65.PROC TRANSPOSE | ID AND VAR STATEMENT

                          PROC TRANSPOSE | ID AND VAR STATEMENT

  

DATA USED IN THIS PROGRAM:

DATA NARROW;

 INPUT PET_OWNER$10. PET$ POPULATION;

 CARDS;

 MR.BLACK  DOG 2

 MR.BLACK  BIRD 1

 MRS.GREEN  FISH 5

 MR.WHITE  CAT 3

 ;

RUN;

PROC PRINT;RUN;

LOG:

NOTE: The data set WORK.NARROW has 4 observations and 3 variables.

NOTE: DATA statement used (Total process time):

      real time           0.01 seconds

      cpu time            0.01 seconds

RESULT:

The SAS System

Obs	PET_OWNER	PET	POPULATION
1	MR.BLACK	DOG	2
2	MR.BLACK	BIRD	1
3	MRS.GREEN	FISH	5
4	MR.WHITE	CAT	3

HERE I WANT TO TRANSPOSE ABOVE SYNTAX..

EG:

PROC TRANSPOSE DATA=NARROW OUT=NARROW_TRAN

                           NAME=COL_TRANSPOSED;

   VAR PET POPULATION;

   ID PET;

RUN;

LOG:

NOTE: Numeric variables in the input data set will be converted to character in the output data set.

NOTE: There were 4 observations read from the data set WORK.NARROW.

NOTE: The data set WORK.NARROW_TRAN has 2 observations and 5 variables.

NOTE: PROCEDURE TRANSPOSE used (Total process time):

      real time           0.01 seconds

      cpu time            0.01 seconds

RESULT:

The SAS System

Obs	COL_TRANSPOSED	DOG	BIRD	FISH	CAT
1	PET	DOG	BIRD	FISH	CAT
2	POPULATION	2	1	5	3

JUST VISIT THE PREVIOUS PROC TRANSPOSE IT WILL BE EASY TO UNDERSTAND AND PRACTICE...

QUESTIONS:

WHAT IS THE DIFFERENCE BETWEEN THIS OUTPUT AND PREVIOUS BLOG OUTPUT?

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64.PROC TRANSPOSE | ID | VAR STATEMENTS

                           PROC TRANSPOSE | ID | VAR STATEMENTS

DATA USED IN THIS PROGRAM:

DATA NARROW;

 INPUT PET_OWNER$10. PET$ POPULATION;

 CARDS;

 MR.BLACK  DOG 2

 MR.BLACK  BIRD 1

 MRS.GREEN  FISH 5

 MR.WHITE  CAT 3

 ;

RUN;

PROC PRINT;RUN;

LOG:

NOTE: The data set WORK.NARROW has 4 observations and 3 variables.

NOTE: DATA statement used (Total process time):

      real time           0.01 seconds

      cpu time            0.01 seconds

RESULT:

The SAS System

Obs	PET_OWNER	PET	POPULATION
1	MR.BLACK	DOG	2
2	MR.BLACK	BIRD	1
3	MRS.GREEN	FISH	5
4	MR.WHITE	CAT	3

HERE I WANT TO TRANSPOSE ABOVE SYNTAX..

EG1:

PROC TRANSPOSE DATA=NARROW OUT=NARROW_TRAN03

                           NAME=COL_TRANSPOSED;

   ID PET;

RUN;

PROC PRINT;RUN;

LOG:

NOTE: There were 4 observations read from the data set WORK.NARROW.

NOTE: The data set WORK.NARROW_TRAN03 has 1 observations and 5 variables.

NOTE: PROCEDURE TRANSPOSE used (Total process time):

      real time           0.10 seconds

      cpu time            0.01 seconds

RESULT:HERE WE GOT NAMES OF ANIMALS COMPARE TO BEFORE BLOG 

                VISIT THAT  BLOG AND KNOW MORE ABOUT SAS TRANSPOSE

The SAS System

Obs	COL_TRANSPOSED	DOG	BIRD	FISH	CAT
1	POPULATION	2	1	5	3

EG2:

PROC TRANSPOSE DATA=NARROW OUT=NARROW_TRAN04

                                                                 NAME=COL_TRANSPOSED;

   VAR PET POPULATION;

RUN;

PROC PRINT;RUN;

LOG:

NOTE: Numeric variables in the input data set will be converted to character in the output data set.

NOTE: There were 4 observations read from the data set WORK.NARROW.

NOTE: The data set WORK.NARROW_TRAN04 has 2 observations and 5 variables.

NOTE: PROCEDURE TRANSPOSE used (Total process time):

      real time           0.07 seconds

      cpu time            0.00 seconds

RESULT:HERE WE GOT NAMES OF ANIMALS AS OBSERVATIONS COMPARE TO ABOVE                       RESULT   AND WE GOT VARIABLES AS COL1-COL4

                VISIT THAT  BLOG AND KNOW MORE ABOUT SAS TRANSPOSE

The SAS System

Obs	COL_TRANSPOSED	COL1	COL2	COL3	COL4
1	PET	DOG	BIRD	FISH	CAT
2	POPULATION	2	1	5	3

QUESTIONS TO YOU :

1.WHAT IS ID STATEMENT DO?

2.WHY WAS PREFIX OPTION NOT NEEDED HERE?

3.TRANSPOSE TRANSPOSES NUMERIC VARS BY DEFAULT, WHY WAS A NUMERIC AND A CHARACTER VARIABLE TRANSPOSED HERE?

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63.PROC TRANSPOSE | PREFIX | NAME OPTION

                          PROC TRANSPOSE | PREFIX | NAME OPTION

DATA USED IN THIS PROGRAM:

DATA NARROW;

 INPUT PET_OWNER$10. PET$ POPULATION;

 CARDS;

 MR.BLACK  DOG 2

 MR.BLACK  BIRD 1

 MRS.GREEN  FISH 5

 MR.WHITE  CAT 3

 ;

RUN;

PROC PRINT;RUN;

LOG:

NOTE: The data set WORK.NARROW has 4 observations and 3 variables.

NOTE: DATA statement used (Total process time):

      real time           0.01 seconds

      cpu time            0.01 seconds

RESULT:

The SAS System

Obs	PET_OWNER	PET	POPULATION
1	MR.BLACK	DOG	2
2	MR.BLACK	BIRD	1
3	MRS.GREEN	FISH	5
4	MR.WHITE	CAT	3

HERE I WANT TO TRANSPOSE THE ABOVE DATA::

EG1:

PROC TRANSPOSE DATA=NARROW OUT=NARROW_TRAN;

RUN;

LOG:

NOTE: There were 4 observations read from the data set WORK.NARROW.

NOTE: The data set WORK.NARROW_TRAN has 1 observations and 5 variables.

NOTE: PROCEDURE TRANSPOSE used (Total process time):

      real time           0.01 seconds

      cpu time            0.00 seconds

RESULT:HERE IT'S SHOWING ONLY POPULATION COUNT..

The SAS System

Obs	_NAME_	COL1	COL2	COL3	COL4
1	POPULATION	2	1	5	3

EG2:IN THIS SYNTAX ADDING ONE MORE OPTION PREFIX..

PROC TRANSPOSE DATA=NARROW OUT=NARROW_TRAN01 PREFIX=PET_COUNT;

RUN;

LOG:

NOTE: There were 4 observations read from the data set WORK.NARROW.

NOTE: The data set WORK.NARROW_TRAN01 has 1 observations and 5 variables.

NOTE: PROCEDURE TRANSPOSE used (Total process time):

      real time           0.01 seconds

      cpu time            0.00 seconds

RESULT:HERE IT'S SHOWING IN THE PLACE OF PREFIX SHOWING NAME = PET_NAME..

The SAS System

Obs	_NAME_	PET_COUNT1	PET_COUNT2	PET_COUNT3	PET_COUNT4
1	POPULATION	2	1	5	3

EG3:HERE WE ARE ADDING NAME OPTION TO GIVE NAME FOR THE VARIABLE..

PROC TRANSPOSE DATA=NARROW OUT=NARROW_TRAN02 

                          NAME=COL_TRANSPOSED PREFIX=PET_COUNT;

RUN;

LOG:

NOTE: There were 4 observations read from the data set WORK.NARROW.

NOTE: The data set WORK.NARROW_TRAN02 has 1 observations and 5 variables.

NOTE: PROCEDURE TRANSPOSE used (Total process time):

      real time           0.01 seconds

      cpu time            0.01 seconds

RESULT:HERE WE GIVEN _NAME_  = COL_TRANSPOSED ..

The SAS System

Obs	COL_TRANSPOSED	PET_COUNT1	PET_COUNT2	PET_COUNT3	PET_COUNT4
1	POPULATION	2	1	5	3

ANSWER THIS QUESTIONS IN COMMENT:

1.WHAT DID THE PREFIX OPTION DO?

2. WHAT DID THE NAME OPTION DO?

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62.ADDING NEW DATA INTO AVAILABLE DATA USING MERGE

         ADDING NEW DATA INTO AVAILABLE DATA USING MERGE 

DATA  WHICH WANTS TO MERGE WITH ALREADY CREATED DATE..IT CONSISTS OF 3 STEPS AND I HAVE ALSO SORTED THE DATA..

1.DATA CLASS1;

 SET SASHELP.CLASS4;

RUN;

LOG:

NOTE: There were 19 observations read from the data set SASHELP.CLASS4.

NOTE: The data set WORK.CLASS1 has 19 observations and 8 variables.

NOTE: DATA statement used (Total process time):

      real time           0.50 seconds

      cpu time            0.04 seconds

2.DATA WORK.CLASS2;

 INPUT  Name$ Sex$ Age Height Weight DOB:DATE9. CLASS GRADE$;

 DATALINES;

 Nancy F 19 69 102 12MAR2002 . A

 Firoz M 20 76 125 15JUN2001 . A

 ;

RUN;

LOG:

NOTE: The data set WORK.CLASS2 has 2 observations and 8 variables.

NOTE: DATA statement used (Total process time):

      real time           0.07 seconds

      cpu time            0.00 seconds

3.DATA SASHELP.CLASS3;

 MERGE CLASS1

       CLASS2;

RUN;

PROC PRINT;RUN;

LOG:

NOTE: There were 19 observations read from the data set WORK.CLASS1.

NOTE: There were 2 observations read from the data set WORK.CLASS2.

NOTE: The data set SASHELP.CLASS3 has 19 observations and 8 variables.

NOTE: DATA statement used (Total process time):

      real time           0.09 seconds

      cpu time            0.01 seconds

RESULT:

The SAS System

Obs	Name	Sex	Age	Height	Weight	DOB	CLASS	GRADE
1	Nancy	F	19	69	102	12MAR2002	.	A
2	Firoz	M	20	76	125	15JUN2001	.	A
3	Barbara	F	13	65.3	98	14DEC2010	8	C
4	Carol	F	14	62.8	102.5	12NOV2009	9	B
5	Henry	M	14	63.5	102.5	14NOV2009	9	C
6	James	M	12	57.3	83	15MAR2011	7	A
7	Jane	F	12	59.8	84.5	17APR2011	7	A
8	Janet	F	15	62.5	112.5	14NOV2009	10	A
9	Jeffrey	M	13	62.5	84	15APR2010	8	B
10	John	M	12	59	99.5	15JUN2011	7	A
11	Joyce	F	11	51.3	50.5	17DEC2012	6	C
12	Judy	F	14	64.3	90	18DEC2010	9	A
13	Louise	F	12	56.3	77	14NOV2011	7	A
14	Mary	F	15	66.5	112	15DEC2009	10	A
15	Philip	M	16	72	150	16APR2008	11	B
16	Robert	M	12	64.8	128	14DEC2011	7	C
17	Ronald	M	15	67	133	12MAR2009	10	A
18	Thomas	M	11	57.5	85	14MAY2012	6	A
19	William	M	15	66.5	112	15FEB2009	10	C

PROC SORT DATA=SASHELP.CLASS3;

 BY NAME;

RUN;

PROC PRINT;RUN;

LOG:

NOTE: There were 19 observations read from the data set SASHELP.CLASS3.

NOTE: The data set SASHELP.CLASS3 has 19 observations and 8 variables.

NOTE: PROCEDURE SORT used (Total process time):

      real time           0.23 seconds

      cpu time            0.03 seconds

RESULT:SORTED DATA

The SAS System

Obs	Name	Sex	Age	Height	Weight	DOB	CLASS	GRADE
1	Barbara	F	13	65.3	98	14DEC2010	8	C
2	Carol	F	14	62.8	102.5	12NOV2009	9	B
3	Firoz	M	20	76	125	15JUN2001	.	A
4	Henry	M	14	63.5	102.5	14NOV2009	9	C
5	James	M	12	57.3	83	15MAR2011	7	A
6	Jane	F	12	59.8	84.5	17APR2011	7	A
7	Janet	F	15	62.5	112.5	14NOV2009	10	A
8	Jeffrey	M	13	62.5	84	15APR2010	8	B
9	John	M	12	59	99.5	15JUN2011	7	A
10	Joyce	F	11	51.3	50.5	17DEC2012	6	C
11	Judy	F	14	64.3	90	18DEC2010	9	A
12	Louise	F	12	56.3	77	14NOV2011	7	A
13	Mary	F	15	66.5	112	15DEC2009	10	A
14	Nancy	F	19	69	102	12MAR2002	.	A
15	Philip	M	16	72	150	16APR2008	11	B
16	Robert	M	12	64.8	128	14DEC2011	7	C
17	Ronald	M	15	67	133	12MAR2009	10	A
18	Thomas	M	11	57.5	85	14MAY2012	6	A
19	William	M	15	66.5	112	15FEB2009	10	C

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61.PROC TABULATE | OUT OPTION

                                        PROC TABULATE | OUT OPTION 

PROC TABULATE OUT OPTION:

PROC TABULATE OUTPUT OPTION PRODUCES DATASETS USING FOLLOWING METHOD:

PROC TABULATE DATA=SAS DATASET OUT=SAS DATASET;

THE OUTPUT VARIABLES ARE:

* BY  VARIABLES

* CLASS VARIABLES

* THE AUTOMATIC VARIABLES _TYPE_ , _PAGE_  AND _TABLE_

* CALCULATED STATISTICS

EG:

PROC TABULATE DATA=PROG1.SALES OUT=SALES;

 CLASS JOB_TITLE GENDER COUNTRY;

 WHERE JOB_TITLE CONTAINS "Rep";

 TABLE COUNTRY;

 TABLE GENDER COUNTRY;

 TABLE JOB_TITLE,GENDER,COUNTRY;

RUN;

PROC PRINT;RUN;

LOG:

NOTE: There were 159 observations read from the data set PROG1.SALES.

      WHERE JOB_TITLE contains 'Rep';

NOTE: The data set WORK.SALES has 31 observations and 7 variables.

NOTE: PROCEDURE TABULATE used (Total process time):

      real time           3.04 seconds

      cpu time            0.40 seconds

NOTE: There were 31 observations read from the data set WORK.SALES.

NOTE: PROCEDURE PRINT used (Total process time):

      real time           0.21 seconds

      cpu time            0.03 seconds

RESULT:

The SAS System

Obs	Job_Title	Gender	Country	_TYPE_	_PAGE_	_TABLE_	N
1			AU	001	1	1	57
2			IN	001	1	1	4
3			UK	001	1	1	5
4			US	001	1	1	93
5		F		010	1	2	67
6		M		010	1	2	92
7			AU	001	1	2	57
8			IN	001	1	2	4
9			UK	001	1	2	5
10			US	001	1	2	93
11	Sales Rep. I	F	AU	111	1	3	8
12	Sales Rep. I	F	UK	111	1	3	1
13	Sales Rep. I	F	US	111	1	3	12
14	Sales Rep. I	M	AU	111	1	3	13
15	Sales Rep. I	M	US	111	1	3	29
16	Sales Rep. II	F	AU	111	2	3	7
17	Sales Rep. II	F	IN	111	2	3	3
18	Sales Rep. II	F	UK	111	2	3	3
19	Sales Rep. II	F	US	111	2	3	11
20	Sales Rep. II	M	AU	111	2	3	8
21	Sales Rep. II	M	US	111	2	3	14
22	Sales Rep. III	F	AU	111	3	3	6
23	Sales Rep. III	F	IN	111	3	3	1
24	Sales Rep. III	F	UK	111	3	3	1
25	Sales Rep. III	F	US	111	3	3	7
26	Sales Rep. III	M	AU	111	3	3	10
27	Sales Rep. III	M	US	111	3	3	9
28	Sales Rep. IV	F	AU	111	4	3	2
29	Sales Rep. IV	F	US	111	4	3	5
30	Sales Rep. IV	M	AU	111	4	3	3
31	Sales Rep. IV	M	US	111	4	3	6

TRY THIS OPTION AND COMMENT ...

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60.PROC TABULATE | MIN MAX STATISTICS

               PROC TABULATE | MIN MAX STATISTICS

PROC FORMAT:

PROC FORMAT;

 VALUE $CTRYFMT  "AU"="AUSTRALIA"

                 "US"="UNITES STATES"

"IN"="INDIA"

"UK"="UNITED KINGDOM";

RUN;

LOG:

NOTE: Format $CTRYFMT has been output.

NOTE: PROCEDURE FORMAT used (Total process time):

      real time           0.09 seconds

      cpu time            0.03 seconds

RESULT:

PROC TABULATE:MINIMUM AND MAXIMUM STATISTICS WITH ODS..

HERE ODS MEANS :OUTPUT DELIVERY  SYSTEM...

WE CAN WRITE ANY DOCUMENTS LIKE PDF,HTML,RTF ETC..

ODS HTML FILE="SALES000.HTML"

PROC TABULATE DATA=PROG1.SALES ;

   CLASS GENDER COUNTRY;

   VAR SALARY;

   TABLE GENDER ALL,COUNTRY*SALARY*(MIN MAX);

   WHERE JOB_TITLE CONTAINS "Rep";

   LABEL SALARY="Annual Salary";

   FORMAT COUNTRY $CTRYFMT.;

   TITLE "SALES REP TABULAR REPORT";

RUN;

ODS _ALL_ CLOSE;

LOG:

NOTE: There were 159 observations read from the data set PROG1.SALES.

      WHERE JOB_TITLE contains 'Rep';

NOTE: PROCEDURE TABULATE used (Total process time):

      real time           0.10 seconds

      cpu time            0.04 seconds

RESULT:

SALES REP TABULAR REPORT

	Country
	AUSTRALIA		INDIA		UNITED KINGDOM		UNITES STATES
	Annual Salary		Annual Salary		Annual Salary		Annual Salary
	Min	Max	Min	Max	Min	Max	Min	Max
Gender	25185.00	30890.00	27475.00	28745.00	26205.00	29525.00	25390.00	32985.00
F
M	25745.00	36605.00	.	.	.	.	22710.00	35990.00
All	25185.00	36605.00	27475.00	28745.00	26205.00	29525.00	22710.00	35990.00

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59.PROC MEANS | CHARTYPE OPTION

                             PROC MEANS | CHARTYPE OPTION 

PROC MEANS DATA=PROG1.SALES  ;

 VAR SALARY;

 CLASS GENDER COUNTRY;

 OUTPUT OUT=WORK.MEANS1

        MIN=MINSALARY MAX=MAXSALARY

        SUM=SUMSALARY MEAN=AVGSALARY;

RUN;

PROC PRINT;RUN;

LOG:

NOTE: Writing HTML Body file: sashtml.htm

NOTE: There were 165 observations read from the data set PROG1.SALES.

NOTE: The data set WORK.MEANS1 has 13 observations and 8 variables.

NOTE: PROCEDURE MEANS used (Total process time):

      real time           3.88 seconds

      cpu time            0.39 seconds

RESULT:WITHOUT  CHARTYPE OPTIONS OBSERVE THE _TYPE_..

The SAS System

Obs	Gender	Country	_TYPE_	_FREQ_	MINSALARY	MAXSALARY	SUMSALARY	AVGSALARY
1			0	165	22710	243190	5141420	31160.12
2		AU	1	59	25185	108255	1790780	30352.20
3		IN	1	4	27475	28745	112235	28058.75
4		UK	1	5	26205	29525	137210	27442.00
5		US	1	97	22710	243190	3101195	31971.08
6	F		2	68	25185	83505	1955865	28762.72
7	M		2	97	22710	243190	3185555	32840.77
8	F	AU	3	23	25185	30890	638730	27770.87
9	F	IN	3	4	27475	28745	112235	28058.75
10	F	UK	3	5	26205	29525	137210	27442.00
11	F	US	3	36	25390	83505	1067690	29658.06
12	M	AU	3	36	25745	108255	1152050	32001.39
13	M	US	3	61	22710	243190	2033505	33336.15

CHARTYPE:SPECIFIES THAT THE  _TYPE_ VARIABLE IN THE OUTPUT DATASET IS A CHARACTER REPRESENTATION OF THE BINARY VALUE OF _TYPE_..

PROC MEANS DATA=PROG1.SALES CHARTYPE ;

 VAR SALARY;

 CLASS GENDER COUNTRY;

 OUTPUT OUT=WORK.MEANS2

        MIN=MINSALARY MAX=MAXSALARY 

        SUM=SUMSALARY MEAN=AVGSALARY;

RUN;

PROC PRINT;RUN; 

LOG:

NOTE: Writing HTML Body file: sashtml.htm

NOTE: There were 165 observations read from the data set PROG1.SALES.

NOTE: The data set WORK.MEANS1 has 13 observations and 8 variables.

NOTE: PROCEDURE MEANS used (Total process time):

      real time           2.57 seconds

      cpu time            0.34 seconds

NOTE: There were 13 observations read from the data set WORK.MEANS1.

NOTE: PROCEDURE PRINT used (Total process time):

      real time           0.15 seconds

      cpu time            0.01 seconds

RESULT:WITH CHARTYPE OPTION OBSERVE THE _TYPE_ VARIABLE..

The SAS System

Obs	Gender	Country	_TYPE_	_FREQ_	MINSALARY	MAXSALARY	SUMSALARY	AVGSALARY
1			00	165	22710	243190	5141420	31160.12
2		AU	01	59	25185	108255	1790780	30352.20
3		IN	01	4	27475	28745	112235	28058.75
4		UK	01	5	26205	29525	137210	27442.00
5		US	01	97	22710	243190	3101195	31971.08
6	F		10	68	25185	83505	1955865	28762.72
7	M		10	97	22710	243190	3185555	32840.77
8	F	AU	11	23	25185	30890	638730	27770.87
9	F	IN	11	4	27475	28745	112235	28058.75
10	F	UK	11	5	26205	29525	137210	27442.00
11	F	US	11	36	25390	83505	1067690	29658.06
12	M	AU	11	36	25745	108255	1152050	32001.39
13	M	US	11	61	22710	243190	2033505	33336.15

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58.PROC MEANS | DESCENDTYPES OPTION

                             PROC MEANS | DESCENDTYPES OPTION

PROC MEANS DATA=PROG1.SALES  ;

 VAR SALARY;

 CLASS GENDER COUNTRY;

 OUTPUT OUT=WORK.MEANS1

        MIN=MINSALARY MAX=MAXSALARY

        SUM=SUMSALARY MEAN=AVGSALARY;

RUN;

PROC PRINT;RUN;

LOG:

NOTE: Writing HTML Body file: sashtml.htm

NOTE: There were 165 observations read from the data set PROG1.SALES.

NOTE: The data set WORK.MEANS1 has 13 observations and 8 variables.

NOTE: PROCEDURE MEANS used (Total process time):

      real time           3.88 seconds

      cpu time            0.39 seconds

RESULT:WITHOUT  DESCENDTYPES OPTION OBSERVE THE _TYPE_..

The SAS System

Obs	Gender	Country	_TYPE_	_FREQ_	MINSALARY	MAXSALARY	SUMSALARY	AVGSALARY
1			0	165	22710	243190	5141420	31160.12
2		AU	1	59	25185	108255	1790780	30352.20
3		IN	1	4	27475	28745	112235	28058.75
4		UK	1	5	26205	29525	137210	27442.00
5		US	1	97	22710	243190	3101195	31971.08
6	F		2	68	25185	83505	1955865	28762.72
7	M		2	97	22710	243190	3185555	32840.77
8	F	AU	3	23	25185	30890	638730	27770.87
9	F	IN	3	4	27475	28745	112235	28058.75
10	F	UK	3	5	26205	29525	137210	27442.00
11	F	US	3	36	25390	83505	1067690	29658.06
12	M	AU	3	36	25745	108255	1152050	32001.39
13	M	US	3	61	22710	243190	2033505	33336.15

 

DESCENDTYPES:ORDERS THE OUTPUT DATASET BY DESCENDING _TYPE_  VALUE..

PROC MEANS DATA=PROG1.SALES DESCENDTYPES ;

 VAR SALARY;

 CLASS GENDER COUNTRY;

 OUTPUT OUT=WORK.MEANS1

        MIN=MINSALARY MAX=MAXSALARY

        SUM=SUMSALARY MEAN=AVGSALARY;

RUN;

PROC PRINT;RUN;

LOG:

NOTE: Writing HTML Body file: sashtml.htm

NOTE: There were 165 observations read from the data set PROG1.SALES.

NOTE: The data set WORK.MEANS1 has 13 observations and 8 variables.

NOTE: PROCEDURE MEANS used (Total process time):

      real time           2.79 seconds

      cpu time            0.28 seconds

NOTE: There were 13 observations read from the data set WORK.MEANS1.

NOTE: PROCEDURE PRINT used (Total process time):

      real time           0.26 seconds

      cpu time            0.01 seconds

RESULT:

The SAS System

Obs	Gender	Country	_TYPE_	_FREQ_	MINSALARY	MAXSALARY	SUMSALARY	AVGSALARY
1	F	AU	3	23	25185	30890	638730	27770.87
2	F	IN	3	4	27475	28745	112235	28058.75
3	F	UK	3	5	26205	29525	137210	27442.00
4	F	US	3	36	25390	83505	1067690	29658.06
5	M	AU	3	36	25745	108255	1152050	32001.39
6	M	US	3	61	22710	243190	2033505	33336.15
7	F		2	68	25185	83505	1955865	28762.72
8	M		2	97	22710	243190	3185555	32840.77
9		AU	1	59	25185	108255	1790780	30352.20
10		IN	1	4	27475	28745	112235	28058.75
11		UK	1	5	26205	29525	137210	27442.00
12		US	1	97	22710	243190	3101195	31971.08
13			0	165	22710	243190	5141420	31160.12

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