presentation and data short courses intro to sas download data to desktop 1

Presentation and Data

http://www.lisa.stat.vt.edu

Short Courses

Intro to SAS

Download Data to Desktop

1

http://www.lisa.stat.vt.edu/

http://www.lisa.stat.vt.edu/

Mark Seiss, Dept. of Statistics

Introduction to SAS Part 1

February 21, 2011

Reference Material

The Little SAS Book – Delwiche and Slaughter

SAS Programming I: Essentials

SAS Programming II: Manipulating Data with the DATA Step

Presentation and Data

http://www.lisa.stat.vt.edu

Presentation Outline

Part 1

1. Introduction to the SAS Environment

2. Working With SAS Data Sets

Part 2

1. Summary Procedures

2. Basic Statistical Analysis Procedures

Presentation Outline

Questions/Comments

Individual Goals/Interests

Introduction to the SAS Environment

1. SAS Programs

2. SAS Data Sets and Data Libraries

3. SAS System Help4. Creating SAS Data Sets

SAS Programs• File extension - .sas

• Editor window has four uses:• Access and edit existing SAS programs

• Write new SAS programs

• Submitting SAS programs for execution

• Saving SAS programs

• SAS program – sequence of steps that the user submits for execution

• Submitting SAS programs• Entire program

• Selection of the program

SAS Programs• Syntax Rules for SAS statements

• Free-format – can use upper or lower case

• Usually begin with an identifying keyword

• Can span multiple lines

• Always end with a semicolon

• Multiple statements can be on the same line

• Errors

• Misspelled key words

• Missing or invalid punctuation (missing semi-colon common)

• Invalid options

• Indicated in the Log window

SAS Programs• 2 Basic steps in SAS programs:

• Data Steps

• Typically used to create SAS datasets and manipulate data,

• Begins with DATA statement

• Proc Steps

• Typically used to process SAS data sets

• Begins with PROC statement

• The end of the data or proc steps are indicated by:• RUN statement – most steps

• QUIT statement – some steps

• Beginning of another step (DATA or PROC statement)

SAS Programs• Output generated from SAS program – 2 Windows

• SAS log

• Information about the processing of the SAS program

• Includes any warnings or error messages

• Accumulated in the order the data and procedure steps are submitted

• SAS output

• Reports generated by the SAS procedures

• Accumulates output in the order it is generated

SAS Data Sets and Data Libraries• SAS Data Set

• Specifically structured file that contains data values.

• File extension - .sas7bdat

• Rows and Columns format – similar to Excel

• Columns – variables in the table corresponding to fields of data

• Rows – single record or observation

• Two types of variables

• Character – contain any value (letters, numbers, symbols, etc.)

• Numeric – floating point numbers

• Located in SAS Data Libraries

SAS Data Sets and Data Libraries• SAS Data Libraries

• Contain SAS data sets

• Identified by assigning a library reference name – libref

• Temporary

• Work library

• SAS data files are deleted when session ends

• Library reference name not necessary

• Permanent

• SAS data sets are saved after session ends

• SASUSER library

• You can create and access your own libraries

SAS Data Sets and Data Libraries• SAS Data Libraries cont.

• Assigning library references

• Syntax

LIBNAME libref ‘SAS-data-library’;

• Rules for Library References• 8 characters or less

• Must begin with letter or underscore

• Other characters are letters, numbers, or under scores

SAS Data Sets and Data Libraries• SAS Data Libraries cont.

• Identifying SAS data sets within SAS Data Libraries

libref.filename

• Accessing SAS data sets within SAS Data Libraries

Example: DATA new_data_set;

set libref.filename;

run;

• Creating SAS data sets within SAS Data Libraries

Example: DATA libref.filename;

set old_data_set;

run;

SAS System Help• SAS Help and Documentation

• Help SAS Help and Documentation

• Red Book Icon

• SAS Online Help

• http://support.sas.com/

Creating SAS Data Sets• Creating a SAS data sets from raw data

• 4 methods

1. Importing existing data sets using Import menu option

2. Importing existing raw data in SAS program

3. Manually entering raw data in SAS program

4. Manually entering raw data using Table Editor

Creating SAS Data Sets• Using the import data menu option

1. File Import Data

2. Standard data source select the file format

3. Specify file location or Browse to select file

4. Create name for the new SAS data set and specify location

Creating SAS Data Sets• Compatible file formats

• Microsoft Excel Spreadsheets

• Microsoft Access Databases

• Comma Separate Files (.csv)

• Tab Delimited Files (.txt)

• dBASE Files (.dbf)

• JMP data sets

• SPSS Files

• Lotus Spreadsheets

• Stata Files

• Paradox Files

Creating SAS Data Sets• Example Data Sets

• Excel File – State_SAT_data.xls

• http://www.stat.ucla.edu/labs/datasets/sat.dat

• Extracted from 1997 Digest of Education Statistics, an annual publication of the U.S. Department of Education

• Contains variables that show the relationship between public school expenditure and SAT performance

• Variables: – State (state)– Current expenditure per pupil (expend)– Average pupil to teacher ratio (PT_ratio)– Estimated annual salary of teachers (salary)– Percentage of eligible students taking the SAT (students)– Average verbal SAT score (verbal)– Average math SAT Score (math)– Average total score (total)

http://www.stat.ucla.edu/labs/datasets/sat.dat

Creating SAS Data Sets• Example Data Sets Cont.

• Text file – State_region_data.txt• Contains region assignments for each state

• 1 = New England

• 2 = Middle Atlantic

• 3 = East North Central

• 4 = West North Central

• 5 = South Atlantic

• 6 = East South Central

• 7 = West South Central

• 8 = Mountain

• 9 = Pacific

Creating SAS Data Sets

Import State_SAT_data.xls Assign as work.state_sat_data.sas7bdat

Import State_region_data.txt Assign as

work.state_region_data.sas7bdat

Introduction to theSAS Environment

Questions/Comments

Working With SAS Data Sets

1. Data Set Information

2. Data Set Manipulation

3. Data Set Processing

4. Combining Data Sets

A. Concatenating/Appending

B. Merging

5. Saving Data Sets

Data Set Information• Proc Contents

• Output contains a table of contents of the specified data set

• Data Set Information• Data set name

• Number of observations

• Number of Variables

• Variable Information• Type (numeric or character)

• Length

• Syntax:

PROC CONTENTS DATA=input_data_set;

RUN;

Data Set InformationAssignment

Obtain Data Set Information for work.state_sat_data and work.state_region_data

Data Set InformationSolution

proc contents data=state_sat_data;

run;

proc contents data=state_region_data;

run;

Data Set Manipulation• Create a new SAS data set using an existing SAS data set as

input• Specify name of the new SAS data set after the DATA statement

• Use SET statement to identify SAS data set being read

• Syntax:

DATA output_data_set;

SET input_data_set;

<additional SAS statements>;

RUN;

• By default the SET statement reads all observations and variables from the input data set into the output data set.

Data Set Manipulation• Assignment Statements

• Evaluate an expression

• Assign resulting value to a variable

• General Form: variable = expression;

• Example: miles_per_hour = distance/time;

• SAS Functions• Perform arithmetic functions, compute simple statistics, manipulate

dates, etc.

• General Form: variable=function_name(argument1, argument2,…);

• Example: Time_worked = sum(Day1,Day2, Day3, Day4, Day5);

Data Set Manipulation• Selecting Variables

• Use DROP and KEEP to determine which variables are written to new SAS data set.

• 2 Ways

• DROP and KEEP as statements– Form: DROP Variable1 Variable2;

KEEP Variable3 Variable4 Variable5;

• DROP and KEEP options in SET statement– Form: SET input_data_set (KEEP=Var1);

Data Set Manipulation• Conditional Processing

• Uses IF-THEN-ELSE logic

• General Form: IF <expression1> THEN <statement>;

ELSE IF <expression2> THEN <statement>;

ELSE <statement>;

• <expression> is a true/false statement, such as:

• Day1=Day2, Day1 > Day2, Day1 < Day2

• Day1+Day2=10

• Sum(day1,day2)=10

• Day1=5 and Day2=5

Data Set Manipulation• Conditional Processing

Symbolic Mnemonic Example

= EQ IF region=‘Spain’;

~= or ^= NE IF region ne ‘Spain’;

> GT IF rainfall > 20;

< LT IF rainfall lt 20;

>= GE IF rainfall ge 20;

<= LE IF rainfall <= 20;

& AND IF rainfall ge 20 & temp < 90;

| or ! OR IF rainfall ge 20 OR temp < 90;

IS NOT MISSING

IF region IS NOT MISSING;

BETWEEN AND IF region BETWEEN ‘Plain’ AND ‘Spain’;

CONTAINS IF region CONTAINS ‘ain’;

IN IF region IN (‘Rain’, ‘Spain’, ‘Plain’);

Data Set Manipulation• Conditional Processing cont.

• If <expression1> is true, <statement> is processed

• ELSE IF and ELSE are only processed if <expression1> is false

• Only one statement specified using this form

• Use DO and END statements to execute group of statements

• General Form: IF <expression> THEN DO;<statements>;

END;ELSE DO;

<statements>;END;

Data Set Manipulation• Subsetting Rows (Observations)

• We will look at two ways

• Using IF statement

• Using WHERE option in SET statement

• IF statement

• Only writes observations to the new data set in which an expression is true;

• General Form: IF <expression>;

• Example: IF career = ‘Teacher’;IF sex ne ‘M’;

• In the second example, only observations where sex is not equal to ‘M’ will be written to the output data set

Data Set Manipulation• Subsetting Rows (Observations) cont.

• Where Option in SET statement

• Use option to only read rows from the input data set in which the expression is true

• General Form: SET input_data_set (where=(<expression>));

• Example:SET vacation (where=(destination=‘Bermuda’));

• Only observations where the destination equals ‘Bermuda’ will be read from the input data set

• Comparison

• Resulting output data set is equivalent

• IF statement – all rows read from the input data set

• Where option – only rows where expression is true are read from input data set

• Difference in processing time when working with big data sets

Data Set Manipulation• Assignments

1. Create new dataset work.state_SAT_data2 from work.state_SAT_data

Assign new variable upper_ind

If total > 1000 then upper_ind=1

Otherwise upper_ind=0

2. Create new dataset work.south from work.state_region_data

Specify work.south contains only records from regions 5, 6, or 7

Specify work.south only contains the state variable

Data Set Manipulation• Solutions

1. data state_sat_data2;

set state_sat_data;

if total>1000 then upper_ind=1;

else upper_ind=0;

run;

Data Set Manipulation• Solutions

2. data south;

set state_region_data;

if region=5 or region=6 or region=7;

keep state;

run;

OR

data south;

set state_region_data(where=(region=5 or region=6 or region=7));

keep state;

run;

Data Set Manipulation• PROC SORT sorts data according to specified variables

• General Form: PROC SORT DATA=input_data_set <options>;BY Variable1 Variable2;

RUN;

• Sorts data according to Variable1 and then Variable2;

• By default, SAS sorts data in ascending order• Number low to high

• A to Z

• Use DESCENDING statement for numbers high to low and letters Z to A• BY City DESCENDING Population;

• SAS sorts data first by city A to Z and then Population high to low

Data Set Manipulation• Some Options

• NODUPKEY

• Eliminates observations that have the same values for the BY variables

• OUT=output_data_set

• By default, PROC SORT replaces the input data set with the sorted data set

• Using this option, PROC SORT creates a newly sorted data set and the input data set remains unchanged

Data Set Processing• Data Set Processing

• DATA steps read in data from existing data sets or raw data files one row at a time, like a loop

• DATA step reads data from the input data set in the following way:

1. Read in current row from input data set to Program Data Vector (PDV)

2. Process SAS statements

3. PDV to output data set

4. Set current row to the next row in the input data set

5. Iterate to Step 1

• One row at a time is processed

• Thus we cannot simply add the value of a variable in one row to the value in another row

Data Set Processing• Data Set Processing – Example

• Consider the following submitted code:

data state_sat_data2;

set state_sat_data;

if total>1000 then upper_ind=1;

else upper_ind=0;

run;


• Execution of the Data Stepdata state_sat_data2;

Current set state_sat_data;if total>1000 then upper_ind=1;else upper_ind=0;

run;

PDV

State_sat_data2

State Expend PT_ratio Salary Students Verbal Math Total Upper_ind

Alabama 4.405 17.2 31.144 8 491 538 1029 .




set state_sat_data;Current if total>1000 then upper_ind=1;

else upper_ind=0;run;

PDV

State_sat_data2


Alabama 4.405 17.2 31.144 8 491 538 1029 1




set state_sat_data;if total>1000 then upper_ind=1;else upper_ind=0;

Current run;

PDV

State_sat_data2


Alabama 4.405 17.2 31.144 8 491 538 1029 1


Alabama 4.405 17.2 31.144 8 491 538 1029 1


• Execution of the Data StepCurrent data state_sat_data2;


run;

PDV

State_sat_data2


Alabama 4.405 17.2 31.144 8 491 538 1029 .


Alabama 4.405 17.2 31.144 8 491 538 1029 1



Current set state_sat_data;if total>1000 then upper_ind=1;else upper_ind=0;

run;

PDV

State_sat_data2


Alaska 8.963 17.6 47.951 47 445 489 934 .


Alabama 4.405 17.2 31.144 8 491 538 1029 1



set state_sat_data;if total>1000 then upper_ind=1;

Current else upper_ind=0;run;

PDV

State_sat_data2


Alaska 8.963 17.6 47.951 47 445 489 934 0


Alabama 4.405 17.2 31.144 8 491 538 1029 1




Current run;

PDV

State_sat_data2


Alaska 8.963 17.6 47.951 47 445 489 934 0


Alabama 4.405 17.2 31.144 8 491 538 1029 1

Alaska 8.963 17.6 47.951 47 445 489 934 0

Combining Data Sets• Concatenating (or Appending)

• Stacks each data set upon the other

• If one data set does not have a variable that the other datasets do, the variable in the new data set is set to missing for the observations from that data set.

• General Form: DATA output_data_set;SET data1 data2;

run;

• PROC APPEND may also be used

Combining Data Sets• Merging Data Sets

• One-to-One Match Merge

• A single record in a data set corresponds to a single record in all other data sets

• Example: Patient and Billing Information

• One-to-Many Match Merge

• Matching one observation from one data set to multiple observations in other data sets

• Example: County and State Information

• Note: Data must be sorted before merging can be done

(PROC SORT)

Combining Data Sets• One-to-One Match Merge

• Usually need at least one common variable between data sets – matching purposes

• For the example, a patient ID would be needed

• Do not need common variable if all data sets are in exactly the same order

• General Form: DATA output_data_set;MERGE input_data_set1

input_data_set2;By variable1 variable2;

RUN;


• Example:

PerformanceGoals

Code:

DATA compare;

MERGE performance goals;

BY month;

difference=sales-goal;

RUN;

Month Sales

1 8223

2 6034

3 4220

Month Goal

1 9000

2 6000

3 5000


• Example cont.:

Compare

Month Sales Goal Difference

1 8223 9000 -777

2 6034 6000 34

3 4220 5000 -780

Combining Data Sets• One-to-Many Match Merge

• Requires at least one common variable in the data sets for matching purposes

• For the example, State information is in both the state and county files

• If two data sets have variables with the same name, the variables in the second data set will overwrite the variable in the first.

• General Form: DATA output_data_set;MERGE Data1 Data2 Data3;BY Variable1 Variable2;

RUN:

Combining Data Sets• One-to-Many Match Merge

• Example:

VideosAdjustment

Code:

DATA prices;

MERGE videos adjustment

BY category;

NewPrice=(1-adjustment)*sales;

RUN;

Category Sales

Aerobics 12.99

Aerobics 13.99

Aerobics 13.99

Step 12.99

Step 12.99

Weights 15.99

Category Adjustment

Aerobics .20

Step .30

Weights .25

Combining Data Sets• One-to-One Many Merge

• Example cont.:

VideosCategory Sales Adjustment NewPrice

Aerobics 12.99 .20 10.39

Aerobics 13.99 .20 11.19

Aerobics 13.99 .20 11.19

Step 12.99 .30 9.09

Step 12.99 .30 9.09

Weights 15.99 .25 11.99

Combining Data Sets• Assignment

Create the dataset work.state_data

Merge work.state_sat_data2 with work.state_region_data by the state variable

Combining Data Sets• Solution

proc sort data=state_sat_data2;

by state;

run;

proc sort data=state_region_data;

by state;

run;

data state_data;

merge state_sat_data2 state_region_data;

by state;

run;

Saving Data Sets• Save as SAS dataset (.sas7bdat)

LIBNAME libref “destination folder”;

DATA libref.filename;

SET current_name;

optional commands;

RUN;

• Other Formats

1. File Export Data

2. Specify SAS data set

3. Standard data source select the file format

4. Specify File Folder and Filename

Working With SAS Data Sets

Questions/Comments

Attendee Questions

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presentation and data short courses intro to sas download data to desktop 1

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