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Presentation and Data http:// www.lisa.stat.vt.edu Short Courses Intro to SAS Download Data to Desktop. Introduction to SAS Part 1. Mark Seiss , Dept. of Statistics. Reference Material. The Little SAS Book – Delwiche and Slaughter SAS Programming I: Essentials - PowerPoint PPT Presentation
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Presentation and Data
http://www.lisa.stat.vt.edu
Short Courses
Intro to SAS
Download Data to Desktop
1
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 Programs2. SAS Data Sets and Data
Libraries3. 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 Librarieslibref.filename
• Accessing SAS data sets within SAS Data LibrariesExample: DATA new_data_set;
set libref.filename;run;
• Creating SAS data sets within SAS Data LibrariesExample: 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 methods1. Importing existing data sets using Import menu option2. Importing existing raw data in SAS program3. Manually entering raw data in SAS program4. Manually entering raw data using Table Editor
Creating SAS Data Sets• Using the import data menu option
1. File Import Data2. Standard data source select the file format3. Specify file location or Browse to select file4. 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)
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 SetsImport 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 Information2. Data Set Manipulation3. Data Set Processing4. 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_indIf total > 1000 then upper_ind=1Otherwise 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 statements3. PDV to output data set4. Set current row to the next row in the input data set5. 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;
Data Set Processing• Data Set Processing – Example
• 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 .
State Expend PT_ratio Salary Students Verbal Math Total Upper_ind
Data Set Processing• Data Set Processing – Example
• Execution of the Data Stepdata state_sat_data2;
set state_sat_data;Current 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 1
State Expend PT_ratio Salary Students Verbal Math Total Upper_ind
Data Set Processing• Data Set Processing – Example
• Execution of the Data Stepdata state_sat_data2;
set state_sat_data;if total>1000 then upper_ind=1;else upper_ind=0;
Current 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 1
State Expend PT_ratio Salary Students Verbal Math Total Upper_ind
Alabama 4.405 17.2 31.144 8 491 538 1029 1
Data Set Processing• Data Set Processing – Example
• Execution of the Data StepCurrent data state_sat_data2;
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 .
State Expend PT_ratio Salary Students Verbal Math Total Upper_ind
Alabama 4.405 17.2 31.144 8 491 538 1029 1
Data Set Processing• Data Set Processing – Example
• 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
Alaska 8.963 17.6 47.951 47 445 489 934 .
State Expend PT_ratio Salary Students Verbal Math Total Upper_ind
Alabama 4.405 17.2 31.144 8 491 538 1029 1
Data Set Processing• Data Set Processing – Example
• Execution of the Data Stepdata state_sat_data2;
set state_sat_data;if total>1000 then upper_ind=1;
Current else upper_ind=0;run;
PDV
State_sat_data2
State Expend PT_ratio Salary Students Verbal Math Total Upper_ind
Alaska 8.963 17.6 47.951 47 445 489 934 0
State Expend PT_ratio Salary Students Verbal Math Total Upper_ind
Alabama 4.405 17.2 31.144 8 491 538 1029 1
Data Set Processing• Data Set Processing – Example
• Execution of the Data Stepdata state_sat_data2;
set state_sat_data;if total>1000 then upper_ind=1;else upper_ind=0;
Current run;
PDV
State_sat_data2
State Expend PT_ratio Salary Students Verbal Math Total Upper_ind
Alaska 8.963 17.6 47.951 47 445 489 934 0
State Expend PT_ratio Salary Students Verbal Math Total Upper_ind
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;
Combining Data Sets• One-to-One Match Merge
• Example:PerformanceGoals
Code:DATA compare;
MERGE performance goals;BY month;difference=sales-goal;
RUN;
Month Sales1 8223
2 6034
3 4220
Month Goal1 9000
2 6000
3 5000
Combining Data Sets• One-to-One Match Merge
• Example cont.:Compare
Month Sales Goal Difference1 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:Videos
Adjustment
Code:DATA prices;
MERGE videos adjustmentBY 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.:Videos
Category 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_dataMerge 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 Data2. Specify SAS data set3. Standard data source select the file format4. Specify File Folder and Filename
Working With SAS Data Sets
Questions/Comments
Attendee Questions
If time permits