264.TEMPLES OF INDIA DATASET ANALYSIS USING PROC PRINT | PROC CONTENTS | PROC SORT | PROC FREQ | PROC MEANS | PROC SQL | PROC TABULATE | PROC REPORT | PROC TRANSPOSE | PROC FORMAT | PROC RANK | PROC SGPLOT | MACROS

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173.COMPREHENSIVE ANALYSIS OF THE RICEBAG DATASET UTILIZING SAS PROCEDURES: PROC CONTENTS | PROC PRINT | PROC SQL | PROC MEANS | PROC REPORT | PROC SGPLOT | PROC SGPIE | PROC EXPORT | MACROS

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COMPREHENSIVE ANALYSIS OF THE RICEBAG DATASET UTILIZING SAS PROCEDURES: PROC CONTENTS | PROC PRINT | PROC SQL | PROC MEANS | PROC REPORT | PROC SGPLOT | PROC SGPIE | PROC EXPORT | MACROS

/*Creating a unique dataset named ricebag*/

/*Creating the ricebag Dataset*/

data ricebag;

    length BagID $10 RiceType $20 StorageLocation $20;

    input BagID $ RiceType $ Weight Price StorageLocation $;

    datalines;

RB001 Basmati 25 1200 Hyderabad

RB002 SonaMasuri 30 950 Vijayawada

RB003 Basmati 20 1000 Hyderabad

RB004 Jasmine 15 1100 Chennai

RB005 SonaMasuri 25 900 Vijayawada

RB006 Basmati 30 1300 Hyderabad

RB007 Jasmine 20 1150 Chennai

RB008 SonaMasuri 15 850 Vijayawada

RB009 Basmati 25 1250 Hyderabad

RB010 Jasmine 30 1200 Chennai

;

run;

proc print;run;

Output:

Obs BagID RiceType StorageLocation Weight Price
1 RB001 Basmati Hyderabad 25 1200
2 RB002 SonaMasuri Vijayawada 30 950
3 RB003 Basmati Hyderabad 20 1000
4 RB004 Jasmine Chennai 15 1100
5 RB005 SonaMasuri Vijayawada 25 900
6 RB006 Basmati Hyderabad 30 1300
7 RB007 Jasmine Chennai 20 1150
8 RB008 SonaMasuri Vijayawada 15 850
9 RB009 Basmati Hyderabad 25 1250
10 RB010 Jasmine Chennai 30 1200

/*Exploring the Dataset*/

proc contents data=ricebag;

run;

Output:

                                                               The CONTENTS Procedure

Data Set Name WORK.RICEBAG Observations 10
Member Type DATA Variables 5
Engine V9 Indexes 0
Created 14/09/2015 00:03:14 Observation Length 72
Last Modified 14/09/2015 00:03:14 Deleted Observations 0
Protection   Compressed NO
Data Set Type   Sorted NO
Label      
Data Representation WINDOWS_64    
Encoding wlatin1 Western (Windows)    


Engine/Host Dependent Information
Data Set Page Size 65536
Number of Data Set Pages 1
First Data Page 1
Max Obs per Page 908
Obs in First Data Page 10
Number of Data Set Repairs 0
ExtendObsCounter YES
Filename C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD9512_DESKTOP-QFAA4KV_\ricebag.sas7bdat
Release Created 9.0401M2
Host Created X64_8HOME


Alphabetic List of Variables and Attributes
# Variable Type Len
1 BagID Char 10
5 Price Num 8
2 RiceType Char 20
3 StorageLocation Char 20
4 Weight Num 8


proc print data=ricebag;

run;

Output:

Obs BagID RiceType StorageLocation Weight Price
1 RB001 Basmati Hyderabad 25 1200
2 RB002 SonaMasuri Vijayawada 30 950
3 RB003 Basmati Hyderabad 20 1000
4 RB004 Jasmine Chennai 15 1100
5 RB005 SonaMasuri Vijayawada 25 900
6 RB006 Basmati Hyderabad 30 1300
7 RB007 Jasmine Chennai 20 1150
8 RB008 SonaMasuri Vijayawada 15 850
9 RB009 Basmati Hyderabad 25 1250
10 RB010 Jasmine Chennai 30 1200


/*Calculating Average Price by Rice Type*/

proc sql;

    select RiceType, avg(Price) as AvgPrice

    from ricebag

    group by RiceType;

quit;

Output:

RiceType AvgPrice
Basmati 1187.5
Jasmine 1150
SonaMasuri 900

/*Identifying the Most Expensive Rice Bag*/

proc sql;

    select *

    from ricebag

    where Price = (select max(Price) from ricebag);

quit;

Output:

BagID RiceType StorageLocation Weight Price
RB006 Basmati Hyderabad 30 1300

/*Counting Bags per Storage Location*/

proc sql;

    select StorageLocation, count(*) as BagCount

    from ricebag

    group by StorageLocation;

quit;

Output:

StorageLocation BagCount
Chennai 3
Hyderabad 4
Vijayawada 3

/*Calculating Price per Kilogram*/

data ricebag;

    set ricebag;

    PricePerKg = Price / Weight;

run;

proc print;run;

Output:

Obs BagID RiceType StorageLocation Weight Price PricePerKg
1 RB001 Basmati Hyderabad 25 1200 48.0000
2 RB002 SonaMasuri Vijayawada 30 950 31.6667
3 RB003 Basmati Hyderabad 20 1000 50.0000
4 RB004 Jasmine Chennai 15 1100 73.3333
5 RB005 SonaMasuri Vijayawada 25 900 36.0000
6 RB006 Basmati Hyderabad 30 1300 43.3333
7 RB007 Jasmine Chennai 20 1150 57.5000
8 RB008 SonaMasuri Vijayawada 15 850 56.6667
9 RB009 Basmati Hyderabad 25 1250 50.0000
10 RB010 Jasmine Chennai 30 1200 40.0000

/*Categorizing Rice Bags Based on Weight*/

data ricebag;

retain BagID RiceType StorageLocation Weight Price PricePerKg ;

    length WeightCategory $15.;

    set ricebag;

    if Weight < 20 then WeightCategory = 'Light';

    else if Weight <= 25 then WeightCategory = 'Medium';

    else WeightCategory = 'Heavy';

run;

proc print;run;

Output:

Obs BagID RiceType StorageLocation Weight Price PricePerKg WeightCategory
1 RB001 Basmati Hyderabad 25 1200 48.0000 Medium
2 RB002 SonaMasuri Vijayawada 30 950 31.6667 Heavy
3 RB003 Basmati Hyderabad 20 1000 50.0000 Medium
4 RB004 Jasmine Chennai 15 1100 73.3333 Light
5 RB005 SonaMasuri Vijayawada 25 900 36.0000 Medium
6 RB006 Basmati Hyderabad 30 1300 43.3333 Heavy
7 RB007 Jasmine Chennai 20 1150 57.5000 Medium
8 RB008 SonaMasuri Vijayawada 15 850 56.6667 Light
9 RB009 Basmati Hyderabad 25 1250 50.0000 Medium
10 RB010 Jasmine Chennai 30 1200 40.0000 Heavy

/*Utilizing Macros for Dynamic Analysis*/

%macro analyze_rice(rice_type);

    proc sql;

        select *

        from ricebag

        where RiceType = "&rice_type";

    quit;


    proc means data=ricebag noprint;

        where RiceType = "&rice_type";

        var Price Weight;

        output out=stats_&rice_type mean=AvgPrice AvgWeight;

    run;


    proc print data=stats_&rice_type;

        title "Statistics for &rice_type Rice Bags";

    run;

%mend analyze_rice;


%analyze_rice(Basmati);

Output:

BagID RiceType StorageLocation Weight Price PricePerKg WeightCategory
RB001 Basmati Hyderabad 25 1200 48 Medium
RB003 Basmati Hyderabad 20 1000 50 Medium
RB006 Basmati Hyderabad 30 1300 43.33333 Heavy
RB009 Basmati Hyderabad 25 1250 50 Medium

 
                                                       Statistics for Basmati Rice Bags

Obs _TYPE_ _FREQ_ AvgPrice AvgWeight
1 0 4 1187.5 25

%analyze_rice(SonaMasuri);

Output:

BagID RiceType StorageLocation Weight Price PricePerKg WeightCategory
RB002 SonaMasuri Vijayawada 30 950 31.66667 Heavy
RB005 SonaMasuri Vijayawada 25 900 36 Medium
RB008 SonaMasuri Vijayawada 15 850 56.66667 Light


                                                        Statistics for SonaMasuri Rice Bags

Obs _TYPE_ _FREQ_ AvgPrice AvgWeight
1 0 3 900 23.3333

%analyze_rice(Jasmine);

Output:

BagID RiceType StorageLocation Weight Price PricePerKg WeightCategory
RB004 Jasmine Chennai 15 1100 73.33333 Light
RB007 Jasmine Chennai 20 1150 57.5 Medium
RB010 Jasmine Chennai 30 1200 40 Heavy
 

                                                          Statistics for Jasmine Rice Bags

Obs _TYPE_ _FREQ_ AvgPrice AvgWeight
1 0 3 1150 21.6667

/*Creating Separate Datasets for Each Rice Type*/

proc sql noprint;

    select distinct RiceType into :types separated by ' '

    from ricebag;

quit;


%macro split_datasets;

    %let count = %sysfunc(countw(&types));

    %do i = 1 %to &count;

        %let type = %scan(&types, &i);

        data &type;

            set ricebag;

            where RiceType = "&type";

        run;

       proc print;run;

    %end;

%mend split_datasets;


%split_datasets;

Output:

Obs BagID RiceType StorageLocation Weight Price PricePerKg WeightCategory
1 RB001 Basmati Hyderabad 25 1200 48.0000 Medium
2 RB003 Basmati Hyderabad 20 1000 50.0000 Medium
3 RB006 Basmati Hyderabad 30 1300 43.3333 Heavy
4 RB009 Basmati Hyderabad 25 1250 50.0000 Medium

Obs BagID RiceType StorageLocation Weight Price PricePerKg WeightCategory
1 RB004 Jasmine Chennai 15 1100 73.3333 Light
2 RB007 Jasmine Chennai 20 1150 57.5000 Medium
3 RB010 Jasmine Chennai 30 1200 40.0000 Heavy

Obs BagID RiceType StorageLocation Weight Price PricePerKg WeightCategory
1 RB002 SonaMasuri Vijayawada 30 950 31.6667 Heavy
2 RB005 SonaMasuri Vijayawada 25 900 36.0000 Medium
3 RB008 SonaMasuri Vijayawada 15 850 56.6667 Light

/*Bar Chart of Average Price by Rice Type*/

proc sgplot data=ricebag;

    vbar RiceType / response=Price stat=mean;

    title "Average Price by Rice Type";

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           2.34 seconds

      cpu time            0.57 seconds


NOTE: Listing image output written to SGPlot1.png.

NOTE: There were 10 observations read from the data set WORK.RICEBAG.


/*Pie Chart of Rice Bags by Storage Location*/

proc gchart data=ricebag;

    pie StorageLocation / value=inside percent=inside slice=outside;

    title "Distribution of Rice Bags by Storage Location";

run;

quit;

Log:

NOTE: There were 10 observations read from the data set WORK.RICEBAG.

NOTE: PROCEDURE GCHART used (Total process time):

      real time           2.45 seconds

      cpu time            0.67 seconds


/*Generating Reports*/

proc report data=ricebag nowd;

    column RiceType WeightCategory PricePerKg;

    define RiceType / group;

    define WeightCategory / group;

    define PricePerKg / mean;

    title "Summary Report of Rice Bags";

run;

Output:

                                                            Summary Report of Rice Bags

RiceType WeightCategory PricePerKg
Basmati Heavy 43.333333
  Medium 49.333333
Jasmine Heavy 40
  Light 73.333333
  Medium 57.5
SonaMasuri Heavy 31.666667
  Light 56.666667
  Medium 36

/*Exporting Data*/

proc export data=ricebag

    outfile="C:\Users\YourUsername\Documents\ricebag.csv"

    dbms=csv

    replace;

run;


Find A Mistake and Comment It:

proc sgplot data=ricebag;

    pie StorageLocation / response=BagID stat=count;

    title "Distribution of Rice Bags by Storage Location";

run;



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