SAS Environment and Concepts of Libraries
SAS Training
Statistical Analysis System (SAS) Is a set of solutions for enterprise-wide business users for performing:
Data Entry, Retrieval and Management
Report writing and graphics
Statistical and Mathematical Analysis
Business planning, Forecasting and Decision support
Operations research and Project management
Quality improvement
Applications development
The core of the SAS system is base SAS software, which consists of:
SAS Language
SAS Procedures
SAS Macros
Data Step debugger
ODS
Windowing Environment
The basic components of SAS language are:
SAS Files
Data Step
Procedure Step
SAS Informats
SAS Formats
Variables
Functions
Statements
Miscellaneous(SAS Programs, Outputs, Log and Errors )
SAS GUI
SAS Programming Environment Contains 6 Main Windows:
1.
Project Designer:
Shows the Process Flow of a Project in Flow charts
2.
Project Explorer:
Shows the Process Flow of a Project as Drop Down Menu
3.
Code Editor:
Used to write and Edit codes
4.
Server List:
Show the Physical Storage Locations of Data
5.
Log Window:
Information about the execution of a program and Lists the errors while execution
6.
Output Window:
Displays the output of execution of a program
SAS Programs
SAS programs can be used to access, manage, analyze, or present your data
Layout for SAS Programs SAS statements are in free format. This means that: they can begin and end anywhere on a line one statement can continue over several lines several statements can be on a line.
SAS statements can be specified in uppercase or lowercase
In most situations, text that is enclosed in quotation marks is case sensitive
SAS Libraries
Every SAS file is stored in a SAS library
SAS Library is a collection of SAS files
A SAS data library is the highest level of organization for information within SAS
In the Windows and UNIX environments, a library is typically a group of SAS files in the same folder or directory.
Storing Files Temporarily or Permanently: There are two types of libraries in SAS
Temporary library
Permanent library
Depending on the library name that is used when create a file, we can store SAS files temporarily or permanently
Temporary Library:
Its Temporary Storage Location of a data file
They last only for the current SAS session
Work is the temporary library in SAS
When the session ends, the data files in the temporary library are deleted
The file is stored in Work, when:
No specific library name is used while creating a file
Specify the library name as Work
Permanent Library:
It‟s the Permanent storage location of data files
Permanent SAS libraries are available in subsequent SAS sessions
Permanent SAS data libraries are stored until delete them
To store files permanently in a SAS data library:
Specify a library name Other than the default library name Work
Three Permanent Libraries provided by SAS are:
Local SASuser SAShelp
Creating a Permanent Library:
To create a permanent library use libname statement
It creates a reference to the path where SAS files are stored
The LIBNAME statement is global, which means that the librefs remain in effect until modify them , cancel them, or end your SAS session
The LIBNAME statement assigns the libref for the current SAS session only
Assign a libref to each permanent SAS data library each time a SAS session starts
SAS no longer has access to the files in the library, once the libref is deleted or SAS session is ended.
Contents of Permanent library exists in the path specified
Syntax :
libname
‘‘ ; where,
libref is the name of the library to be created
It can be 1 to 8 characters long Begins with a letter or underscore Contains only letters, numbers, or underscores
path is location in memory to store the SAS files
Example:
libname Taxes ‘C:\Documents and Settings\admin\Desktop\Training‘ ;
Here,
Taxes
-
libname -
A library reference name This keyword assigns the libref taxes to the folder called training in the path:
‘C:\Documents and Settings\admin\Desktop\Training‘
Path should be specified in single code
Data lib1.emp; Length name$ 12; Input id name$ doj sal; Informat doj mmddyy8. sal dollar7.; Format doj date9. sal dollar7.; Label id = “Employee Id” name = “Employee Name” doj = “Date of Joining” Sal = “Salary”; Cards; 1076 abcasdayut 12/23/05 $10,000 1983 aaaertgr 07/12/98 $40,000 1723 xyzasdsf 04/15/98 $25,000 ; Run;
SAS Data Sets
SAS Data Set is a SAS file which holds Data
Data must be in the form of a SAS data set to be processed
Many of the data processing tasks access data in the form of a SAS data set and analyze, manage, or present the data
A SAS data set also points to one or more indexes, which enable SAS to locate records in the data set more efficiently
Rules for SAS Data Set Names: SAS data set names :
can be 1 to 32 characters long must begin with a letter (A–Z, either uppercase or lowercase) or an underscore (_) can continue with any combination of numbers, letters, or underscores.
These are examples of valid data set names:
Payroll LABDATA1995_1997 _EstimatedTaxPayments3
SAS data set consists of two parts:
Descriptor portion
Data portion
Descriptor Portion:
The descriptor portion of a SAS data set contains information about the data set, including:
The name of the data set The date and time that the data set was created The number of observations The number of variables.
Example: Descriptor portion of the data set Clinic.Insure
Data Set Name: CLINIC.INSURE Member Type: DATA Engine: V8 Created: 10:05 Tuesday, March 30, 1999 Observations: 21 Variables: 7 Indexes: 0 Observation Length: 64
Data Portion:
Collection of data values that are arranged in a rectangular table
Example: Here,
Jones is a data value, the weight 158.3 is a data value, and so on
Observations:
Rows are called observations in SAS
It is a Collections of data values that usually relate to a single object in SAS Data Sets
The values Jones, M, 48, and 128.6 constitute a single observation in the data set shown below
Variables:
Columns are called variables in SAS
It is a collection of values that describe a particular characteristic
The values Jones, Laverne, Jaffe and Wilson contribute the variable Name in the data set shown below
Missing Values:
If a data is unknown for a particular observation, a missing value is recorded
“.” (called period) indicates missing value of a numeric variable
“ “ (blank) indicates missing value of a character variable
Variable Attributes:
In addition to general information about the data set, the descriptor portion contains information about the attributes of each variable in the data set
The attribute information includes the variable's: Name Type Length Format Informat Label
Example: Listing of the attribute information in the descriptor portion of the SAS data set Clinic.Insure Variable Type Length Format
Informat
Label
Policy
Num
8
Policy
Number
Total
Num
8
DOLLAR8.2 COMMA10. Total Balance
20
Patient Name
Name
Char
Name:
Each variable has a name that conforms to SAS naming conventions
Variable names follow exactly the same rules as SAS data set names
Like data set names, variable names:
Can be 1 to 32 characters long Must begin with a letter (A–Z, either uppercase or lowercase) or an underscore (_) Can continue with any combination of numbers, letters, or underscores.
Type:
A variable's type is either character or numeric
Character variables, such as Name (shown below), can contain any values
Numeric variables, such as Policy and Total (shown below), can contain only numeric values (the digits 0 through 9, +, -, ., and E for scientific notation)
Length:
A variable's length (the number of bytes used to store it) is related to its type
Character variables can be up to 32,767 bytes long
In the example below, Name has a length of 20 characters and uses 20 bytes of storage.
All numeric variables have a default length of 8
Numeric values (no matter how many digits they contain) are stored as floating-point numbers in 8 bytes of storage, unless specify a different length.
Format:
A Format is an instruction that SAS uses to write data values
Format is used to control the written appearance of data values, or in some cases, to group data values together for analysis
SAS software offers a variety of character, numeric, and date and time formats
Formats can be created and stored
Can permanently assign a format to a variable in a SAS data set, or can temporarily specify a format in a PROC step to determine the way the data values appear in the output
Informat:
Used to Read data values in certain formats into standard SAS values
It determines how data values are read into a SAS data set
Informats are used to read numeric values that contain letters or other special characters
Label:
A variable can have a label consisting of descriptive text up to 256 characters long
By default, many reports identify variables by their names
To display more descriptive information about the variable assign a label to that variable
Example: Label Policy as Policy Number, Total as Total Balance, and Name as Patient Name to display these labels in reports
Referencing Permanent SAS Files
Two-Level Names: Two-level name are used to reference a permanent SAS file in SAS programs
There are two parts in a Two-Level Name: 1. 2.
Libref name Filename Libref – Is the name of the SAS data library that contains the file Filename – Is the name of the file itself
A period separates the libref and filename
Example:
Clinic.Admit is the two-level name for the SAS data set Admit
Admit is assigned to the library named Clinic
Referencing Temporary SAS Files
To reference temporary SAS files specify the default libref Work, a period, and the filename
Example: Here, The two-level name Work.Test references the SAS data set named Test that is stored in the temporary SAS library Work
One-Level name
One-level name (the filename only) can be used to reference a file in a temporary SAS library
When a one-level name is used, the default libref Work is assumed
Example: Here, The one-level name Test also references the SAS data set named Test that is stored in the temporary SAS library Work.
Components of SAS Programs
SAS Programs contains only two steps:
Data Step Proc Step
A SAS Program may contain:
A DATA step A PROC step Combination of DATA and PROC step
Data Step:
Typically create or modify SAS data sets and they can also be used to produce custom-designed reports
DATA steps are used to:
Put data into a SAS data set
Compute values
Check for and correct errors in data
Produce new SAS data sets by subsetting, merging, and updating existing data sets
Proc Step:
They pre-written routines that enable us to analyze and process the data in a SAS data set and to present the data in the form of a report
PROC steps sometimes create new SAS data sets that contain the results of the procedure
PROC steps can list, sort, and summarize data
PROC steps are used to:
Create a report that lists the data
Produce descriptive statistics
Create a summary report
Produce plots and charts
Importing Data for creating SAS Datasets SAS Data step concepts:
DATA steps typically create or modify SAS data sets
Can also be used to produce custom-designed reports.
SAS DATA steps can be used to:
put data into a SAS data set
compute values
check for and correct errors in your data
produce new SAS data sets by subsetting, merging, and updating existing data sets.
A SAS data set can be created by:
Entering data as input Reading existing raw data Accessing external files (files that were created by other software)
The fig below shows how to design and write a DATA step program to create a SAS data set from raw data that is stored in an external file
Data step: Data & Set Statements:
Data & Set statements are used to create a data set
Syntax:
DATA ; SET ; Where,
dataset1 is the Destination Data Set dataset2 is the Source Data Set
Reading Instream Data using Cards and Datalines
Data can be entered into SAS data set directly through SAS program
Reading instream data is useful when to create data and test programming statements on a few observations
To read instream data use:
DATALINES statement as the last statement in the DATA step (except for the RUN statement) and immediately preceding the data lines
a null statement (a single semicolon) to indicate the end of the input data
Only one DATALINES statement can be used in a DATA step
Use separate DATA steps to enter multiple sets of data
If the data contains semicolons, use the DATALINES4 statement plus a null statement that consists of four semicolons (;;;;) to indicate the end of the input data
Syntax:
DATA ; INPUT [$] [$] … ; DATALINES; . . data lines go here . . ; run ;
After the DATALINES statement specify the data values
After typing in the values give a semicolon to indicate the end of the data values.
Can also use Cards instead of datalines
Example:
Data emp_details ; Input id name$ age ; Datalines ; 2458 Murray, W 2462 Almers, C 2501 Bonaventure, T 2523 Johnson, R 2539 LaMance, K 2544 Jones, M
42 38 48 39 45 49
; run ; Here,
A dataset called emp_details is created with variables id, name & age, and having 6 observations
Name is a character variable which is indicated by $ sign after name
Importing Different File types
SAS GUI can be used to import different file types data such as:
Excel File
Comma separated Files (CSV)
Importing Files using PROC IMPORT
Proc import procedure step can be used to import an external file of different file types
Syntax:
proc import datafile =“ External file path “ out= dbms= replace; delimiter= “special character” ; getnames= ; datarow= n ; Where, „External file path‟ is the path of the external file to import „Out=„ specifies the dataset to be created using the imported file „dbms‟ specifies the file type to be imported or „dlm‟ if delimited files are imported „replace‟ replaces already existing files „getnames=yes‟ tells SAS to read the variable names from the first line of the data file „delimiter=„ specifies the delimiter in the external file. It is specified only when the „dbms= dlm‟ is specified „datarow =n‟ specifies the row from which the data has to read from the external file. Where, n is a number
Importing a comma separated file (.csv) : Example 1:
Comma separated file is a special external file with file extension .csv (comma separated variables) proc import datafile="comma.csv" out= mydata dbms=csv replace; getnames=no; run;
Here, A comma separated file called „comma.csv‟ is imported A new dataset called „mydata‟ is created „getnames=no‟ indicates that the first row in the file is not variable names „replace‟ indicated SAS to replace the existing file mydata
Example 2:
Another way of reading a comma delimited file is to consider a comma as an ordinary delimiter
Here is a program that shows how to use the dbms=dlm and delimiter="," proc import datafile="comma1.txt" out=mydata dbms=dlm replace; Delimiter =",” ; Getnames =yes ; Datarow =5 ; Run ; Here, „comma1.txt‟ is a comma separated text file whose variable values are separated by commas
„dbms=dlm‟ indicates that comma1.txt is a delimiter file „delimiter=“,” „ indicates the delimiter as “,” „Datarow=5‟ tell SAS to read data from the 5th row
Import from Tab- Delimitated files (TXT File): Example: proc import datafile ="tab.txt" out=mydata dbms=tab replace ; getnames=no ; Run ;
Here, „tab.txt‟ is a tab separated text file „dbms=tab‟ indicates tab.txt as tab separated file
Data Understanding Proc Contents Step:
The CONTENTS procedure is used to create SAS output that describes either of the following:
The contents of a library The descriptor information for an individual SAS data set
Describes the structure of the data set rather than the data values Displays valuable information at the...
Data set level
Name Engine Creation date Number of observations Number of variables File size (bytes)
Variable level
Name Type Length Formats Position Label
Syntax:
Proc Contents Data = libref . _ALL_ NODETAILS; Run;
Where,
libref is the libref that has been assigned to the SAS library.
_ALL_ requests a listing of all files in the library
A period (.) is used to append _ALL_ to the libref
NODETAILS (NODS) suppresses the printing of detailed information about each file when _ALL_ is specified.
Specify NODS only when you specify _ALL_
Example:
To view the contents of the Mylib library, submit the following PROC CONTENTS step: proc contents data = mylib ._all_ nods ; run ;
The output from this step lists only the names, types, sizes, and modification dates for the SAS files in the Mylib library
To view the descriptor information for the Mylib.Admit data set, submit the following PROC CONTENTS step: proc contents data = mylib .admit ; run ;
The output from this step lists information for Mylib.Admit data set, including an alphabetic list of the variables in the data set
Proc Print:
Prints a listing of the values of some or all of the variables in a SAS data set
Syntax: proc print data = libref .Datasetname [ (firstobs = n obs = n) split = „Special Character‟ double label n noobs ] ; [
Id Variable list ; Var Variable list ; By Variable list ; Sum Varibale list
] Run ; Where,
„[ ]‟ are optionals
Libref is the library in which Datasetname is the dataset whose values are to be printed
Firstobs indicates the starting number of observation to be printed
Obs indicates the ending number of observation to be printed
Drop indicates the variables to be dropped
Keep indicates the variables to be keep
Split ='split character' - splits labels as column headings across multiple lines where split character appears
Double - double spaces the printed output
Label - uses variable labels as column headings (variable name is default heading)
N - Lists no: of observations in the specified datasets
Noobs - suppresses the observation number in the output.
Id -Identify observations by the formatted values of the variables which can be listed instead of observation numbers
Var -Select variables that appear in the report and determine their order
By - Produce a separate section of the report for each BY group
Sum - Total values of numeric variables
Example:
proc print data = candy_products (firstobs=1 obs=16 ) n noobs double label ; id Prodid ; var Prodid Product Category Retail_price ; by Category ; Sum Retail_price ; Run ; Here,
Candy_products is the dataset which is present in work library First observation to 16thobservation are printed (firstobs=1 and obs=16) N gives the number of observation Double - Double spacing between observations printed (only in list input) Label - Prints the label of each variable instead of variable names Id - Prodid becomes the row identifier instead of observation no: Var - Only the variables indicated here are printed By - The outputs are grouped by category Sum - Sum of the Retail_price
Data Transfer from one library to another
We can create a new data set from an existing SAS data set
To create the new data set, read a data set using the DATA step and use the programming features of the DATA step to manipulate data
Store the manipulated data to new data set or the same which will overwrite the existing data
Syntax 1: Data SAS-data-set; Set SAS-data-set; Run; where ,
SAS-data-set in the DATA statement is the name (libref.filename) of the SAS data set to be created (Destination Data Set)
SAS-data-set in the SET statement is the name (libref.filename) of the SAS data set to be read (Source Data Set)
Example:
libname lab23 „ c : \ drug\ allergy \ labtests „ ; libname research „ c : \ drug \ allergy „ ; data lab23.drug1h ; set research.cltrials ; Run ;
Where
Lab23 and research are two libraries which are created in two different locations
The DATA statement creates the permanent SAS data set Drug1H
Drug1H will be stored in a SAS data library to which the libref Lab23 has been assigned
The SET statement below reads the permanent SAS data set Research.CLTrials.
Syntax 2: Data Transfer from one library to another using Proc Copy
proc copy in = libref1 out = libref2 ; [ select Ds1 Ds2 . . . ; ] run ; Where,
Libref1 is the library from which the data sets are to copied
Libref2 is the library to which the data sets are to be copied
Select is an option which selects the data sets Ds1, Ds2, etc form libref1 to libref2
If Select is not used, all the data sets from libref1 is copied to libref2
Example:
proc copy in = clinic out = work ; select admit ; run ;
Here,
Data Set admit is copied from clinic libref to temporary library work
Manipulating data during data transfers
Some of the options for manipulating data are:
Firstobs Obs Label Rename Delete Drop Keep by group point= option Output END= option
Firstobs & Obs Data Set Options:
Firstobs and Obs options are used to select a range of observations from a data set
It can be used in both Data step and proc step
When used in Data step the selected observation remain in memory
When used in proc print step the output displays the selected observations
Firstobs specifies the starting no: of the observations to be selected
Obs specifies the ending no: of the observations to be selected
Firstobs and Obs can be used together to select a range of observations
If only Firstobs is specified, observations from that position to the end of file are selected
If only Obs is specified, observations from first to the specified no: are selected
Syntax:
data SAS-Data-Set; Set SAS-Data-Set (firstobs = n obs = n); run; or data SAS-Data-Set (firstobs = n obs = n); Set SAS-Data-Set; run; Where,
SAS-Data-Set in Data Step is the Destination Data set
SAS-Data-Set in Set Step is the Source Data set
N ;- Any numeric value
Firstobs specifies the observation to start with
Obs specifies the last observation
Firstobs and Obs options can be used both in Data Step or Set Step
Example:
data candy_products; set local.candy_products (firstobs=10 obs=100); run;
Here,
91 observations are copied from candy_products in local library to candy_products in work library
Label & Rename Statements:
Label is a descriptive text given to a variable
It can be up to 256 characters long
Label can be assigned temporarily in proc step or permanently using data step
Label assigned in data step remains in memory and will be shown when the data set is printed using proc print step
Rename statement is used to rename a variable in the data set
Rename statement in data step will permanently rename the variable in the data set
Syntax:
Data libname .dataset-name ; Set libname .dataset-name ; Label Variable-Name = <„ Variable Label‟>; Rename Variable-Name = ; Run; or proc print data= libname . Dataset-name Label; Label Variable-Name = <„Variable Label‟>; Run; Where,
„Variable Label‟ is assigned to Variable specified by Variable-Name in the Label Statement „New Variable Name‟ is assigned to the Variable specified by Variable-Name in the Rename Statement Label in Data step will write the new label in memory for that variable and will be displayed when Label in proc step will only be displayed when that block of proc step is being executed Label option should be specified in proc when using label statement in proc step
Example:
Data demo.class; Set demo.class ; Label sizehh = „Size of household‟; Rename sizehh = sizehouse; Run; proc print data = demo1.class1 Label; label sizehh = „Size of Household‟; run; Here,
„Size of household‟ is assigned as label for the variable Sizehh in Data step
Sizehh variable is renamed as „Sizehouse‟ in Data step
„Size of household‟ label is assigned for the variable Sizehh temporarily using proc step which is effective only when that block of code is executed
Rename Statement can be used only in Data step as it is data modification
DROP= and KEEP= Data Set Options:
Drop= and Keep= options in data step can be used to drop and keep variables in that data set
Drop=, omits all variables specified after it
Keep=, keeps all variables specified after it
Use the KEEP= option instead of the DROP= option if more variables are dropped than kept
Specify drop and keep options in parentheses after a SAS data set name
Syntax: (DROP = variable(s)) (KEEP = variable(s)) where ,
the DROP= or KEEP= option, in parentheses, follows the name of the data set that contains the variables to be dropped or kept
variable(s) identifies the variables to drop or keep
Example:
1.
Timemin and Timesec are dropped from the data set clinic.stress data clinic.stress (drop= timemin timesec); Set clinic.stress; Run;
2.
Timemin and Timesec are Kept in the data set clinic.stress data clinic.stress (Keep= timemin timesec); Set clinic.stress; Run;
Drop and Keep Statements:
Another way to exclude variables from data set is to use the DROP statement or the KEEP statement
Like the DROP= and KEEP= data set options, these statements drop or keep variables
The DROP statement differs from the DROP= data set option in the following ways:
Cannot use the DROP statement in SAS procedure steps The DROP statement applies to all output data sets that are named in the DATA statement. To exclude variables from some data sets but not from others, place the appropriate DROP= data set option next to each data set name that is specified in the DATA statement.
The KEEP statement is similar to the DROP statement, except that the KEEP statement specifies a list of variables to write to output data sets
Use the KEEP statement instead of the DROP statement if the number of variables to keep is significantly smaller than the number to drop
Syntax:
DROP variable(s); KEEP variable(s); Where,
variable(s) identifies the variables to drop or keep
Example: data clinic.stress; Set clinic.stress; drop timemin timesec; Run; Here,
Drop statement omits variables timemin and timesec
Data Modifications using conditional statements Conditional Statement:- Where:
„Where‟ statement can be used to select observations during proc step and data step
There can be only one WHERE statement in a step
Syntax: Where where-expression; Where,
where-expression specifies a condition for selecting observations
The where-expression can be any valid SAS expression
The WHERE statement works for both character and numeric variables
WHERE statement is observation level
To specify a condition based on the value of a character variable: enclose the value in quotation marks write the value with lowercase and uppercase letters exactly as it appears in the data set
Following comparison operators can be used to express a condition in the WHERE statement: Symbol
Meaning
Example
= or eq
equal to
where name='Jones, C.';
^= or ne
not equal to
where temp ne 212;
> or gt
greater than
where income>20000;
< or lt
less than
where partno lt "BG05";
>= or ge
greater than or equal to
where id>='1543';
<= or le
less than or equal to
where pulse le 85;
Contains operator in Where:
The CONTAINS operator selects observations that include the specified substring.
The mnemonic equivalent for the CONTAINS operator is „?’
Example:
where firstname CONTAINS 'Jon'; where firstname ? 'Jon'; Here,
„Firstname‟ is the variable name and „Jon‟ is the value
Compound WHERE Expressions:
WHERE statements can be used to select observations that meet multiple conditions
To link a sequence of expressions into compound expressions, use logical operators, including the following: Operator
Meaning
AND or &
and, both. If both expressions are true, then the compound expression is true.
OR or |
or, either. If either expression is true, then the compound expression is true.
Example:
1.
Where with proc step
proc print data = clinic.admit; var age height weight fee; where age > 30; run; 2.
Where with data step data clinic.admit; set clinic.admit; where age >30 and pulse >55; run;
3.
Some examples using logical operators: where ID>1050 and state='NC'; where actlevel = 'LOW' or actlevel = 'MOD'; where actlevel in ('LOW','MOD'); where fee in (124.80,178.20); where (age<=55 and pulse>75) or area='A';
Conditional Statement:- IF Then Else:
The IF-THEN statement executes a SAS statement when the condition in the IF clause is true comparison and Logical operators can be used in IF conditional expression Any numeric value other than 0 or missing is true, and a value of 0 or missing is false
Syntax:
IF expression THEN statement; [ else IF expression THEN statement; . . else statement; ] Where,
expression is any valid SAS expression statement is any executable SAS statement
Example:
Data clinic.stress; Set clinic.stress; if totaltime > 800 then TestLength = 'Long'; else if 750 <= totaltime <= 800 then TestLength ='Normal'; else if totaltime < 750 then TestLength = 'Short'; Run; Here,
„Long‟ is assigned to variable Testlength if totaltime is greater than 800
If first IF expression is not true, the control will check the next expression. If true it will assign and quit the execution
If first and second IF statements are not true, the control will come to third expression and assign „Short‟ to Testlenght
Deleting Unwanted Observations: Delete option
If Then statement along with Delete option can be used to select observations in a data set and delete
Syntax: IF expression THEN DELETE;
If the expression is:
true, the DELETE statement executes, and control returns to the top of the DATA step (the observation is deleted).
false, the DELETE statement does not execute, and processing continues with the next statement in the DATA step
Example:
Data clinic.stress; Set clinic.stress; if resthr < 70 then delete; Run; Here,
The IF-THEN and DELETE statements below omit any observations whose values for RestHR are lower than 70
Assigning Values Conditionally Using SELECT Groups:
Use IF-THEN/ELSE statements or SELECT groups based on the following criteria.:
When a long series of mutually exclusive conditions and the comparison is numeric, using a SELECT group is more efficient than using a series of IF-THEN or IFTHEN/ELSE statements because CPU time is reduced
SELECT groups also make the program easier to read and debug.
For programs with few conditions, use IF-THEN/ELSE statements
Syntax:
SELECT <(select-expression)>; WHEN-1 (when-expression-1 <..., when-expression-n>) statement; WHEN-n (when-expression-1 <..., when-expression-n>) statement; END; Where,
SELECT begins a SELECT group
The optional select-expression specifies any SAS expression that evaluates to a single value.
WHEN identifies SAS statements that are executed when a particular condition is true.
When-expression specifies any SAS expression, including a compound expression
Must specify at least one when-expression
Statement is any executable SAS statement.
The optional OTHERWISE statement specifies a statement to be executed if no WHEN condition is met.
END ends a SELECT group
Example:
data emps (keep=salary group); set sasuser.payrollmaster; length Group $ 20; select (jobcode); when ("FA1") group="Flight Attendant I"; when ("FA2") group="Flight Attendant II"; when ("FA3") group="Flight Attendant III"; when ("ME1") group="Mechanic I"; when ("ME2") group="Mechanic II"; when ("ME3") group="Mechanic III"; when ("NA1") group="Navigator I"; when ("NA2") group="Navigator II"; when ("NA3") group="Navigator III"; when ("TA1","TA2","TA3") group="Ticket Agents"; otherwise group="Other"; end; run;
The SELECT group assigns values to the variable Group based on values of the variable JobCode
Appending Data Sets
It is concatenation of two data sets which are already existing.
The observation in each data set will stack together according to the order specified to form new data set
Appends the observations from one data set to another data set
Syntax:
DATA output-SAS-data-set; SET SAS-data-set-1 SAS-data-set-2; RUN;
Where,
output-SAS-data-set names the data set to be created
SAS-data-set-1 and SAS-data-set-2 specify the data sets to be read
SAS-data-set-1 and SAS-data-set-2 gets appended and copies to output-SAS-data-set
Example:
Data combined; Set A C; Run;
Appending Data Sets Using Proc Step
Adding observations using append procedure
The base file gets appended with observations from data file.
No new data set is created
Works only if the base file is having all the variables in the data file, otherwise use force option
Syntax:
Proc Append base = data = [force]; Run;
Where,
SAS-data-set-1 and SAS-data-set-2 specify the data sets to be read
SAS-data-set-2 gets appended to SAS-data-set-1999
Force is an optional keyword, used when base file is having some variables missing compared to data file, to force SAS to append
Example:
Proc Append base = A data = C; Run;
Merging
A merge combines observations from two or more SAS data sets based on the values of specified common variables (one or more)
It creates a new data set (the merged data set)
Merging is done in a data step with the statements
Prerequisites for a match-merge
MERGE : to name the input data sets BY : to name the common variable(s) to be used for matching
input data sets must have a common variable input data sets must be sorted by the common variable(s)
It is also called "match-merge."
Syntax:
DATA output-SAS-data-set; MERGE SAS-data-set-1 SAS-data-set-2; BY variable(s); RUN; Where,
output-SAS-data-set names the data set to be created
SAS-data-set-1 and SAS-data-set-2 specify the data sets to be read
variable(s) in the BY statement specifies one or more variables whose values are used to match observations
DESCENDING indicates that the input data sets are sorted in descending order by the variable that is specified
If there are more than one variable in the BY statement, DESCENDING applies only to the variable that immediately follows it
Each input data set in the MERGE statement must be sorted in order of the values of the BY variable(s)
Each BY variable must have the same type in all data sets to be merged
Sorting of Data Set:
Procedure sort can be used to sort the data sets either ascending or descending
Syntax: Proc Sort Data = Data-Set-1 [out = Data-Set-2]; By [Descending] Variabel1 [Variable2 …]; Run; Here,
Data-Set-1 will be sorted in either ascending or descending order
If „OUT=„ option is specified then a Data-Set-1 will be copied to Data-Set-2 and will get sorted there but the original data set (Data-Set-1) remains un sorted.
„By‟ statement will sort the data set according to the variables specified
„Descending‟ option will sort the data set in descending order by the variable just proceeding that.
Example:
During match-merging SAS sequentially checks each observation of each data set to see whether the BY values match, then writes the combined observation to the new data set data merged; merge a b; by num; run;
Example: Sample Data Sets:
1. Clinic.Demog proc sort data=clinic.demog; by id; run; proc print data=clinic.demog; Obs
ID
Age
Sex
Date
1
A001
21
m
05/22/75
2
A002
32
m
06/15/63
3
A003
24
f
08/17/72
4
A004
.
5
A005
44
f
02/24/52
6
A007
39
m
11/11/57
03/27/69
2. Clinic.Visit proc sort data=clinic.visit; by id; run; proc print data=clinic.visit; run;
Obs
ID
Visit
SysBP
DiasBP
Weight
Date
1
A001
1
140
85
195
11/05/98
2
A001
2
138
90
198
10/13/98
3
A001
3
145
95
200
07/04/98
4
A002
1
121
75
168
04/14/98
5
A003
1
118
68
125
08/12/98
6
A003
2
112
65
123
08/21/98
7
A004
1
143
86
204
03/30/98
8
A005
1
132
76
174
02/27/98
9
A005
2
132
78
175
07/11/98
10
A005
3
134
78
176
04/16/98
11
A008
1
126
80
182
05/22/98
Example: Merging
data clinic.merged; merge clinic.demog clinic.visit; by id; run; Obs
ID
Age
Sex
Date Visit
SysBP
DiasBP
Weight
1
A001
21
m
11/05/98
1
140
85
195
2
A001
21
m
10/13/98
2
138
90
198
3
A001
21
M
07/04/98
3
145
95
200
4
A002
32
M
04/14/98
1
121
75
168
5
A003
24
f
08/12/98
1
118
68
125
6
A003
24
f
08/21/98
2
112
65
123
7
A004
.
03/30/98
1
143
86
204
8
A005
44
f
02/27/98
1
132
76
174
9
A005
44
f
07/11/98
2
132
78
175
10
A005
44
f
04/16/98
3
134
78
176
11
A007
39
m
11/11/57
.
.
.
12
A008
.
126
80
182
05/22/98
1
Excluding Unmatched Observations:
By default, DATA step match-merging combines all observations in all input data sets
To exclude unmatched observations from output data set, use the IN= data set option and the subsetting IF statement in DATA step.
In this case, use the IN= data set option to create and name a variable that indicates whether the data set contributed data to the current observation
the subsetting IF statement to check the IN= values and to write to the merged data set only those observations that appear in the data sets for which IN= is specified
Syntax:
(IN=variable) Where,
the IN= option, in parentheses, follows the data set name
variable names the variable to be created
Within the DATA step, the value of the variable is 1 if the data set contributed data to the current observation. Otherwise, its value is 0.
Example:
To Match-merge the data sets Clinic.Demog and Clinic.Visit and select only observations that appear in both data sets :
Use IN= to create two temporary variables, indemog and invisit
The first IN= creates the temporary variable indemog, which is set to 1 when an observation from Clinic.Demog contributes to the current observation; otherwise, it is set to 0
Likewise, the value of invisit depends on whether Clinic.Visit contributes to an observation or not
IF statement is used to select only observations that appear in both Clinic.Demog and Clinic.Visit
If the condition is met, the new observation is written to Clinic.Merged. Otherwise, the observation is deleted data clinic.merged; merge clinic.demog (in= indemog) clinic.visit (in=invisit); by id; if indemog=1 and invisit=1; run; proc print data=clinic.merged; run;
Output:
Obs
ID
Age
Sex
BirthDate
Visit
SysBP
DiasBP
Weigh t
VisitDate
1
A001
21
m
05/22/75
1
140
85
195
11/05/98
2
A001
21
m
05/22/75
2
138
90
198
10/13/98
3
A001
21
m
05/22/75
3
145
95
200
07/04/98
4
A002
32
m
06/15/63
1
121
75
168
04/14/98
5
A003
24
f
08/17/72
1
118
68
125
08/12/98
6
A003
24
f
08/17/72
2
112
65
123
08/21/98
7
A004
.
03/27/69
1
143
86
204
03/30/98
8
A005
44
f
02/24/52
1
132
76
174
02/27/98
9
A005
44
f
02/24/52
2
132
78
175
07/11/98
10
A005
44
f
02/24/52
3
134
78
176
04/16/98
Different Types Of Merge Join Inner Join
Condition No condition
Description Includes all the observations from both the dataset
Right Inner Join
If Y = 1
Includes all the observations from right dataset
Left Inner Join
If X = 1
Includes all the observations from left dataset
Exact Join
If X = 1 and Y = 1
Includes all the matching observations from both datasets
Outer Join
If X = 0 or Y = 0
Includes all the non matching observations from both datasets
Right Outer Join
If X = 0 and Y = 1
Includes all the non matching observations from right dataset
Left Outer Join
If X = 1 and Y = 0
Includes all the non matching observations from left dataset
