338.TRANSPORTATION SYSTEMS PERFORMANCE ANALYSIS USING DATA STEP | PROC SQL | PROC MEANS | PROC SGPLOT | PROC CORR | MACROS | DATE FUNCTIONS (INTCK | INTNX)

TRANSPORTATION SYSTEMS PERFORMANCE ANALYSIS USING DATA STEP | PROC SQL | PROC MEANS | PROC SGPLOT | PROC CORR | MACROS | DATE FUNCTIONS (INTCK | INTNX)

options nocenter;

1.CREATE THE DATASET OF TRANSPORT_SYSTEMS

data transport_systems;

    infile datalines dlm=',' truncover;

    input Transport_Type :$20. Country :$20. Speed Capacity Energy_Consumption Cost

          Safety_Index Start_Date :date9.;

    format Start_Date date9.;

datalines;

Metro,India,80,1500,45,200,85,01JAN2010

Tram,Germany,60,300,25,150,90,01JAN2005

Hyperloop,USA,1000,50,120,5000,95,01JAN2025

Monorail,Japan,90,800,40,400,92,01JAN2012

Bullet_Train,France,320,900,60,1500,93,01JAN2008

Maglev,China,430,1000,70,2500,97,01JAN2016

Cable_Car,Switzerland,25,20,15,80,98,01JAN2000

Ferry,Norway,40,200,100,300,88,01JAN1995

Electric_Bus,UK,55,100,20,120,89,01JAN2018

Aerial_Tram,Austria,22,25,18,75,94,01JAN2003

Suspended_Monorail,Brazil,70,600,35,220,87,01JAN2014

High_Speed_Rail,Spain,300,1100,58,1400,96,01JAN2011

;

run;

proc print data=transport_systems;

run;

OUTPUT:

Obs	Transport_Type	Country	Speed	Capacity	Energy_Consumption	Cost	Safety_Index	Start_Date
1	Metro	India	80	1500	45	200	85	01JAN2010
2	Tram	Germany	60	300	25	150	90	01JAN2005
3	Hyperloop	USA	1000	50	120	5000	95	01JAN2025
4	Monorail	Japan	90	800	40	400	92	01JAN2012
5	Bullet_Train	France	320	900	60	1500	93	01JAN2008
6	Maglev	China	430	1000	70	2500	97	01JAN2016
7	Cable_Car	Switzerland	25	20	15	80	98	01JAN2000
8	Ferry	Norway	40	200	100	300	88	01JAN1995
9	Electric_Bus	UK	55	100	20	120	89	01JAN2018
10	Aerial_Tram	Austria	22	25	18	75	94	01JAN2003
11	Suspended_Monorail	Brazil	70	600	35	220	87	01JAN2014
12	High_Speed_Rail	Spain	300	1100	58	1400	96	01JAN2011

2.MACRO FOR RATING CLASSIFICATION

%macro classify(var=, out=);

    if &var >= 95 then &out = "Excellent";

    else if &var >= 90 then &out = "Very_Good";

    else if &var >= 85 then &out = "Good";

    else if &var >= 80 then &out = "Moderate";

    else &out = "Poor";

%mend classify;

data transport_with_rating;

    set transport_systems;

    %classify(var=Safety_Index, out=Rating);

run;

proc print data=transport_with_rating;

run;

OUTPUT:

Obs	Transport_Type	Country	Speed	Capacity	Energy_Consumption	Cost	Safety_Index	Start_Date	Rating
1	Metro	India	80	1500	45	200	85	01JAN2010	Good
2	Tram	Germany	60	300	25	150	90	01JAN2005	Very_Good
3	Hyperloop	USA	1000	50	120	5000	95	01JAN2025	Excellent
4	Monorail	Japan	90	800	40	400	92	01JAN2012	Very_Good
5	Bullet_Train	France	320	900	60	1500	93	01JAN2008	Very_Good
6	Maglev	China	430	1000	70	2500	97	01JAN2016	Excellent
7	Cable_Car	Switzerland	25	20	15	80	98	01JAN2000	Excellent
8	Ferry	Norway	40	200	100	300	88	01JAN1995	Good
9	Electric_Bus	UK	55	100	20	120	89	01JAN2018	Good
10	Aerial_Tram	Austria	22	25	18	75	94	01JAN2003	Very_Good
11	Suspended_Monorail	Brazil	70	600	35	220	87	01JAN2014	Good
12	High_Speed_Rail	Spain	300	1100	58	1400	96	01JAN2011	Excellent

3.USE PROC SQL TO ADD DATE-BASED DERIVATIONS

proc sql;

    create table transport_enhanced as

    select *,

           intck('year', Start_Date, today()) as Years_Operational,

           intnx('year', Start_Date, 5) as Five_Year_Mark format=date9.

    from transport_with_rating;

quit;

proc print data=transport_enhanced;

run;

OUTPUT:

Obs	Transport_Type	Country	Speed	Capacity	Energy_Consumption	Cost	Safety_Index	Start_Date	Rating	Years_Operational	Five_Year_Mark
1	Metro	India	80	1500	45	200	85	01JAN2010	Good	15	01JAN2015
2	Tram	Germany	60	300	25	150	90	01JAN2005	Very_Good	20	01JAN2010
3	Hyperloop	USA	1000	50	120	5000	95	01JAN2025	Excellent	0	01JAN2030
4	Monorail	Japan	90	800	40	400	92	01JAN2012	Very_Good	13	01JAN2017
5	Bullet_Train	France	320	900	60	1500	93	01JAN2008	Very_Good	17	01JAN2013
6	Maglev	China	430	1000	70	2500	97	01JAN2016	Excellent	9	01JAN2021
7	Cable_Car	Switzerland	25	20	15	80	98	01JAN2000	Excellent	25	01JAN2005
8	Ferry	Norway	40	200	100	300	88	01JAN1995	Good	30	01JAN2000
9	Electric_Bus	UK	55	100	20	120	89	01JAN2018	Good	7	01JAN2023
10	Aerial_Tram	Austria	22	25	18	75	94	01JAN2003	Very_Good	22	01JAN2008
11	Suspended_Monorail	Brazil	70	600	35	220	87	01JAN2014	Good	11	01JAN2019
12	High_Speed_Rail	Spain	300	1100	58	1400	96	01JAN2011	Excellent	14	01JAN2016

4.ADDITIONAL DATE METRICS (INTERVAL TREND ANALYTICS)

data transport_enhanced;

    set transport_enhanced;

    Days_Operational = intck('day', Start_Date, today());

    Quarter_Mark = intnx('qtr', Start_Date, 1);

    format Quarter_Mark date9.;

run;

proc print data=transport_enhanced;

run;

OUTPUT:

Obs	Transport_Type	Country	Speed	Capacity	Energy_Consumption	Cost	Safety_Index	Start_Date	Rating	Years_Operational	Five_Year_Mark	Days_Operational	Quarter_Mark
1	Metro	India	80	1500	45	200	85	01JAN2010	Good	15	01JAN2015	5826	01APR2010
2	Tram	Germany	60	300	25	150	90	01JAN2005	Very_Good	20	01JAN2010	7652	01APR2005
3	Hyperloop	USA	1000	50	120	5000	95	01JAN2025	Excellent	0	01JAN2030	347	01APR2025
4	Monorail	Japan	90	800	40	400	92	01JAN2012	Very_Good	13	01JAN2017	5096	01APR2012
5	Bullet_Train	France	320	900	60	1500	93	01JAN2008	Very_Good	17	01JAN2013	6557	01APR2008
6	Maglev	China	430	1000	70	2500	97	01JAN2016	Excellent	9	01JAN2021	3635	01APR2016
7	Cable_Car	Switzerland	25	20	15	80	98	01JAN2000	Excellent	25	01JAN2005	9479	01APR2000
8	Ferry	Norway	40	200	100	300	88	01JAN1995	Good	30	01JAN2000	11305	01APR1995
9	Electric_Bus	UK	55	100	20	120	89	01JAN2018	Good	7	01JAN2023	2904	01APR2018
10	Aerial_Tram	Austria	22	25	18	75	94	01JAN2003	Very_Good	22	01JAN2008	8383	01APR2003
11	Suspended_Monorail	Brazil	70	600	35	220	87	01JAN2014	Good	11	01JAN2019	4365	01APR2014
12	High_Speed_Rail	Spain	300	1100	58	1400	96	01JAN2011	Excellent	14	01JAN2016	5461	01APR2011

5.CREATING NEW VARIABLE

data logic_speed;

    set transport_enhanced;

    Speed_Growth = Years_Operational / Speed;  

run;

run;

proc print data=logic_speed;

run;

OUTPUT:

Obs	Transport_Type	Country	Speed	Capacity	Energy_Consumption	Cost	Safety_Index	Start_Date	Rating	Years_Operational	Five_Year_Mark	Days_Operational	Quarter_Mark	Speed_Growth
1	Metro	India	80	1500	45	200	85	01JAN2010	Good	15	01JAN2015	5826	01APR2010	0.18750
2	Tram	Germany	60	300	25	150	90	01JAN2005	Very_Good	20	01JAN2010	7652	01APR2005	0.33333
3	Hyperloop	USA	1000	50	120	5000	95	01JAN2025	Excellent	0	01JAN2030	347	01APR2025	0.00000
4	Monorail	Japan	90	800	40	400	92	01JAN2012	Very_Good	13	01JAN2017	5096	01APR2012	0.14444
5	Bullet_Train	France	320	900	60	1500	93	01JAN2008	Very_Good	17	01JAN2013	6557	01APR2008	0.05313
6	Maglev	China	430	1000	70	2500	97	01JAN2016	Excellent	9	01JAN2021	3635	01APR2016	0.02093
7	Cable_Car	Switzerland	25	20	15	80	98	01JAN2000	Excellent	25	01JAN2005	9479	01APR2000	1.00000
8	Ferry	Norway	40	200	100	300	88	01JAN1995	Good	30	01JAN2000	11305	01APR1995	0.75000
9	Electric_Bus	UK	55	100	20	120	89	01JAN2018	Good	7	01JAN2023	2904	01APR2018	0.12727
10	Aerial_Tram	Austria	22	25	18	75	94	01JAN2003	Very_Good	22	01JAN2008	8383	01APR2003	1.00000
11	Suspended_Monorail	Brazil	70	600	35	220	87	01JAN2014	Good	11	01JAN2019	4365	01APR2014	0.15714
12	High_Speed_Rail	Spain	300	1100	58	1400	96	01JAN2011	Excellent	14	01JAN2016	5461	01APR2011	0.04667

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

6.PROC MEANS FOR SUMMARY STATISTICS

proc means data=transport_enhanced mean std min max maxdec=2;

    var Speed Capacity Energy_Consumption Cost Safety_Index Years_Operational;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Std Dev	Minimum	Maximum
Speed Capacity Energy_Consumption Cost Safety_Index Years_Operational	207.67 549.58 50.50 995.42 92.00 15.25	284.21 503.54 33.15 1475.78 4.20 8.25	22.00 20.00 15.00 75.00 85.00 0.00	1000.00 1500.00 120.00 5000.00 98.00 30.00

7.CORRELATION ANALYSIS (PROC CORR)

proc corr data=transport_enhanced plots=matrix;

    var Speed Capacity Energy_Consumption Cost Safety_Index;

run;

OUTPUT:

The CORR Procedure

5 Variables:	Speed Capacity Energy_Consumption Cost Safety_Index

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Speed	12	207.66667	284.21002	2492	22.00000	1000
Capacity	12	549.58333	503.53504	6595	20.00000	1500
Energy_Consumption	12	50.50000	33.14568	606.00000	15.00000	120.00000
Cost	12	995.41667	1476	11945	75.00000	5000
Safety_Index	12	92.00000	4.19957	1104	85.00000	98.00000

Pearson Correlation Coefficients, N = 12 Prob > |r| under H0: Rho=0
	Speed	Capacity	Energy_Consumption	Cost	Safety_Index
Speed	1.00000	-0.00318 0.9922	0.75152 0.0048	0.99552 <.0001	0.41960 0.1745
Capacity	-0.00318 0.9922	1.00000	0.06265 0.8466	-0.00640 0.9843	-0.19690 0.5396
Energy_Consumption	0.75152 0.0048	0.06265 0.8466	1.00000	0.77012 0.0034	0.08360 0.7962
Cost	0.99552 <.0001	-0.00640 0.9843	0.77012 0.0034	1.00000	0.45281 0.1394
Safety_Index	0.41960 0.1745	-0.19690 0.5396	0.08360 0.7962	0.45281 0.1394	1.00000

8.VISUALIZATION USING PROC SGPLOT

proc sgplot data=transport_enhanced;

    scatter x=Speed y=Cost / group=Transport_Type;

    title "Speed vs Cost Across Transportation Systems";

run;

OUTPUT:

proc sgplot data=transport_enhanced;

    vbar Transport_Type / response=Safety_Index datalabel;

    title "Safety Index by Transport Type";

run;

OUTPUT:

9.FORMATS FOR RATING GROUPING

proc format;

    value safetyfmt

        95 - high = 'Excellent'

        90 -< 95 = 'Very Good'

        85 -< 90 = 'Good'

        80 -< 85 = 'Moderate'

        low -< 80 = 'Poor';

run;

LOG:

NOTE: Format SAFETYFMT has been output.

proc print data=transport_enhanced;

    format Safety_Index safetyfmt.;

run;

OUTPUT:

Obs	Transport_Type	Country	Speed	Capacity	Energy_Consumption	Cost	Safety_Index	Start_Date	Rating	Years_Operational	Five_Year_Mark	Days_Operational	Quarter_Mark
1	Metro	India	80	1500	45	200	Good	01JAN2010	Good	15	01JAN2015	5826	01APR2010
2	Tram	Germany	60	300	25	150	Very Good	01JAN2005	Very_Good	20	01JAN2010	7652	01APR2005
3	Hyperloop	USA	1000	50	120	5000	Excellent	01JAN2025	Excellent	0	01JAN2030	347	01APR2025
4	Monorail	Japan	90	800	40	400	Very Good	01JAN2012	Very_Good	13	01JAN2017	5096	01APR2012
5	Bullet_Train	France	320	900	60	1500	Very Good	01JAN2008	Very_Good	17	01JAN2013	6557	01APR2008
6	Maglev	China	430	1000	70	2500	Excellent	01JAN2016	Excellent	9	01JAN2021	3635	01APR2016
7	Cable_Car	Switzerland	25	20	15	80	Excellent	01JAN2000	Excellent	25	01JAN2005	9479	01APR2000
8	Ferry	Norway	40	200	100	300	Good	01JAN1995	Good	30	01JAN2000	11305	01APR1995
9	Electric_Bus	UK	55	100	20	120	Good	01JAN2018	Good	7	01JAN2023	2904	01APR2018
10	Aerial_Tram	Austria	22	25	18	75	Very Good	01JAN2003	Very_Good	22	01JAN2008	8383	01APR2003
11	Suspended_Monorail	Brazil	70	600	35	220	Good	01JAN2014	Good	11	01JAN2019	4365	01APR2014
12	High_Speed_Rail	Spain	300	1100	58	1400	Excellent	01JAN2011	Excellent	14	01JAN2016	5461	01APR2011

10.PROC SQL FOR COST PER CAPACITY ANALYSIS

proc sql;

    create table cost_efficiency as

    select Transport_Type, Country, Cost, Capacity,

           (Cost / Capacity) as Cost_Per_Seat

    from transport_enhanced;

quit;

proc print data=cost_efficiency;

run;

OUTPUT:

Obs	Transport_Type	Country	Cost	Capacity	Cost_Per_Seat
1	Metro	India	200	1500	0.133
2	Tram	Germany	150	300	0.500
3	Hyperloop	USA	5000	50	100.000
4	Monorail	Japan	400	800	0.500
5	Bullet_Train	France	1500	900	1.667
6	Maglev	China	2500	1000	2.500
7	Cable_Car	Switzerland	80	20	4.000
8	Ferry	Norway	300	200	1.500
9	Electric_Bus	UK	120	100	1.200
10	Aerial_Tram	Austria	75	25	3.000
11	Suspended_Monorail	Brazil	220	600	0.367
12	High_Speed_Rail	Spain	1400	1100	1.273

11.ADVANCED DATE FUNCTIONS FOR PROJECT ANALYSIS

data transport_final;

    set transport_enhanced;

    Next_Upgrade = intnx('year', Start_Date, 15);

    Cycles = intck('month', Start_Date, today());

    format Next_Upgrade date9.;

run;

proc print data=transport_final;

run;

OUTPUT:

Obs	Transport_Type	Country	Speed	Capacity	Energy_Consumption	Cost	Safety_Index	Start_Date	Rating	Years_Operational	Five_Year_Mark	Days_Operational	Quarter_Mark	Next_Upgrade	Cycles
1	Metro	India	80	1500	45	200	85	01JAN2010	Good	15	01JAN2015	5826	01APR2010	01JAN2025	191
2	Tram	Germany	60	300	25	150	90	01JAN2005	Very_Good	20	01JAN2010	7652	01APR2005	01JAN2020	251
3	Hyperloop	USA	1000	50	120	5000	95	01JAN2025	Excellent	0	01JAN2030	347	01APR2025	01JAN2040	11
4	Monorail	Japan	90	800	40	400	92	01JAN2012	Very_Good	13	01JAN2017	5096	01APR2012	01JAN2027	167
5	Bullet_Train	France	320	900	60	1500	93	01JAN2008	Very_Good	17	01JAN2013	6557	01APR2008	01JAN2023	215
6	Maglev	China	430	1000	70	2500	97	01JAN2016	Excellent	9	01JAN2021	3635	01APR2016	01JAN2031	119
7	Cable_Car	Switzerland	25	20	15	80	98	01JAN2000	Excellent	25	01JAN2005	9479	01APR2000	01JAN2015	311
8	Ferry	Norway	40	200	100	300	88	01JAN1995	Good	30	01JAN2000	11305	01APR1995	01JAN2010	371
9	Electric_Bus	UK	55	100	20	120	89	01JAN2018	Good	7	01JAN2023	2904	01APR2018	01JAN2033	95
10	Aerial_Tram	Austria	22	25	18	75	94	01JAN2003	Very_Good	22	01JAN2008	8383	01APR2003	01JAN2018	275
11	Suspended_Monorail	Brazil	70	600	35	220	87	01JAN2014	Good	11	01JAN2019	4365	01APR2014	01JAN2029	143
12	High_Speed_Rail	Spain	300	1100	58	1400	96	01JAN2011	Excellent	14	01JAN2016	5461	01APR2011	01JAN2026	179

YESTERDAY INVALID CODE ANSWER

10.BASIC DESCRIPTIVE STATISTICS

proc mean data=space_inventions;  

    var Year;

run;

proc means data=space_inventions;  

    var Year;

run;

PROC MEAN does not exist in SAS. The correct procedure is PROC MEANS.

OUTPUT:

The MEANS Procedure

Analysis Variable : Year
N	Mean	Std Dev	Minimum	Maximum
12	1990.08	20.1560202	1957.00	2015.00

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

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