Bay_of_Bengal,7,110

Krishna,1400,Maharashtra,Bay_of_Bengal,5,78

Yamuna,1376,Uttarakhand,Ganga_Confluence,6,93

Brahmaputra,2900,Arunachal_Pradesh,Bay_of_Bengal,4,537

Narmada,1312,Madhya_Pradesh,Arabian_Sea,3,96

Tapti,724,Madhya_Pradesh,Arabian_Sea,3,37

Mahanadi,858,Chhattisgarh,Bay_of_Bengal,5,66

Cauvery,805,Karnataka,Bay_of_Bengal,3,21

Sabarmati,371,Rajasthan,Arabian_Sea,2,12

Beas,470,Himachal_Pradesh,Sutlej_River,2,15

Sutlej,1450,Tibet,Indus_River,4,77

;

run;

proc print data=work.indian_rivers;

    title "INDIAN RIVERS DATASET";

run;

OUTPUT:

INDIAN RIVERS DATASET

Obs	River_Name	Origin_State	End_Location	Length_KM	No_of_States	Annual_Flow_BCM
1	Ganga	Uttarakhand	Bay_of_Bengal	2525	11	525
2	Godavari	Maharashtra	Bay_of_Bengal	1465	7	110
3	Krishna	Maharashtra	Bay_of_Bengal	1400	5	78
4	Yamuna	Uttarakhand	Ganga_Confluence	1376	6	93
5	Brahmaputra	Arunachal_Pradesh	Bay_of_Bengal	2900	4	537
6	Narmada	Madhya_Pradesh	Arabian_Sea	1312	3	96
7	Tapti	Madhya_Pradesh	Arabian_Sea	724	3	37
8	Mahanadi	Chhattisgarh	Bay_of_Bengal	858	5	66
9	Cauvery	Karnataka	Bay_of_Bengal	805	3	21
10	Sabarmati	Rajasthan	Arabian_Sea	371	2	12
11	Beas	Himachal_Pradesh	Sutlej_River	470	2	15
12	Sutlej	Tibet	Indus_River	1450	4	77

2. DESCRIPTIVE STATISTICS USING PROC MEANS

proc means data=work.indian_rivers mean min max maxdec=2;

    var Length_KM No_of_States Annual_Flow_BCM;

    title "SUMMARY STATISTICS FOR NUMERIC VARIABLES";

run;

OUTPUT:

SUMMARY STATISTICS FOR NUMERIC VARIABLES

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Length_KM No_of_States Annual_Flow_BCM	1304.67 4.58 138.92	371.00 2.00 12.00	2900.00 11.00 537.00

3. DATA SORTING USING PROC SORT

proc sort data=work.indian_rivers out=work.rivers_sorted;

    by descending Annual_Flow_BCM Length_KM;

run;

proc print data=work.rivers_sorted;

    title "RIVERS SORTED BY DESCENDING FLOW AND ASCENDING LENGTH";

run;

OUTPUT:

RIVERS SORTED BY DESCENDING FLOW AND ASCENDING LENGTH

Obs	River_Name	Origin_State	End_Location	Length_KM	No_of_States	Annual_Flow_BCM
1	Brahmaputra	Arunachal_Pradesh	Bay_of_Bengal	2900	4	537
2	Ganga	Uttarakhand	Bay_of_Bengal	2525	11	525
3	Godavari	Maharashtra	Bay_of_Bengal	1465	7	110
4	Narmada	Madhya_Pradesh	Arabian_Sea	1312	3	96
5	Yamuna	Uttarakhand	Ganga_Confluence	1376	6	93
6	Krishna	Maharashtra	Bay_of_Bengal	1400	5	78
7	Sutlej	Tibet	Indus_River	1450	4	77
8	Mahanadi	Chhattisgarh	Bay_of_Bengal	858	5	66
9	Tapti	Madhya_Pradesh	Arabian_Sea	724	3	37
10	Cauvery	Karnataka	Bay_of_Bengal	805	3	21
11	Beas	Himachal_Pradesh	Sutlej_River	470	2	15
12	Sabarmati	Rajasthan	Arabian_Sea	371	2	12

4. VISUALIZING RIVER DATA USING PROC SGPLOT

proc sgplot data=work.indian_rivers;

    title "LENGTH vs ANNUAL FLOW OF MAJOR INDIAN RIVERS";

    scatter x=Length_KM y=Annual_Flow_BCM 

                / datalabel=River_Name markerattrs=(symbol=circlefilled);

    xaxis label="River Length (KM)";

    yaxis label="Annual Flow (Billion Cubic Meters)";

run;

OUTPUT:

5. USING PROC SQL FOR DATA MANIPULATION

proc sql;

    title "RIVERS LONGER THAN 1000 KM";

    select River_Name, Length_KM, Origin_State, Annual_Flow_BCM

    from work.indian_rivers

    where Length_KM > 1000

    order by Annual_Flow_BCM desc;

quit;

OUTPUT:

RIVERS LONGER THAN 1000 KM

River_Name	Length_KM	Origin_State	Annual_Flow_BCM
Brahmaputra	2900	Arunachal_Pradesh	537
Ganga	2525	Uttarakhand	525
Godavari	1465	Maharashtra	110
Narmada	1312	Madhya_Pradesh	96
Yamuna	1376	Uttarakhand	93
Krishna	1400	Maharashtra	78
Sutlej	1450	Tibet	77

6. MACRO AUTOMATION FOR REUSABLE ANALYSIS

/*Macro Example: Compute average flow for selected rivers*/

%macro river_flow(rivername);

    proc sql noprint;

        select mean(Annual_Flow_BCM) into :avgflow

        from work.indian_rivers

        where River_Name="&rivername";

    quit;

    %put Average Flow for &rivername = &avgflow Billion Cubic Meters;

%mend;

%river_flow(Ganga);

LOG:

Average Flow for Ganga = 525 Billion Cubic Meters

%river_flow(Godavari);

LOG:

Average Flow for Godavari = 110 Billion Cubic Meters

%river_flow(Narmada);

LOG:

Average Flow for Narmada = 96 Billion Cubic Meters

7. DISTRIBUTION ANALYSIS USING PROC UNIVARIATE

proc univariate data=work.indian_rivers normal;

    var Annual_Flow_BCM;

    title "DISTRIBUTION ANALYSIS OF RIVER FLOW (BCM)";

run;

OUTPUT:

DISTRIBUTION ANALYSIS OF RIVER FLOW (BCM)

The UNIVARIATE Procedure

Variable: Annual_Flow_BCM

Moments
N	12	Sum Weights	12
Mean	138.916667	Sum Observations	1667
Std Deviation	186.092088	Variance	34630.2652
Skewness	1.9118054	Kurtosis	2.29904603
Uncorrected SS	612507	Corrected SS	380932.917
Coeff Variation	133.959511	Std Error Mean	53.7201585

Basic Statistical Measures
Location		Variability
Mean	138.9167	Std Deviation	186.09209
Median	77.5000	Variance	34630
Mode	.	Range	525.00000
		Interquartile Range	74.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	2.585932	Pr > |t|	0.0253
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Tests for Normality
Test	Statistic		p Value
Shapiro-Wilk	W	0.629754	Pr < W	0.0002
Kolmogorov-Smirnov	D	0.395076	Pr > D	<0.0100
Cramer-von Mises	W-Sq	0.382163	Pr > W-Sq	<0.0050
Anderson-Darling	A-Sq	2.042281	Pr > A-Sq	<0.0050

Quantiles (Definition 5)
Level	Quantile
100% Max	537.0
99%	537.0
95%	537.0
90%	525.0
75% Q3	103.0
50% Median	77.5
25% Q1	29.0
10%	15.0
5%	12.0
1%	12.0
0% Min	12.0

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
12	10	93	4
15	11	96	6
21	9	110	2
37	7	525	1
66	8	537	5

8. DERIVING NEW VARIABLES (Flow Efficiency)

data work.river_efficiency;

    set work.indian_rivers;

    Flow_Efficiency = Annual_Flow_BCM / Length_KM;

run;

proc print data=work.river_efficiency;

    title "DERIVED VARIABLE: FLOW EFFICIENCY (BCM PER KM)";

run;

OUTPUT:

DERIVED VARIABLE: FLOW EFFICIENCY (BCM PER KM)

Obs	River_Name	Origin_State	End_Location	Length_KM	No_of_States	Annual_Flow_BCM	Flow_Efficiency
1	Ganga	Uttarakhand	Bay_of_Bengal	2525	11	525	0.20792
2	Godavari	Maharashtra	Bay_of_Bengal	1465	7	110	0.07509
3	Krishna	Maharashtra	Bay_of_Bengal	1400	5	78	0.05571
4	Yamuna	Uttarakhand	Ganga_Confluence	1376	6	93	0.06759
5	Brahmaputra	Arunachal_Pradesh	Bay_of_Bengal	2900	4	537	0.18517
6	Narmada	Madhya_Pradesh	Arabian_Sea	1312	3	96	0.07317
7	Tapti	Madhya_Pradesh	Arabian_Sea	724	3	37	0.05110
8	Mahanadi	Chhattisgarh	Bay_of_Bengal	858	5	66	0.07692
9	Cauvery	Karnataka	Bay_of_Bengal	805	3	21	0.02609
10	Sabarmati	Rajasthan	Arabian_Sea	371	2	12	0.03235
11	Beas	Himachal_Pradesh	Sutlej_River	470	2	15	0.03191
12	Sutlej	Tibet	Indus_River	1450	4	77	0.05310

9. ADVANCED SQL GROUPING (BY TERMINATION LOCATION)

proc sql;

    title "TOTAL FLOW CONTRIBUTION BY END LOCATION";

    select End_Location,

           count(*) as River_Count,

           sum(Annual_Flow_BCM) as Total_Flow_BCM format=8.2

    from work.indian_rivers

    group by End_Location

    order by Total_Flow_BCM desc;

quit;

OUTPUT:

DERIVED VARIABLE: FLOW EFFICIENCY (BCM PER KM)

Obs	River_Name	Origin_State	End_Location	Length_KM	No_of_States	Annual_Flow_BCM	Flow_Efficiency
1	Ganga	Uttarakhand	Bay_of_Bengal	2525	11	525	0.20792
2	Godavari	Maharashtra	Bay_of_Bengal	1465	7	110	0.07509
3	Krishna	Maharashtra	Bay_of_Bengal	1400	5	78	0.05571
4	Yamuna	Uttarakhand	Ganga_Confluence	1376	6	93	0.06759
5	Brahmaputra	Arunachal_Pradesh	Bay_of_Bengal	2900	4	537	0.18517
6	Narmada	Madhya_Pradesh	Arabian_Sea	1312	3	96	0.07317
7	Tapti	Madhya_Pradesh	Arabian_Sea	724	3	37	0.05110
8	Mahanadi	Chhattisgarh	Bay_of_Bengal	858	5	66	0.07692
9	Cauvery	Karnataka	Bay_of_Bengal	805	3	21	0.02609
10	Sabarmati	Rajasthan	Arabian_Sea	371	2	12	0.03235
11	Beas	Himachal_Pradesh	Sutlej_River	470	2	15	0.03191
12	Sutlej	Tibet	Indus_River	1450	4	77	0.05310

10. COMPARATIVE VISUALIZATION BY END LOCATION

proc sgplot data=work.indian_rivers;

    vbar End_Location / response=Annual_Flow_BCM stat=sum datalabel;

    title "TOTAL ANNUAL FLOW CONTRIBUTION BY TERMINATION BASIN";

    yaxis label="Total Flow (BCM)";

    xaxis label="End Location / Basin";

run;

OUTPUT:

11. MACRO FOR AUTOMATED GRAPH GENERATION

%macro plot_numeric(var);

    proc sgplot data=work.indian_rivers;

        vbar River_Name / response=&var datalabel;

        title "BAR CHART OF &var FOR INDIAN RIVERS";

        yaxis label="&var";

    run;

%mend;

%plot_numeric(Length_KM);

OUTPUT:

%plot_numeric(Annual_Flow_BCM);

OUTPUT:

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