This presentation is about - Overview of SAS 9 Business Intelligence Platform, SAS Data Integration, Study Business Intelligence, overview Business Intelligence Information Consumers ,navigating in SAS Data Integration Studio, For more details Visit :- http://vibranttechnologies.co.in/sas-classes-in-mumbai.html

2. Introduction To
SAS

3. Need to know
– SAS environment
– SAS files (datasets, catalogs etc) & libraries
– SAS programs
How to:
 Get data in
 Manipulate data
 Get results out

4. SAS software
environment

5. SAS Windows (SAS 9)

6. Some (!) SAS windows
– Editor
Where code is written or imported, and submitted
– Log
What happened, including what went wrong
– Output
Results of program procedures that produce output
– Explorer
Shows libraries (SAS & Windows), their files, and where you can see data, graphs
– Results
Shows how the output is made up of tables, graphs, datasets etc
– Notepad
A useful place to keep bits of code

7. SAS software
programs

8. SAS Programs
data one;
input x y;
datalines;
-3.2 0.0024
-3.1 0.0033
. . .
;
run;
proc print data = one (obs = 5);
run;
proc means data = one;
run;
DATA step
creates SAS data set
PROC steps
process data in data set

9. SAS steps begin with a
 DATA statement
 PROC statement.
SAS detects the end of a step when it encounters
 a RUN statement (for most steps)
 a QUIT statement (for some procedures)
 the beginning of another step (DATA statement or PROC statement).
 Recommendation: use RUN; at end of each step
Step Boundaries

10. data seedwt;
input oz $ rad wt;
datalines;
Low 118.4 0.7
High 109.1 1.3
Low 215.2 2.9
run;
proc print data = two;
proc means data = seedwt;
class oz;
var rad wt;
run;
Step Boundaries

11. When you execute a SAS program, the output generated by SAS is divided
into two major parts:
SAS log contains information about the processing of
the SAS program, including any warning and
error messages.
SAS output contains reports generated by SAS
procedures and DATA steps.
Submitting a SAS Program

12. 1) Submit all (or selected) code by
 F4
 Click on the runner in the toolbar
1) Read log
2) Look in output window
if you expect code to produce output
3) Problems
 Bad syntax
 Missing ; at end of line
 Missing quote ’ at end of title (nasty!)
Recommended steps!

13. Improved output - HTML
Tools → Options → Preferences→ Results
Do this & resubmit code
Check HTML output in Results Window

14. SAS data
sets

15. SAS data sets
• SAS procedures (PROC … ) process data from SAS data sets
• Need to know (briefly!)
– What a SAS data set looks like
– How to get out data into a SAS data set

16. SAS data sets
• live in libraries
• have a descriptor part (with useful info)
• have a data part which is a rectangular table of character
and/or numeric data values (rows called observations)
• have names with syntax
<libname.>datasetname
libname defaults to work if omitted

17. work library
SAS data sets with a single part name like
oz, wp or mybestdata99
1) are stored in the work library
2) can be referenced e.g. as
mybestdata99 or work.mybestdata99
3) are deleted at end of SAS session!

18. Don’t loose your data!
Keep the SAS program that read the data from its
original source
. . . More later!

19. Viewing descriptor & data
/* view descriptor part */
proc contents data = wp;
run;
/* view data part */
proc print data = work.wp;
run;
Alternatively:
Use SAS Explorer: Open (for data) Properties (for descriptor)
Properties is not as clear as CONTENTS

20. SAS variables
There are two types of variables:
• character contain any value: letters, numbers, special characters, and
blanks.
Character values are stored with a length of 1 to 32,767 bytes (default is 8).
One byte equals one character.
• numeric stored as floating point numbers in 8 bytes
of storage by default.
Eight bytes of floating point storage provide space for 16 or 17 significant
digits.
You are not restricted to 8 digits.
Don’t change the 8 byte length!

21. SAS variables
The CONTENTS Procedure
Alphabetic List of Variables and Attributes
# Variable Type Len
1 oz Char 8
2 rad Num 8
3 wt Num 8
OUTPUT

22. SAS names
– for data sets & variables
• can be 32 characters long.
• can be uppercase, lowercase, or mixed-case
but are not case sensitive!
• must start with a letter or underscore. Subsequent characters can be letters,
underscores, or numeric digits
- no %$!*&#@ or spaces.

23. LastName FirstName JobTitle Salary
TORRES JAN Pilot 50000
LANGKAMM SARAH Mechanic 80000
SMITH MICHAEL Mechanic .
WAGSCHAL NADJA Pilot 77500
TOERMOEN JOCHEN 65000
A value must exist for every variable for each observation.
Missing values are valid values.
A numeric
missing value
is displayed as
a period.
A character missing
value is displayed as
a blank.
Missing Data Values

24. SAS syntax
• Not case sensitive
• Each ‘line’ usually begins with keyword
and ends with ;
• Common Errors:
– Forget ;
– Miss-spelt or wrong keyword
– Missing final quote in title
title ‘Woodpecker Habitat; /* quote mark missing */
title ‘Woodpecker Habitat’;

25. Comments
1. Type /* to begin a comment.
2. Type your comment text.
3. Type */ to end the comment.
• To comment selected typed text remember: Ctrl+/
• Alternative:
* comment ;

26. SAS
Creating a SAS data set

27. Getting data in!
Consider 2 methods
1) Data in program (briefly!)
2) Data in Excel workbook

28. Getting data in!
Data in program file:
data oz;
input oz $ rad wt;
datalines;
Low 118.4 0.7
High 109.1 1.3
Low 215.2 2.9
. . .
;
run;
Note:
1. oz is text variable so requires $
2. No missing values
3. Values of oz
• don’t contain spaces
• are at most 8 character long

29. Getting data in!
from Excel
• Use IMPORT wizard
saving program to reduce future clicking!

30. Creating new variables
Adding a new variable to an existing SAS data set (say
work.old)
1. Use set
2. Give definition of new variable
data new;
/* read data from work.old */
set old;
y2 = y**2;
ly = log(y);
ly_base10 = log10(y);
t1 = (treat = 1);
run;

31. Data set: work.new
Obs treat y ysquared logy logy_base10 t1
1 A 10.0 100.00 2.30259 1 0
2 A 100.0 10000.00 4.60517 2 0
3 B -10.0 100.00 . . 1
4 B 0.0 0.00 . . 1
5 B 0.1 0.01 -2.30259 -1 1

32. Data Screening

33. Data Screening
checking input data for gross errors
• Use PRINT procedure to scan for obvious anomalies
• Use MEANS procedure & examine summary table
– MAXIMUM, MINIMUM – reasonable?
– MEAN - near middle of range?
– MISSING VALUES - input or calculation error e.g. log(0)?
– CV (= 100*std.dev/mean) - < 10% for plant growth, between 12
& 30% for animal production variables, > 50% implies skewness
for any positive variable

34. Dealing with data errors
• Check original records
• Change mistakes in recording where the correct value is
beyond question
• Regenerate observations where possible – e.g.
reweigh sample, redo chemical analysis
• With a large body of data in an unbalanced design err on the
side of omitting questionable data
Do not proceed until data has been properly
cleaned – if necessary perform a number of
screening runs

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