Sunday, 23 March 2025

135.A NEW EXAMPLE : DO | SELECT | PROC SORT | PROC MEANS | PROC FREQ | PROC REPORT | PROC SQL | PROC SGPLOT | PROC TABULATE | PROC EXPORT | PROC UNIVARIATE | PROC GCHART | PROC FORMAT | PROC TRANSPOSE | PROC PRINT | PROC CORR | PROC REG

A NEW EXAMPLE : DO | SELECT | PROC SORT | PROC MEANS | PROC FREQ | PROC REPORT | PROC SQL | PROC SGPLOT | PROC TABULATE | PROC EXPORT | PROC UNIVARIATE | PROC GCHART | PROC FORMAT | PROC TRANSPOSE | PROC PRINT | PROC CORR | PROC REG


 /* Step 1: Creating a Pharmaceutical Sales Dataset */

DATA PharmaSales;

    LENGTH Region $10 Product $20 SalesRep $15;

    FORMAT SaleDate DATE9. Revenue DOLLAR10.2;

    DO i = 1 TO 5000;

        /* Assign random values for different attributes */

        SELECT (CEIL(RAND("UNIFORM") * 4));

            WHEN (1) Region = 'North';

            WHEN (2) Region = 'South';

            WHEN (3) Region = 'East';

            OTHERWISE Region = 'West';

        END;


        SELECT (CEIL(RAND("UNIFORM") * 5));

            WHEN (1) Product = 'Painkiller';

            WHEN (2) Product = 'Antibiotic';

            WHEN (3) Product = 'Antacid';

            WHEN (4) Product = 'Antiviral';

            OTHERWISE Product = 'Antifungal';

        END;


        SalesRep = CAT('Rep', PUT(CEIL(RAND("UNIFORM") * 50), 2.));

        SaleDate = INTNX('DAY', '01JAN2024'D, FLOOR(RAND("UNIFORM") * 90));

        UnitsSold = CEIL(RAND("UNIFORM") * 100);

        PricePerUnit = ROUND(50 + RAND("UNIFORM") * 200, 0.01);

        Revenue = UnitsSold * PricePerUnit;

        OUTPUT;

    END;

    DROP i;

RUN;

PROC PRINT DATA=PharmaSales;

RUN;


Output:

Obs Region Product SalesRep SaleDate Revenue UnitsSold PricePerUnit
1 East Antiviral Rep13 15FEB2024 $4,083.20 20 204.16
2 South Painkiller Rep41 28MAR2024 $2,705.81 37 73.13
3 North Painkiller Rep45 17MAR2024 $8,454.60 63 134.20
4 East Painkiller Rep50 06MAR2024 $14,518.14 78 186.13
5 East Antiviral Rep25 20FEB2024 $13,198.08 96 137.48
6 North Painkiller Rep42 27JAN2024 $999.54 9 111.06
7 East Antifungal Rep17 17JAN2024 $1,665.93 21 79.33
8 East Antacid Rep36 19JAN2024 $9,807.55 73 134.35
9 West Antibiotic Rep 1 30JAN2024 $7,102.50 75 94.70
10 West Antacid Rep46 28MAR2024 $2,331.34 22 105.97
11 South Antifungal Rep31 21JAN2024 $3,659.66 41 89.26
12 North Antibiotic Rep48 12JAN2024 $10,140.48 72 140.84
13 South Antifungal Rep40 16FEB2024 $18,027.96 76 237.21
14 North Painkiller Rep44 14FEB2024 $10,292.68 58 177.46
15 West Antiviral Rep31 23FEB2024 $6,316.18 34 185.77


/* Step 2: Sorting Data */

PROC SORT DATA=PharmaSales;

    BY Region Product SaleDate;

RUN;

PROC PRINT DATA=PharmaSales;

RUN;


Output:

Obs Region Product SalesRep SaleDate Revenue UnitsSold PricePerUnit
1 East Antacid Rep 4 01JAN2024 $12,426.00 76 163.50
2 East Antacid Rep36 01JAN2024 $16,466.10 70 235.23
3 East Antacid Rep41 02JAN2024 $2,375.67 11 215.97
4 East Antacid Rep31 02JAN2024 $11,127.84 48 231.83
5 East Antacid Rep15 02JAN2024 $9,700.56 54 179.64
6 East Antacid Rep23 03JAN2024 $8,348.54 46 181.49
7 East Antacid Rep22 03JAN2024 $4,703.00 50 94.06
8 East Antacid Rep14 03JAN2024 $377.92 2 188.96
9 East Antacid Rep15 03JAN2024 $16,551.29 79 209.51
10 East Antacid Rep 1 03JAN2024 $18,342.72 88 208.44
11 East Antacid Rep17 03JAN2024 $2,902.50 45 64.50
12 East Antacid Rep 3 03JAN2024 $4,250.55 43 98.85
13 East Antacid Rep19 04JAN2024 $16,507.92 78 211.64
14 East Antacid Rep13 04JAN2024 $2,138.15 35 61.09
15 East Antacid Rep27 05JAN2024 $5,492.19 51 107.69


/* Step 3: Descriptive Statistics */

PROC MEANS DATA=PharmaSales MEAN MEDIAN MIN MAX STD;

    CLASS Region;

    VAR Revenue UnitsSold;

RUN;


Output:

                                                                                  The MEANS Procedure

Region N Obs Variable Mean Median Minimum Maximum Std Dev
East 1236
Revenue
UnitsSold
7714.29
50.6577670
6573.19
51.0000000
59.1900000
1.0000000
24523.00
100.0000000
5451.60
28.1749054
North 1242
Revenue
UnitsSold
7432.48
50.3381643
6440.83
49.0000000
54.1900000
1.0000000
23360.51
100.0000000
5340.63
29.0660418
South 1282
Revenue
UnitsSold
7750.05
51.7948518
6654.72
52.0000000
59.0400000
1.0000000
24088.40
100.0000000
5376.70
28.1638321
West 1240
Revenue
UnitsSold
7232.34
48.2056452
6009.43
48.0000000
52.6000000
1.0000000
24444.00
100.0000000
5363.35
28.8480834


/* Step 4: Frequency Analysis */

PROC FREQ DATA=PharmaSales;

    TABLES Region Product SalesRep / NOCUM;

RUN;


Output:

                                                                The FREQ Procedure

Region Frequency Percent
East 1236 24.72
North 1242 24.84
South 1282 25.64
West 1240 24.80


Product Frequency Percent
Antacid 1059 21.18
Antibiotic 916 18.32
Antifungal 979 19.58
Antiviral 1014 20.28
Painkiller 1032 20.64


SalesRep Frequency Percent
Rep 1 97 1.94
Rep 2 102 2.04
Rep 3 108 2.16
Rep 4 96 1.92
Rep 5 87 1.74
Rep 6 99 1.98
Rep 7 113 2.26
Rep 8 92 1.84
Rep 9 89 1.78
Rep10 104 2.08


/* Step 5: Creating a Summary Report */

PROC REPORT DATA=PharmaSales NOWD;

    COLUMN Region Product UnitsSold Revenue;

    DEFINE Region / GROUP;

    DEFINE Product / GROUP;

    DEFINE UnitsSold / ANALYSIS SUM FORMAT=COMMA10.;

    DEFINE Revenue / ANALYSIS SUM FORMAT=DOLLAR12.2;

RUN;


Output:

Region Product UnitsSold Revenue
East Antacid 12,921 $2058392.86
  Antibiotic 11,063 $1732170.26
  Antifungal 12,821 $1884646.04
  Antiviral 13,939 $2090965.07
  Painkiller 11,869 $1768682.57
North Antacid 12,318 $1818173.25
  Antibiotic 11,288 $1684153.30
  Antifungal 12,831 $1838029.24
  Antiviral 12,201 $1858553.86
  Painkiller 13,882 $2032229.38
South Antacid 14,209 $2169839.75
  Antibiotic 11,466 $1746899.04
  Antifungal 13,079 $1944976.91
  Antiviral 13,178 $2023474.02
  Painkiller 14,469 $2050371.03
West Antacid 13,034 $1981865.19
  Antibiotic 11,341 $1771929.76
  Antifungal 11,666 $1752772.69
  Antiviral 11,862 $1742710.39
  Painkiller 11,872 $1718821.30


/* Step 6: Using SQL to Aggregate Data */

PROC SQL;

    CREATE TABLE SalesSummary AS

    SELECT Region, Product, SUM(UnitsSold) AS TotalUnits, SUM(Revenue) AS TotalRevenue

    FROM PharmaSales

    GROUP BY Region, Product;

QUIT;

PROC PRINT DATA=SalesSummary;

RUN;


Output:

Obs Region Product TotalUnits TotalRevenue
1 East Antacid 12921 2058392.86
2 East Antibiotic 11063 1732170.26
3 East Antifungal 12821 1884646.04
4 East Antiviral 13939 2090965.07
5 East Painkiller 11869 1768682.57
6 North Antacid 12318 1818173.25
7 North Antibiotic 11288 1684153.30
8 North Antifungal 12831 1838029.24
9 North Antiviral 12201 1858553.86
10 North Painkiller 13882 2032229.38
11 South Antacid 14209 2169839.75
12 South Antibiotic 11466 1746899.04
13 South Antifungal 13079 1944976.91
14 South Antiviral 13178 2023474.02
15 South Painkiller 14469 2050371.03
16 West Antacid 13034 1981865.19
17 West Antibiotic 11341 1771929.76
18 West Antifungal 11666 1752772.69
19 West Antiviral 11862 1742710.39
20 West Painkiller 11872 1718821.30


/* Step 7: Generating a Graph */

PROC SGPLOT DATA=PharmaSales;

    VBAR Product / RESPONSE=Revenue STAT=SUM DATALABEL;

    TITLE "Total Revenue by Product";

RUN;


Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           2.76 seconds

      cpu time            0.42 seconds


NOTE: Listing image output written to SGPlot1.png.

NOTE: There were 5000 observations read from the data set WORK.PHARMASALES.


/* Step 8: Tabulating Data */

PROC TABULATE DATA=PharmaSales FORMAT=DOLLAR10.2;

    CLASS Region Product;

    VAR Revenue;

    TABLE Region*Product, Revenue*(SUM MEAN);

RUN;


Output:

Revenue
Sum Mean
Region Product 2058392.86 $7,767.52
East Antacid
Antibiotic 1732170.26 $7,767.58
Antifungal 1884646.04 $7,885.55
Antiviral 2090965.07 $7,548.61
Painkiller 1768682.57 $7,623.63
North Antacid 1818173.25 $7,331.34
Antibiotic 1684153.30 $7,620.60
Antifungal 1838029.24 $7,124.14
Antiviral 1858553.86 $7,711.84
Painkiller 2032229.38 $7,416.90
South Antacid 2169839.75 $8,250.34
Antibiotic 1746899.04 $7,595.21
Antifungal 1944976.91 $7,779.91
Antiviral 2023474.02 $7,935.19
Painkiller 2050371.03 $7,219.62
West Antacid 1981865.19 $7,003.06
Antibiotic 1771929.76 $7,322.02
Antifungal 1752772.69 $7,555.05
Antiviral 1742710.39 $7,231.16
Painkiller 1718821.30 $7,102.57


/* Step 9: Exporting Data to a CSV File */

PROC EXPORT DATA=SalesSummary

    OUTFILE="C:\\Users\\YourName\\Documents\\SalesSummary.csv"

    DBMS=CSV REPLACE;

    PUTNAMES=YES;

RUN;


Log:

NOTE: The file"C:\\Users\\YourName\\Documents\\SalesSummary.csv" is:

      Filename=C:\\Users\\YourName\\Documents\\SalesSummary.csv,

      RECFM=V,LRECL=32767,File Size (bytes)=0,

      Last Modified=24 March 2025 08:59:36,

      Create Time=24 March 2025 08:59:36


NOTE: 21 records were written to the  file "C:\\Users\\YourName\\Documents\\SalesSummary.csv".

      The minimum record length was 29.

      The maximum record length was 38.

NOTE: There were 20 observations read from the data set WORK.SALESSUMMARY.

NOTE: DATA statement used (Total process time):

      real time           0.35 seconds

      cpu time            0.06 seconds


NOTE: "C:\\Users\\YourName\\Documents\\SalesSummary.csv" file was successfully
      created.
NOTE: PROCEDURE EXPORT used (Total process time):
      real time           4.31 seconds
      cpu time            0.62 seconds

/* Step 10: Univariate Analysis for Revenue Distribution */

PROC UNIVARIATE DATA=PharmaSales;

    VAR Revenue;

    HISTOGRAM Revenue / NORMAL;

    PROBPLOT Revenue;

RUN;


Output:

                                                  The UNIVARIATE Procedure

                                                        Variable: Revenue

Moments
N 5000 Sum Weights 5000
Mean 7533.93118 Sum Observations 37669655.9
Std Deviation 5385.68296 Variance 29005580.9
Skewness 0.75511335 Kurtosis -0.1421338
Uncorrected SS 4.28799E11 Corrected SS 1.44999E11
Coeff Variation 71.4856936 Std Error Mean 76.1650588


The UNIVARIATE Procedure

Fitted Normal Distribution for Revenue

Parameters for Normal Distribution
Parameter Symbol Estimate
Mean Mu 7533.931
Std Dev Sigma 5385.683


/* Step 11: Creating a Pie Chart for Product Sales */

PROC GCHART DATA=PharmaSales;

    PIE Product / SUMVAR=Revenue VALUE=INSIDE SLICE=OUTSIDE;

    TITLE "Revenue Distribution by Product";

RUN;


Log:

NOTE: 37748 bytes written to C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary

      Files\_TD7760_DESKTOP-QFAA4KV_\gchart.png.


/* Step 12: Using PROC FORMAT to Categorize Revenue */

PROC FORMAT;

    VALUE RevenueGrp

        LOW - 5000 = 'Low Revenue'

        5001 - 15000 = 'Medium Revenue'

        15001 - HIGH = 'High Revenue';

RUN;

DATA PharmaSalesFormatted;

    SET PharmaSales;

    RevenueCategory = PUT(Revenue, RevenueGrp.);

RUN;

PROC PRINT DATA=PharmaSalesFormatted;

RUN;


Output:

Obs Region Product SalesRep SaleDate Revenue UnitsSold PricePerUnit RevenueCategory
1 East Antacid Rep 4 01JAN2024 $12,426.00 76 163.50 Medium Revenue
2 East Antacid Rep36 01JAN2024 $16,466.10 70 235.23 High Revenue
3 East Antacid Rep41 02JAN2024 $2,375.67 11 215.97 Low Revenue
4 East Antacid Rep31 02JAN2024 $11,127.84 48 231.83 Medium Revenue
5 East Antacid Rep15 02JAN2024 $9,700.56 54 179.64 Medium Revenue
6 East Antacid Rep23 03JAN2024 $8,348.54 46 181.49 Medium Revenue
7 East Antacid Rep22 03JAN2024 $4,703.00 50 94.06 Low Revenue
8 East Antacid Rep14 03JAN2024 $377.92 2 188.96 Low Revenue
9 East Antacid Rep15 03JAN2024 $16,551.29 79 209.51 High Revenue
10 East Antacid Rep 1 03JAN2024 $18,342.72 88 208.44 High Revenue
11 East Antacid Rep17 03JAN2024 $2,902.50 45 64.50 Low Revenue
12 East Antacid Rep 3 03JAN2024 $4,250.55 43 98.85 Low Revenue
13 East Antacid Rep19 04JAN2024 $16,507.92 78 211.64 High Revenue
14 East Antacid Rep13 04JAN2024 $2,138.15 35 61.09 Low Revenue
15 East Antacid Rep27 05JAN2024 $5,492.19 51 107.69 Medium Revenue


/* Step 13: Creating a Cross-tabulation Report */

PROC TABULATE DATA=PharmaSalesFormatted;

    CLASS Region RevenueCategory;

    VAR Revenue;

    TABLE Region, RevenueCategory*Revenue*(N SUM MEAN);

RUN;


Output:

RevenueCategory
High Revenue Low Revenue Medium Revenue
Revenue Revenue Revenue
N Sum Mean N Sum Mean N Sum Mean
Region 150 2749216.04 18328.11 458 1164505.28 2542.59 628 5621135.48 8950.85
East
North 129 2374670.33 18408.30 495 1243509.53 2512.14 618 5612959.17 9082.46
South 149 2711658.71 18199.05 479 1251274.52 2612.26 654 5972627.52 9132.46
West 135 2407497.95 17833.32 526 1283081.03 2439.32 579 5277520.35 9114.89



/* Step 14: Using PROC TRANSPOSE to Pivot Data */

PROC TRANSPOSE DATA=SalesSummary OUT=SalesTransposed;

    BY Region;

    VAR TotalUnits TotalRevenue;

    ID Product;

RUN;

PROC PRINT DATA=SalesTransposed;

RUN;


Output:

Obs Region _NAME_ Antacid Antibiotic Antifungal Antiviral Painkiller
1 East TotalUnits 12921.00 11063.00 12821.00 13939.00 11869.00
2 East TotalRevenue 2058392.86 1732170.26 1884646.04 2090965.07 1768682.57
3 North TotalUnits 12318.00 11288.00 12831.00 12201.00 13882.00
4 North TotalRevenue 1818173.25 1684153.30 1838029.24 1858553.86 2032229.38
5 South TotalUnits 14209.00 11466.00 13079.00 13178.00 14469.00
6 South TotalRevenue 2169839.75 1746899.04 1944976.91 2023474.02 2050371.03
7 West TotalUnits 13034.00 11341.00 11666.00 11862.00 11872.00
8 West TotalRevenue 1981865.19 1771929.76 1752772.69 1742710.39 1718821.30


/* Step 15: Generating a Line Plot for Revenue Trends */

PROC SGPLOT DATA=PharmaSales;

    SERIES X=SaleDate Y=Revenue / GROUP=Region;

    TITLE "Revenue Trends Over Time";

RUN;


Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           0.90 seconds

      cpu time            0.14 seconds


NOTE: Marker and line antialiasing has been disabled because the threshold has been reached. You can

      set ANTIALIASMAX=5000 in the ODS GRAPHICS statement to restore antialiasing.

NOTE: Marker and line antialiasing has been disabled because the threshold has been reached. You can

      set ANTIALIASMAX=5000 in the ODS GRAPHICS statement to restore antialiasing.

NOTE: Listing image output written to SGPlot3.png.

NOTE: There were 5000 observations read from the data set WORK.PHARMASALES.


/* Step 16: Creating a Macro for Automated Analysis */

%MACRO AnalyzeSales(region);

    PROC MEANS DATA=PharmaSales MEAN MIN MAX;

        WHERE Region = "&region";

        VAR Revenue UnitsSold;

    RUN;

%MEND AnalyzeSales;


%AnalyzeSales(North);

Output:

                                                              The MEANS Procedure

Variable Mean Minimum Maximum
Revenue
UnitsSold
7432.48
50.3381643
54.1900000
1.0000000
23360.51
100.0000000

%AnalyzeSales(South);

Output:

                                                              The MEANS Procedure

Variable Mean Minimum Maximum
Revenue
UnitsSold
7750.05
51.7948518
59.0400000
1.0000000
24088.40
100.0000000


%AnalyzeSales(East);

Output:

                                                              The MEANS Procedure

Variable Mean Minimum Maximum
Revenue
UnitsSold
7714.29
50.6577670
59.1900000
1.0000000
24523.00
100.0000000

%AnalyzeSales(West);

Output:

                                                                The MEANS Procedure

Variable Mean Minimum Maximum
Revenue
UnitsSold
7232.34
48.2056452
52.6000000
1.0000000
24444.00
100.0000000


/* Step 17: Checking for Missing Values */

PROC MEANS DATA=PharmaSales N NMISS;

    VAR Revenue UnitsSold;

RUN;


Output:

                                                                   The MEANS Procedure

Variable N N Miss
Revenue
UnitsSold
5000
5000
0
0


/* Step 18: Creating a Boxplot to Analyze Revenue Distribution */

PROC SGPLOT DATA=PharmaSales;

    VBOX Revenue / CATEGORY=Product;

    TITLE "Revenue Distribution by Product";

RUN;


Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           0.43 seconds

      cpu time            0.04 seconds


NOTE: Listing image output written to SGPlot5.png.

NOTE: There were 5000 observations read from the data set WORK.PHARMASALES.


/* Step 19: Merging Data with External Dataset (Simulated) */

DATA ExternalData;

    INPUT Region $ MarketShare;

    DATALINES;

    North 25

    South 30

    East 20

    West 25

    ;

RUN;


DATA PharmaSalesMerged;

    MERGE PharmaSales (IN=a) ExternalData (IN=b);

    BY Region;

    IF a AND b;

RUN;

Output:

/* Step 20: Final Summary Report */

PROC PRINT DATA=PharmaSalesMerged;

    TITLE "Final Merged Sales Report";

RUN;


Output:

Obs Region Product SalesRep SaleDate Revenue UnitsSold PricePerUnit MarketShare
1 North Antacid Rep17 01JAN2024 $21,668.88 94 230.52 25
2 North Antacid Rep12 01JAN2024 $1,999.80 11 181.80 25
3 North Antacid Rep 6 01JAN2024 $1,918.54 26 73.79 25
4 North Antacid Rep14 02JAN2024 $3,275.89 23 142.43 25
5 North Antacid Rep10 03JAN2024 $12,016.40 88 136.55 25
6 North Antacid Rep27 04JAN2024 $318.34 2 159.17 25
7 North Antacid Rep34 04JAN2024 $4,489.10 22 204.05 25
8 North Antacid Rep11 04JAN2024 $8,442.84 69 122.36 25
9 North Antacid Rep36 04JAN2024 $602.00 5 120.40 25
10 North Antacid Rep38 04JAN2024 $7,311.70 85 86.02 25
11 North Antacid Rep49 05JAN2024 $6,221.76 32 194.43 25
12 North Antacid Rep35 05JAN2024 $13,522.80 60 225.38 25
13 North Antacid Rep11 05JAN2024 $17,137.84 89 192.56 25
14 North Antacid Rep38 06JAN2024 $7,952.49 63 126.23 25
15 North Antacid Rep25 06JAN2024 $10,245.42 54 189.73 25


/* Step 21: Additional Statistical Analysis */

PROC CORR DATA=PharmaSales;

    VAR UnitsSold Revenue;

    TITLE "Correlation Between Units Sold and Revenue";

RUN;


Output: 

                                             Correlation Between Units Sold and Revenue

                                                             The CORR Procedure

2 Variables: UnitsSold Revenue


Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum
UnitsSold 5000 50.26180 28.58422 251309 1.00000 100.00000
Revenue 5000 7534 5386 37669656 52.60000 24523


Pearson Correlation Coefficients, N = 5000
Prob > |r| under H0: Rho=0
  UnitsSold Revenue
UnitsSold
1.00000
 
0.78961
<.0001
Revenue
0.78961
<.0001
1.00000
 


PROC REG DATA=PharmaSales;

    MODEL Revenue = UnitsSold PricePerUnit;

    TITLE "Regression Analysis of Revenue";

RUN;


Output:

                                                  Regression Analysis of Revenue

                                                             The REG Procedure 

                                                              Model: MODEL1

                                                      Dependent Variable: Revenue

Number of Observations Read 5000
Number of Observations Used 5000


Analysis of Variance
Source DF Sum of
Squares		 Mean
Square		 F Value Pr > F
Model 2 1.316928E11 65846375739 24728.0 <.0001
Error 4997 13306147624 2662827    
Corrected Total 4999 1.449989E11      


Root MSE 1631.81715 R-Square 0.9082
Dependent Mean 7533.93118 Adj R-Sq 0.9082
Coeff Var 21.65957    


Parameter Estimates
Variable DF Parameter
Estimate		 Standard
Error		 t Value Pr > |t|
Intercept 1 -7556.41606 76.92294 -98.23 <.0001
UnitsSold 1 150.53167 0.80755 186.41 <.0001
PricePerUnit 1 50.00734 0.40160 124.52 <.0001


/* Step 22: Generating Boxplot for Units Sold by Region */

PROC SGPLOT DATA=PharmaSales;

    VBOX UnitsSold / CATEGORY=Region;

    TITLE "Units Sold Distribution by Region";

RUN;


Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           0.40 seconds

      cpu time            0.04 seconds


NOTE: Listing image output written to SGPlot7.png.

NOTE: There were 5000 observations read from the data set WORK.PHARMASALES.



Note: Here Out Of 5000 Only 15 Observations Are Given.


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