SAS Programming: Working With Variables

1
SAS Programming Working With Variables
2
Data Step Manipulations

• New variables should be created during a Data
  step
• Existing variables should be manipulated during a
  data step

3
Missing Values in SAS

• SAS uses a period (.) to represent missing values
  in a SAS data set
• Different SAS procedures and functions treat
  missing values differently - always be careful
  when your SAS data set contains missing values

4
Working With Numeric Variables

• SAS uses the standard arithmetic operators
• , -, , /, (exponentiation)
• Note on Missing Values Arithmetic operators
  propagate missing values.
• SAS has many built-in numeric functions
• round(variable,value) Rounds variable to nearest
  unit given by value.
• sum(variable1, variable2, ) Adds any number of
  variables and ignores missing values

5
Acting on Selected Observations

• Working with selected observations - subsets of a
  SAS data set - is easy in SAS
• First, you must decide on a selection process.
  What is the distinguishing characteristic of the
  observations you want to work with?

6
Selecting Observations IF-THEN Statements

• The IF-THEN statement is the most common way to
  select observations. Format
• IF condition THEN action
• condition is one or more comparisons. For any
  observation, condition is either true or false.
  If condition is true, SAS performs the action.

7
IF-THEN Statement Example

• Suppose INC is a variable representing annual
  household income and you want to create a dummy
  variable, DUM, based on income that takes value 1
  when income is less than 10,000.
• IF INClt10000 THEN DUM1
• IF INC gt10000 THEN DUM0

8
Using OBS in condition

• In a SAS data set, each record has an observation
  number which is the number stored in the variable
  OBS
• OBS can be used in a condition, but you must
  refer to the observation number using the
  variable _n_
• Example set the first 10 observations of INC
  equal to zero
• IF _n_ lt 10 THEN INC0

9
Comparison Operators

• There are 6 comparison operators
• Can use either the symbol or mnemonic
• Symbol Mnemonic Meaning
• EQ Equal to
• NE Not equal to
• gt GT Greater than
• lt LT Less than
• gt GE Greater than or equal to
• lt LE Less than or equal to

10
Multiple Comparisons

• Can make more than one comparison in condition by
  using AND/OR
• AND / All parts must be true for condition to
  be true
• Or / At least one part must be true for
  condition to be true
• Be careful when using AND/OR
• Can use parentheses in condition

11
Selecting Observations for New SAS Data Sets

• Can use IF-THEN statements to create new SAS data
  sets
• Either delete or keep selected observations based
  on condition

12
Deleting Observations

• Format for IF-THEN
• IF condition THEN DELETE
• Example Removing missing observations. Suppose
  the variable INC is missing for some households
  and you want to drop these observations
• IF INC. THEN DELETE

13
Keeping Selected Observations

• A more straightforward way to create new SAS data
  sets is to keep only those observations that meet
  some condition. Format
• IF condition

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Example

• The file salary.dat contains data for 93
  employees of a Chicago bank. The file contains
  the following variables
• Y Salary
• X Years of education
• E Months of previous work experience
• T Number of months after 1/1/69 that the
  individual was hired
• First 61 observations are females, last 32 males

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Example Create Dummy for Males
Program to create dummy variables and new SAS
data sets data
salary infile s\mysas\salary.dat input y x e
t IF _n_ gt61 THEN G1 IF _n_ lt 60 THEN
G0 run
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Example Create Data Set for Males
Make a new SAS data set composed of
only records for males
data males New SAS data set
setsalary Created from salary IF
G1 run
17
Example Create Data Set for Females
Make a new SAS data set composed of
only records for females
data females New SAS data set
setsalary Created from salary IF
G0 run
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Describing Data Sample Statistics

• Format
• PROC UNIVARIATE ltoption-listgt
• VAR variable-list
• BY variable-list
• FREQ variable
• WEIGHT variable

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Selected Options

• DATASAS-data-set Specify Data Set
• If omitted, uses most recent
• SAS data set
• FREQ Generate Frequency Table
• NOPRINT Suppress Printed Output

20
VAR Statement

• List of variables to calculate sample statistics
  for.
• If no variables are specified, sample statistics
  are generated for all numeric variables

21
WEIGHT Statement

• Specifies a numeric variable in the SAS data set
  whose values are used to weight each observation

22
BY Statement

• Can be used to obtain separate analyses on
  observations in groups defined by some value of a
  variable.
• Example Suppose SEX1 if individual is male,
  SEX0 if individual is female EARNannual
  earnings.
• PROC UNIVARIATE Generates statistics
• VAR EARN on earnings for men
• BY SEX and women
• RUN

23
BY Statements and Sorting

• Before using a BY statement, the SAS data set
  must be sorted on the variable specified
• SAS puts the observations in order, based on the
  values of the variables specified in the BY
  statement.
• Use PROC SORT

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PROC SORT

• FORMAT
• PROC SORT ltoptionsgt
• BY ltoptionsgtvariables
• Sort Order ascending. For descending, put
  DESCENDING on BY line

25
Describing Data Frequencies

• FORMAT
• PROC FREQ ltoptionsgt
• BY variables
• TABLES requestslt/optionsgt
• WEIGHT variable

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One-Way Frequency Table

• SEX1 (Male) SEX0(Female)
• EDUCATION1(Less than High School), 2(High
  School),3(Some College),4(College grad.)
• EARNAnnual Earnings
• PROC FREQ
• TABLES EDUCATION
• RUN
• PROC FREQ
• TABLES EDUCATION
• BY SEX
• RUN