114.CLINICAL TRIALS INTERVIEW QUESTIONS IN SAS - 1

         CLINICAL  TRIALS  INTERVIEW QUESTIONS IN SAS - 1 

1.Describe the phases of clinical trials?

 Ans:- These are the following four phases of the clinical trials: 

 Phase 1: Test a new drug or treatment to a small group of people (20-80) to evaluate its safety. 

 Phase 2: The experimental drug or treatment is given to a large group of people (100-300) to see that the drug is effective or not for that treatment.

 Phase 3: The experimental drug or treatment is given to a large group of people (1000-3000) to see its effectiveness, monitor side effects and compare it to commonly used treatments.

 Phase 4: The 4 phase study includes the post marketing studies including the drug’s risk, benefits etc.

 2. Describe the validation procedure? How would you perform the validation for TLG as well as analysis data set? 

 Ans:- Validation procedure is used to check the output of the SAS program generated by the source programmer. In this process validator write the program and generate the output. If this output is same as the output generated by the SAS programmer’s output then the program is considered to be valid. We can perform this validation for TLG by checking the output manually and for analysis data set it can be done using PROC COMPARE.

 3. How would you perform the validation for the listing, which has 400 pages? 

 Ans:- It is not possible to perform the validation for the listing having 400 pages manually. To do this, we convert the listing in data sets by using PROC REPORT and then after that we can compare it by using PROC COMPARE. 

 4. Can you use PROC COMPARE to validate listings? Why?

 Ans:- Yes, we can use PROC COMPARE to validate the listing because if there are many entries (pages) in the listings then it is not possible to check them manually. So in this condition we use PROC COMPARE to validate the listings. 

 5. How would you generate tables, listings and graphs? 

 Ans:- We can generate the listings by using the PROC REPORT. Similarly we can create the tables by using PROC FREQ, PROC MEANS, and PROC TRANSPOSE and PROC REPORT. We would generate graph, using proc Gplot etc.

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113.SET | ASSIGNMENT | UPCASE | LOWCASE | DROP | LENGTH | IF THEN OUTPUT | LIKE

SET | ASSIGNMENT | UPCASE | LOWCASE | DROP | LENGTH | IF THEN OUTPUT | LIKE IN SAS

DATA NEW;

 SET PROG1.SALES;

 NAME=UPCASE(FIRST_NAME||""||LAST_NAME);

 DROP FIRST_NAME LAST_NAME;

RUN;

PROC PRINT;

 VAR Employee_ID NAME Gender Salary; 

RUN;

OUTPUT:

Obs	Employee_ID	NAME	Gender	Salary
1	120167	KIMIKO TILLEY	F	25185
2	120168	SELINA BARCOE	F	25275
3	121101	BURNETTA BUCKNER	F	25390
4	121092	GYNELL PRITT	F	25680
5	120138	SHANI DUCKETT	F	25795
6	121047	KAREN GRZEBIEN	F	25820
7	.	LIBBY LEVI	F	25930
8	121036	TERESA MESLEY	F	25965
9	121051	GLORINA MYERS	F	26025
10	.	PATRICIA CAPRISTO-ABRAMCZYK	F	26080

NOTE: DUE TO OVER SPACE ONLY 10 OBSERVATIONS ARE DISPLAYED HERE..

DATA NEW01;

 LENGTH GENDER $8;

 SET NEW;

 IF EMPLOYEE_ID="" THEN OUTPUT;

RUN;

PROC PRINT;

 VAR Employee_ID NAME Gender Salary ;

RUN;

OUTPUT:

Obs	Employee_ID	NAME	GENDER	Salary
1	.	LIBBY LEVI	F	25930
2	.	PATRICIA CAPRISTO-ABRAMCZYK	F	26080
3	.	JUDY CHANTHARASY	F	26390
4	.	JULIANNA PHAIYAKOUNH	F	26520
5	.	WILLETA CHERNEGA	F	26830
6	.	PERRIOR BATAINEH	F	26930

DATA NEW02;

 SET NEW01;

 WHERE NAME LIKE 'J%';

RUN;

PROC PRINT;RUN;

OUTPUT:

Obs	GENDER	Employee_ID	Salary	Job_Title	Country	Birth_Date	Hire_Date	NAME
1	F	.	26390	Sales Rep. I	AU	5438	12054	JUDY CHANTHARASY
2	F	.	26520	Sales Rep. II	AU	-5519	5114	JULIANNA PHAIYAKOUNH

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112.ASSIGNMENT STATEMENT | DATA STEP | SET STATEMENT

              ASSIGNMENT STATEMENT | DATA STEP | SET STATEMENT

OBSERVE THE ASSIGNMENT STATEMENT WHICH IS PLACED WITH ALREADY AVAILABLE VARIABLE WITH SAME NAME AND DIFFERNT NAME..

DATA NEW;

 SET SASHELP.CLASS;

 GENDER=SEX;

RUN;

PROC PRINT;RUN;

OUTPUT:

Obs	Name	Sex	Age	Height	Weight	GENDER
1		M	12	57.3	83.0	M
2		M	12	59.0	99.5	M
3		F	13	56.5	84.0	F
4		F	15	66.5	112.0	F
5	Alfred	M	14	69.0	112.5	M
6	Alice	F	13	56.5	84.0	F
7	Barbara	F	13	65.3	98.0	F
8	Carol		14	62.8	102.5
9	Carol	F	14	62.8	102.5	F
10	Henry	M	14	63.5	102.5	M
11	James	M	12	57.3	83.0	M
12	Jane	F	12	59.8	84.5	F
13	Janet	F	15	62.5	112.5	F
14	Jeffrey	M	13	62.5	84.0	M
15	John	M	12	59.0	99.5	M
16	Joyce	F	11	51.3	50.5	F
17	Judy		14	64.3	90.0
18	Judy	F	14	64.3	90.0	F
19	Louise	F	12	56.3	77.0	F
20	Mary	F	15	66.5	112.0	F
21	Philip	M	16	72.0	150.0	M
22	Robert	M	12	64.8	128.0	M
23	Ronald	M	15	67.0	133.0	M
24	Thomas	M	11	57.5	85.0	M
25	William	M	15	66.5	112.0	M

DATA NEW1;

 SET SASHELP.CLASS;

 AGE=Age;

RUN;

PROC PRINT;RUN;

OUTPUT:

Obs	Name	Sex	Age	Height	Weight
1		M	12	57.3	83.0
2		M	12	59.0	99.5
3		F	13	56.5	84.0
4		F	15	66.5	112.0
5	Alfred	M	14	69.0	112.5
6	Alice	F	13	56.5	84.0
7	Barbara	F	13	65.3	98.0
8	Carol		14	62.8	102.5
9	Carol	F	14	62.8	102.5
10	Henry	M	14	63.5	102.5
11	James	M	12	57.3	83.0
12	Jane	F	12	59.8	84.5
13	Janet	F	15	62.5	112.5
14	Jeffrey	M	13	62.5	84.0
15	John	M	12	59.0	99.5
16	Joyce	F	11	51.3	50.5
17	Judy		14	64.3	90.0
18	Judy	F	14	64.3	90.0
19	Louise	F	12	56.3	77.0
20	Mary	F	15	66.5	112.0
21	Philip	M	16	72.0	150.0
22	Robert	M	12	64.8	128.0
23	Ronald	M	15	67.0	133.0
24	Thomas	M	11	57.5	85.0
25	William	M	15	66.5	112.0

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111.SAS INTERVIEW QUESTIONS - 3

                 SAS INTERVIEW QUESTIONS - 3

11. Explain how to use PROC SQL for data manipulation and querying?

 In Proc SQL we use 

 Select:Specifies the columns which you want. 

 From:Specifies the table from which you want to retrieve. 

 Where:Filter the data based on conditions.

 Order By:It sorts the data in ascending or descending manner.

 Group By:Group data based on one or columns. 

 Having:Filters Groups based on conditions. 

 Eg: Proc sql; 

 Select column1,column2 

 From dataset 

 Where condition 

 Order by column1

 Exit;

12.How Would You Create A Frequency Table And Calculate Percentages Using SAS?

 By using Proc Freq Statement and Colpctn,Rowpctn options are used to calculate percentages. 

13. How To Use PROC FORMAT To Create Custom Formats?

In Proc Format by using Value Statement we can create custom formats.

14. What are SAS macros and why are they used? 

SAS Macros are used to automate the repetitive task. It can make your work faster by automating the task that requires writing same lines of code every day.

15.Explain the difference between macro variables and data step variables? 

 Macro Variables are Global or Local variables.These are textual strings.It executes before the Data Step.It is used for Code generation,Automation. 

 Data Step Variables are Local to a dataset.These are Numeric or Character.It executes during the Data Step.It is used for Data Manipulation,Calculations. 

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110.PROC CONTENTS | SET | IF THEN OUTPUT STATEMENTS

       PROC CONTENTS | SET | IF THEN OUTPUT STATEMENTS

PROC CONTENTS DATA=SASHELP.CLASS4;

RUN;

OUTPUT:

Alphabetic List of Variables and Attributes
#	Variable	Type	Len	Format	Informat	Label
3	Age	Num	8	BEST18.		Age
7	CLASS	Num	8	BEST15.		CLASS
6	DOB	Num	8	DATE9.		DOB
8	GRADE	Char	11	$11.	$11.	GRADE
4	Height	Num	8	BEST18.		Height
1	Name	Char	14	$14.	$14.	Name
2	Sex	Char	11	$11.	$11.	Sex
5	Weight	Num	8	BEST18.		Weight

DATA NEWCLASS;

 SET SASHELP.CLASS4;

 IF AGE > 13 THEN OUTPUT;

RUN;

PROC PRINT;RUN;

OUTPUT:

Obs	Name	Sex	Age	Height	Weight	DOB	CLASS	GRADE
1	Alfred	M	14	69	112.5	12OCT2009	9	A
2	Carol	F	14	62.8	102.5	12NOV2009	9	B
3	Henry	M	14	63.5	102.5	14NOV2009	9	C
4	Janet	F	15	62.5	112.5	14NOV2009	10	A
5	Judy	F	14	64.3	90	18DEC2010	9	A
6	Mary	F	15	66.5	112	15DEC2009	10	A
7	Philip	M	16	72	150	16APR2008	11	B
8	Ronald	M	15	67	133	12MAR2009	10	A
9	William	M	15	66.5	112	15FEB2009	10	C

DATA NEWCLASS1;

 SET SASHELP.CLASS4;

 IF GRADE IN ('B','C') THEN OUTPUT;

RUN;

PROC PRINT;RUN;

 

OUTPUT:

Obs	Name	Sex	Age	Height	Weight	DOB	CLASS	GRADE
1	Alice	F	13	56.5	84	12MAR2010	8	B
2	Barbara	F	13	65.3	98	14DEC2010	8	C
3	Carol	F	14	62.8	102.5	12NOV2009	9	B
4	Henry	M	14	63.5	102.5	14NOV2009	9	C
5	Jeffrey	M	13	62.5	84	15APR2010	8	B
6	Joyce	F	11	51.3	50.5	17DEC2012	6	C
7	Philip	M	16	72	150	16APR2008	11	B
8	Robert	M	12	64.8	128	14DEC2011	7	C
9	William	M	15	66.5	112	15FEB2009	10	C

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109.SAS INTERVIEW QUESTIONS - 2

                     SAS INTERVIEW QUESTIONS - 2

6. Describe different ways to combine datasets in sas (e.g., merge, set, append). When would you use each?

 Already we know that different ways to combine datasets are 

 Merge : they combine datasets in horizontally using by statement. 

 Set: the dataset concatenates the other dataset in horizontally. 

 Append: the second dataset observations added to original dataset.

When you want to add observations from one dataset to other dataset then use append statement. 

 When you want to combine datasets without matching conditions then use set statement. 

 When you want to combine datasets with matching conditions then use merge statement.

7. How do you handle missing values in sas? Discuss different missing value representations and functions like nmiss, cmiss, and missing. 

 Nmiss: returns the number of missing values in numeric variables. 

 Cmiss: returns the character of missing values in character variables. 

 Missing: returns 1 if the (both numeric and character) values are missing and otherwise 0. 

8. How do you create new variables in sas? Explain the difference between assignment statements and the retain statement.

 By using assignment statement, conditional statement, sum statement we can create new variables.

 In conditional statements we use : if then, if then else, if then else if, if then do, if then delete, if then output. 

 Assignment statement:used to assign values to variables within the datastep.values are typically assigned based on calculations,comparisions or input from the data. 

 Retain statement:used to retain the value of variable from one iteration of the datastep to the next.allows you to carry information across observations within a dataset. 

9. Explain the difference between where clause and subseting if statement in sas?

 Where statement: it is used to subset the observations. it cannot create any new variables. it is processed before the data is read into the pdv then sas filters data based on where condition only reads selected observations into memory. 

 If statement: it is used to subset the observation conditionally. It can also create new variables accordingly. if statement processed after an observation read into the pdv.sas reads all observations into memory and discards those that do not meet the condition.  

10.How would you transpose a dataset in SAS? 

 By using the Proc Transpose statement we can transpose the dataset. 

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108.SAS INTERVIEW QUESTIONS - 1

                      SAS INTERVIEW QUESTIONS - 1

1. What are some commonly used sas procedures for data analysis and reporting? Give examples of when you would use each. 

 Sas procedures for data analysis and reporting: proc means, proc freq, proc summary, proc univariate, proc anova, proc reg, proc print, proc report etc.. 

 I have used for creating statistical and summarizing information and for customized and professional report in sas..

2. How do you read data from different file formats (e.g., csv, txt, excel) into sas?

 By using infile statement and import statement we can read data from different file formats...

3. How do you export sas datasets to different file formats? 

 By using export statement we can export data to different file formats...

4. How do you debug sas code? What are some common debugging techniques?

 Debugging techniques are mprint, mlogic,symbolgen,put .. 

5.How do you handle errors in sas? 

 By Using Syntax Checks Options , Error Handling Statements, System Options Mprint NOTES, Return Codes Like _Error_ _N_ , Sas Debugging Tools Etc… 

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107.SET | WHERE | KEEP | ASSIGNMENT STATEMENT | LIKE

      SET | WHERE | KEEP | ASSIGNMENT STATEMENT | LIKE 

DATA WORK.CUSTOMER;

         SET PROG1.CUSTOMER;

WHERE Customer_FirstName like 'M%';

KEEP CUSTOMER_ID Customer_FirstName Customer_LastName GENDER COUNTRY;

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	Customer_ID	Country	Gender	Customer_FirstName	Customer_LastName
1	13	DE	M	Markus	Sepke
2	20	US	M	Michael	Dineley
3	75	US	M	Mikel	Spetz

DATA WORK.CUSTOMER02;

        SET PROG1.CUSTOMER;

NAME=Customer_FirstName||','||Customer_LastName;

KEEP CUSTOMER_ID Name Name GENDER COUNTRY;

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	Customer_ID	Country	Gender	NAME
1	4	US	M	James ,Kvarniq
2	5	US	F	Sandrina ,Stephano
3	9	DE	F	Cornelia ,Krahl
4	10	US	F	Karen ,Ballinger
5	11	DE	F	Elke ,Wallstab
6	12	US	M	David ,Black
7	13	DE	M	Markus ,Sepke
8	16	DE	M	Ulrich ,Heyde
9	17	US	M	Jimmie ,Evans
10	18	US	M	Tonie ,Asmussen

NOTE: DUE TO LENGTH ISSUE I HAVE KEPT ONLY 10 OBSERVATIONS..

DATA CUSTOMER03;

        SET CUSTOMER02;

WHERE NAME LIKE '%,M%';

KEEP CUSTOMER_ID Name GENDER COUNTRY;

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	Customer_ID	Country	Gender	NAME
1	27	US	F	Cynthia ,Mccluney
2	31	US	F	Cynthia ,Martinez
3	50	DE	M	Gert-Gunter ,Mendler
4	61	DE	M	Carsten ,Maestrini
5	54655	CA	F	Lauren ,Marx
6	70046	CA	M	Tommy ,Mcdonald

DATA CUSTOMER04;

        SET CUSTOMER;

WHERE NAME LIKE 'M%,D%';

KEEP CUSTOMER_ID Name GENDER COUNTRY;

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	Customer_ID	Country	Gender	NAME
1	20	US	M	Michael ,Dineley

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106.SET | PROC SORT | FIRST. | LAST. STATEMENTS

          SET | PROC SORT | FIRST. | LAST. STATEMENTS 

Data Work.Emps;

  Set Prog2.Empsals;

Run;

Proc Print;

Run;

OUTPUT:

Obs	EmpID	Salary	Div
1	E00004	42000	HUMRES
2	E00009	34000	FINACE
3	E00011	27000	FLTOPS
4	E00036	20000	FINACE
5	E00037	19000	FINACE
6	E00048	19000	FLTOPS
7	E00077	27000	APTOPS
8	E00097	20000	APTOPS
9	E00107	31000	FINACE
10	E00123	20000	APTOPS
11	E00155	27000	APTOPS
12	E00171	44000	SALES
13	E00188	37000	HUMRES
14	E00196	43000	APTOPS
15	E00210	31000	APTOPS
16	E00222	250000	SALES
17	E00236	41000	APTOPS
18	E00239	42000	FLTOPS
19	E00259	32000	APTOPS
20	E00260	39000	APTOPS
21	E00262	36000	FLTOPS
22	E00272	22000	FINACE
23	E00290	37000	FINACE
24	E00302	18000	HUMRES
25	E00333	36000	APTOPS
26	E00367	33000	FLTOPS
27	E00372	36000	HUMRES
28	E00379	25000	APTOPS
29	E00388	25000	APTOPS
30	E00402	28000	FLTOPS
31	E00407	61000	FLTOPS
32	E00421	31000	HUMRES
33	E00424	17000	HUMRES
34	E00427	27000	APTOPS
35	E00440	17000	APTOPS
36	E00454	45000	FLTOPS
37	E00463	39000	SALES
38	E00465	27000	FLTOPS
39	E00470	40000	SALES

Proc Sort Data=Prog2.Empsals Out=Salsort;

  By Div;

Run;

Proc Print;

Run;

OUTPUT:

Obs	EmpID	Salary	Div
1	E00077	27000	APTOPS
2	E00097	20000	APTOPS
3	E00123	20000	APTOPS
4	E00155	27000	APTOPS
5	E00196	43000	APTOPS
6	E00210	31000	APTOPS
7	E00236	41000	APTOPS
8	E00259	32000	APTOPS
9	E00260	39000	APTOPS
10	E00333	36000	APTOPS
11	E00379	25000	APTOPS
12	E00388	25000	APTOPS
13	E00427	27000	APTOPS
14	E00440	17000	APTOPS
15	E00009	34000	FINACE
16	E00036	20000	FINACE
17	E00037	19000	FINACE
18	E00107	31000	FINACE
19	E00272	22000	FINACE
20	E00290	37000	FINACE
21	E00011	27000	FLTOPS
22	E00048	19000	FLTOPS
23	E00239	42000	FLTOPS
24	E00262	36000	FLTOPS
25	E00367	33000	FLTOPS
26	E00402	28000	FLTOPS
27	E00407	61000	FLTOPS
28	E00454	45000	FLTOPS
29	E00465	27000	FLTOPS
30	E00004	42000	HUMRES
31	E00188	37000	HUMRES
32	E00302	18000	HUMRES
33	E00372	36000	HUMRES
34	E00421	31000	HUMRES
35	E00424	17000	HUMRES
36	E00171	44000	SALES
37	E00222	250000	SALES
38	E00463	39000	SALES
39	E00470	40000	SALES

Data Work.Divsals;

  Set Work.Salsort;

  By Div;

  If First.Div Then Divsal=0;

  Divsal+Salary;

Run;

Proc Print;

Run;

OUTPUT:

Obs	EmpID	Salary	Div	Divsal
1	E00077	27000	APTOPS	27000
2	E00097	20000	APTOPS	47000
3	E00123	20000	APTOPS	67000
4	E00155	27000	APTOPS	94000
5	E00196	43000	APTOPS	137000
6	E00210	31000	APTOPS	168000
7	E00236	41000	APTOPS	209000
8	E00259	32000	APTOPS	241000
9	E00260	39000	APTOPS	280000
10	E00333	36000	APTOPS	316000
11	E00379	25000	APTOPS	341000
12	E00388	25000	APTOPS	366000
13	E00427	27000	APTOPS	393000
14	E00440	17000	APTOPS	410000
15	E00009	34000	FINACE	34000
16	E00036	20000	FINACE	54000
17	E00037	19000	FINACE	73000
18	E00107	31000	FINACE	104000
19	E00272	22000	FINACE	126000
20	E00290	37000	FINACE	163000
21	E00011	27000	FLTOPS	27000
22	E00048	19000	FLTOPS	46000
23	E00239	42000	FLTOPS	88000
24	E00262	36000	FLTOPS	124000
25	E00367	33000	FLTOPS	157000
26	E00402	28000	FLTOPS	185000
27	E00407	61000	FLTOPS	246000
28	E00454	45000	FLTOPS	291000
29	E00465	27000	FLTOPS	318000
30	E00004	42000	HUMRES	42000
31	E00188	37000	HUMRES	79000
32	E00302	18000	HUMRES	97000
33	E00372	36000	HUMRES	133000
34	E00421	31000	HUMRES	164000
35	E00424	17000	HUMRES	181000
36	E00171	44000	SALES	44000
37	E00222	250000	SALES	294000
38	E00463	39000	SALES	333000
39	E00470	40000	SALES	373000

Data Work.Divsals01;

  Set Work.Salsort;

  By Div;

  If First.Div Then Divsal=0;

  Divsal+Salary;

  If Last.Div;

Run;

Proc Print Noobs;

Run;

OUTPUT:

EmpID	Salary	Div	Divsal
E00440	17000	APTOPS	410000
E00290	37000	FINACE	163000
E00465	27000	FLTOPS	318000
E00424	17000	HUMRES	181000
E00470	40000	SALES	373000

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105.%LET | SET | WHERE | PUT | CALL SYMPUT | TITLE | FOOTNOTE

%LET | SET | WHERE | PUT | CALL SYMPUT | TITLE | FOOTNOTE

Options Mprint;

%Let Crsno=5;

Data Revenue;

 Set Mauto.All End=Final;

 Where Course_Number=&Crsno;

 Total+1;

 If Paid="Y" Then Paidup+1;

 If Final Then Do;

   Put Total= Paidup=;

   If Paidup<Total Then Do;

    Call Symput("Foot","Some Fees Due");

   End;

   Else Do;

    Call Symput("Foot","All Students Paid");

   End;

 End;

Run;

Proc Print Data=Revenue;

  Var Student_Name Student_company Paid;

  Title "Paid Status For Course &Crsno";

  FootNote "&Foot";

Run;

OUTPUT:

Paid Status For Course 5

Obs	Student_Name	Student_Company	Paid
1	Albritton, Mr. Bryan	Special Services	Y
2	Babbitt, Mr. Bill	National Credit Corp.	Y
3	Blayney, Ms. Vivian	Southern Gas Co.	N
4	Chavez, Ms. Louise	US Express Corp.	Y
5	Connick, Ms. Pat	Central Container Corporation	Y
6	Davies, Ms. Leah	International Education Association	Y
7	Davis, Mr. Bruce	Semi;Conductor	N
8	Edwards, Mr. Charles	Gorman Tire Corp.	Y
9	Edwards, Ms. Kathy	Allied Wood Corporation	Y
10	Griffin, Mr. Jim	Housers S&L	Y
11	Hickel, Mr. Douglas	Towncorp Savings of Illinois	N
12	Higgins, Ms. Anne	San Juan Gas and Electric	Y
13	Kiraly, Mr. Bill	Washington International Corp.	Y
14	Lehmeyer, Ms. Jean	K&P Products	N
15	Lochbihler Mr. Mark	K&P Products	Y
16	Micheaux, Mr. John	Emulate Research	Y
17	Montgomery, Mr. Jeff	Bonstell Electronics	N
18	Patterson, Mr. Greg	Park Health Management	Y
19	Peterson, Ms. Julie	Chase Information Technology	N
20	Rubin, Mr. Jimmie	National Immunology Laboratory	N
21	Shipman, Ms. Jan	Southern Edison Co.	N
22	Short, Ms. Nancy	ABC, Inc.	Y
23	Stewart, Ms. Lora	Sandlot Enterprises	N
24	Sukumoljan, Mr. Robert	San Juan Gas and Electric	Y
25	Wang, Mr. David	Log Chemical	Y

Some Fees Due

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104.%LET | %PUT | %EVAL | MPRINT | MLOGIC | SYMBOLGEN | LOG PRINTTO

%LET | %PUT | %EVAL | MPRINT | MLOGIC | SYMBOLGEN | LOG PRINTTO

%LET A=450;

%LET B=550;

%LET K=%EVAL(&A+&B);

%PUT TOTAL IS &K;

LOG:

5    %PUT TOTAL IS &K;

        TOTAL IS 1000

OPTIONS MPRINT MLOGIC SYMBOLGEN;

%LET A=SHIVA;

%LET B=SHANKAR;

%LET C=RAO;

%PUT &A;

%PUT &B;

%PUT &C;

%LET V=A B;

%PUT &&&V &&&&A;

LOG:

7    OPTIONS MPRINT MLOGIC SYMBOLGEN;

8    %LET A=SHIVA;

9    %LET B=SHANKAR;

10   %LET C=RAO;

11   %PUT &A;

SYMBOLGEN:  Macro variable A resolves to SHIVA

SHIVA

12   %PUT &B;

SYMBOLGEN:  Macro variable B resolves to SHANKAR

SHANKAR

13   %PUT &C;

SYMBOLGEN:  Macro variable C resolves to RAO

RAO

14   %LET V=A B;

15   %PUT &&&V &&&&A;

SYMBOLGEN:  && resolves to &.

SYMBOLGEN:  Macro variable V resolves to A B

SYMBOLGEN:  Macro variable A resolves to SHIVA

SYMBOLGEN:  && resolves to &.

SYMBOLGEN:  && resolves to &.

SYMBOLGEN:  && resolves to &.

SYMBOLGEN:  Macro variable A resolves to SHIVA

SHIVA B SHIVA

TO SAVE YOUR LOG IN YOUR FILES IN DESIRED PATH:

PROC PRINTTO LOG="DESIRED PATH";

RUN;

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103.INPUT | ABSOLUTE | LAG | LOG | LOG10 | MOD | DIF OPTIONS

 INPUT | ABSOLUTE | LAG | LOG | LOG10 | MOD | DIF OPTIONS

DATA NEW;

INPUT NUM;

CARDS;

34.12

56.15

89.34

14.32

-32.15

;

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	NUM
1	34.12
2	56.15
3	89.34
4	14.32
5	-32.15

/*/*ABSOLUTE GIVE '-' NUMBERS TO '+' NUMBERS*/*/;

/*/*LAG GETS PREVIOUS VARIABLE */*/

/*/*LOG MEANS LOGARITHM AND LOG10 ALSO*/*

/*/*MOD MEANS DIVISION BY OUR NUMBER/*/*/

/*/*DIF MEANS SUBSTRACTION*/*/;

DATA MATH;

SET NEW;

INT=INT(NUM);

ABS=ABS(NUM);

LAG=LAG(NUM);

LOG=LOG(NUM);

LOG1=LOG10(NUM);

MOD=MOD(NUM,5);

DIF=DIF(NUM);

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	NUM	INT	ABS	LAG	LOG	LOG1	MOD	DIF
1	34.12	34	34.12	.	3.52988	1.53301	4.12	.
2	56.15	56	56.15	34.12	4.02803	1.74935	1.15	22.03
3	89.34	89	89.34	56.15	4.49245	1.95105	4.34	33.19
4	14.32	14	14.32	89.34	2.66166	1.15594	4.32	-75.02
5	-32.15	-32	32.15	14.32	.	.	-2.15	-46.47

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102.ABSOLUTE | SQUAREROOT | EXPONENT | NATURAL OPTIONS

 ABSOLUTE | SQUAREROOT | EXPONENT | NATURAL  OPTIONS

DATA NEW;

INPUT Z;

ABSOLUTE=ABS(Z);

SQUARE=SQRT(ABSOLUTE);

EXPONENT=EXP(ABSOLUTE);

NATURAL=LOG(ABSOLUTE);

F=Z**5;

CARDS;

1

-5

6

8

;

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	Z	ABSOLUTE	SQUARE	EXPONENT	NATURAL	F
1	1	1	1.00000	2.72	0.00000	1
2	-5	5	2.23607	148.41	1.60944	-3125
3	6	6	2.44949	403.43	1.79176	7776
4	8	8	2.82843	2980.96	2.07944	32768

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101.FIND | COMPARE | VERIFY | AMPERSAND & | PROPCASE | SUBSTR | INDEX FUNCTIONS

FIND | COMPARE | VERIFY | AMPERSAND & | PROPCASE | SUBSTR | INDEX FUNCTIONS 

DATA A1;

A='Further, the company emphasized that much Further';

B=FIND(A,'PANY','I');

C=COMPARE(A,'that');

D='Further, the company emphasized that much';

E=FIND(A,'the');

F=FIND(A,'Fur',4);

G=VERIFY(A,D);

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	A	B	C	D	E	F	G
1	Further, the company emphasized that much Further	17	-1	Further, the company emphasized that much	4	43	0

DATA A2;

A='ABCJHDLABC';

B='ABCDKJDF';

C=VERIFY(B,A);

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	A	B	C
1	ABCJHDLABC	ABCDKJDF	5

DATA A3;

INPUT NAME&$ 30.;

CARDS;

Kamireddy jagan reddy  

adithya desh pandey  

ankit patel rathod 

;

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	NAME
1	Kamireddy jagan reddy
2	adithya desh pandey
3	ankit patel rathod

DATA A4;

SET A3;

A=PROPCASE(NAME);

B=SUBSTR(A,1,1);

C=INDEX(A,' ');

D=SUBSTR(A,C);

FNAME=STRIP(B)||'.'||D;

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	NAME	A	B	C	D	FNAME
1	Kamireddy jagan reddy	Kamireddy Jagan Reddy	K	10	Jagan Reddy	K. Jagan Reddy
2	adithya desh pandey	Adithya Desh Pandey	A	8	Desh Pandey	A. Desh Pandey
3	ankit patel rathod	Ankit Patel Rathod	A	6	Patel Rathod	A. Patel Rathod

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100.TRANWRD | INDEX | INDEXW | COMPRESS | COMPBL | STRIP | TRIM | CAT | CATX FUNCTIONS

 TRANWRD | INDEX | INDEXW | COMPRESS | COMPBL | STRIP | TRIM | CAT | CATX FUNCTIONS

DATA A0;

A='Kamireddy jagan reddy';

B='adithya desh pandey';

C='ankit patel rathod';

D=TRANWRD(A,'Kamireddy jagan reddy','K.Jagan Reddy');

E=TRANWRD(B,'adithya desh pandey','A.Desh Pandey');

F=TRANWRD(C,'ankit patel rathod','A.Patel Rathod');

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	A	B	C	D	E	F
1	Kamireddy jagan reddy	adithya desh pandey	ankit patel rathod	K.Jagan Reddy	A.Desh Pandey	A.Patel Rathod

DATA A1;

A='          THIS     IS     A     CALCULATOR     ';

B=INDEX(A,'H');

C=INDEXW(A,'IS');

D=COMPRESS(A,'C');

E=COMPBL(A);

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	A	B	C	D	E
1	THIS IS A CALCULATOR	12	20	THIS IS A ALULATOR	THIS IS A CALCULATOR

DATA A2;

A='     THIS       IS      A      CALCULATOR    ';

F=RIGHT(A);

G=LEFT(A);

H=STRIP(A);

I=TRIM(A);

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	A	F	G	H	I
1	THIS IS A CALCULATOR	THIS IS A CALCULATOR	THIS IS A CALCULATOR	THIS IS A CALCULATOR	THIS IS A CALCULATOR

DATA A3;

A='   THIS  ';

B='    IS  ';

C='   A  CALCULATOR';

D=CAT(A,B,C);

E=CATS(A,B,C);

F=CATT(A,B,C);

G=CATX('/',A,B,C);

RUN;

PROC PRINT;

RUN;

OUTPUT:

Obs	A	B	C	D	E	F	G
1	THIS	IS	A CALCULATOR	THIS IS A CALCULATOR	THISISA CALCULATOR	THIS IS A CALCULATOR	THIS/IS/A CALCULATOR

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