SAS Programming

concatenating data sets

2008-04-22T12:34:00.000-07:00

data thrdcls ;
input name$ age sex grade$;
datalines;
ali 21 0 2.78
hasan 22 0 3.21
yasemin 21 1 3.52
run;
data thrdcls (rename=(tmp=grade) drop=grade);
set thrdcls;
tmp=input(grade,5.2);
run;
data frthcls;
input name$ age sex grade;
datalines;
mustafa 21 0 3.15
fatam 22 1 2.95
emre 21 0 2.25
selin 22 1 2.70
run;
data std;
set thrdcls frthcls;
run;
proc print; run;

conditional processing ( if )

2008-04-22T11:36:00.000-07:00

example(general if statement)

data family ;
infile datalines dlm=',' missover;
input fname:$20. rship age sex occup income dollar5.;
format income dollar5.;
datalines;
kaya,1,50,0,1,$1500
kaya,2,45,1,2
kaya,3,30,0,1,$2000
sahin,1,60,0,2
sahin,2,50,1,1,$1750
sahin,3,15,1,0,
run;
proc print data=family; run;
data incomeytl;
set family ;
incomeytl=income*1.2;
run;
proc print data=incomeytl; run;
data job;
set family;
if occup eq 1 then status='working';
else status='not working';
run;
proc print data=job; run;

example(length statement)

data job;
set family;
length status $ 20;
if occup eq 1 then status='working';
else if occup=2 or occup=0 then status='not working';
run;
proc print data=job; run;

example(do and end)

data job_status;
set family;
length status $ 20;
if occup eq 1 then status='working';
else do
status='not working';
income=0;
end;
run;
proc print data=job_status; run;

example (multiple if else statement)

data job_status;
set family;
length status $ 20;
if occup eq 1 then status='working';
else if occup=2 then do
status='not working';
income=0;
end;
else if occup= 0 then do
status ='student';
income=0;
end;
run;
proc print data=job_status; run;

example (more than one output dataset)

data working not_working student;
set family;
if occup=0 then output student;
else if occup=1 then output working;
else if occup=2 output not_working;
run;
proc print data=not_working; run;

reading data that requires spedcial intructions(Formatted input)

2008-04-20T13:55:00.000-07:00

SAS can read numeric data that is in special formats such as binary, packed decimals, date/time.

example: formatted input
data std;
input name$ 1-15 age 17-18 class 20 sex 22 grade comma5.2;
datalines;
ali sahin 21 4 0 3,11
hüseyin karatas 20 2 1 2,90
;
run;
proc print;
run;

note that in thi example numbers for grade variable has character ',' ( with blank ti has 5 place and the value with 2 decimals so comma5.2
if we have 31,12 we have to write comma6.2

example: formatted input

data std;
input name$ 1-15 age 17-18 class 20 sex 22 grade 24-27 birthdate ddmmyy11.;
datalines;
ali sahin 21 4 0 3.11 21/02/1986
hüseyin karatas 20 2 1 2.90 13/06/1987
;
run;
proc print;
run;

reading data that is aligned in columns(column input) inline

2008-04-20T13:46:00.000-07:00

in column input in input statement list the variable names and specify column positions that identify the location of the corresponding data fields. you can use column input when your raw data is in fixed columns an does not require the use of informats to be read.

example: column input
data std;
input name$ 1-15 age 17-18 class 20 sex 22 grade 24-27;
datalines;
ali sahin 21 4 0 3.1
hüseyin karatas 20 2 1 2.90
;
run;
proc print;
run;

reading unaligned data(list input) inline

2008-04-20T13:41:00.000-07:00

example: comma delimited data
data std;
infile datalines dlm=',' ;
input name$ age class sex grade;
datalines;
sahin,21,4,0,3.1
dasd,20,2,1,2.90
;
run;
proc print;
run;

Creating and Using Dummy Variables

2007-10-18T14:01:00.000-07:00

A dummy variable is a numerical variable used in regression analysis to represent subgroups of the sample in your study. Dummy variable is used to distinguish different treatment groups. They are useful because they enable us to use a single regression equation to represent multiple groups. This means we don't need to write out separate equation models for each subgroup. If we have "n" subgroups we will have (n-1) dummy variables.
EX:
consider this simple data file having 9 subjects in 3 groups with a score iv dv.

sas program :

data dummy;
input sub iv dv;
cards;
1 1 48
2 1 49
3 1 50
4 2 17
5 2 20
6 2 23
7 3 28
8 3 30
9 3 32
;
run;
data dummy2;
set dummy;
if (iv=1) then iv1=1; else iv1=0;
if (iv=2) then iv2=1; else iv2=0;
if (iv=3) then iv3=1; else iv3=0;
run;
proc reg data=dummy2;
model dv=iv1 iv2;
run;

simple anova example

2007-10-18T13:52:00.000-07:00

EX: the response time in milliseconds was determined for 3 different types of circuits.

sas program:

data time;
input circuit $ Time @@;
cards;
A 9 A 12 A 10 A 9 A 15
B 20 B 21 B 23 B 17 B 30
C 6 C 5 C 8 C 16 C 7
;
run;
proc anova data=time;
class circuit ;
model time= circuit;
means circuit / duncan scheffe alpha= 0.01 tukey lines;
run;

Analysis of variance

2007-10-18T13:41:00.000-07:00

Proc Anova General Form

proc anova data=dataset;
by variables;
class variables;
model dependent variable=independent variables;
means effects ;

the class statement is used to designate which variables are factors in the model. it can have numeric values or character values.
the model statement has the form dependent variable=factorial.
the means statement requests multiple comparisons. list each variable for which you want multiple comparisons after means.

examples

2007-10-04T09:36:00.000-07:00

example1:
The data set “fitness.dat” is required in this problem. Consider the simple straight-line regression of maxpluse (Y) on runpulse (X).

Physical Fitness Data
These measurements were made on 31 men involved in a physical
fitness course at NC State University. The variables are
AGE (years)
WEIGHT (KG)
OXY (Oxygen uptake rate, ML per KG body weight per minute)
RUNTIME (time to run 1.5 miles, minutes)
RSTPULSE (heart rate while resting)
RUNPULSE (heart rate while running)
MAXPULSE (maximum heart rate while running)

Determine the ANOVA table
Test the significance of the straight line using an F test.
Compute the estimated regression line.
Plot the data and overlay the estimated regression line.
Comment on how well the line fits the data. (HINT: Comment on everything that you see on your output.)
Perform a multiple regression analysis and comment on the output. (Y=maxpulse; X1=age, X2=weight, X3=oxy, X4=runtime, X5=rstpluse, X6=runpulse)

answer:

filename data 'c:\abc\fitness.dat';
data dataset;
infile data;
input age weight oxy runtime rstpulse runpulse maxpulse;
run;
proc print data=dataset;
run;
proc anova data=dataset;
class runpulse;
model maxpulse=runpulse;
means runpulse;
run;
proc reg data=dataset;
model maxpulse=runpulse;
run;
proc reg data=dataset;
model maxpulse=age weight oxy runtime rstpulse runpulse;
run;
proc reg data=dataset;
model maxpulse=runpulse;
plot predicted.*runpulse='p' maxpulse*runpulse='*' / overlay;
run;

example 2:

DATA D1;
INPUT STATUS $ SEX $ NUMBER;
DATALINES; I BB 34
I BG 26
I GG 15
S BB 14
S BG 22
S GG 36 ;
PROC FREQ DATA=D1;
TABLES STATUS*SEX / ALL;
WEIGHT NUMBER;
TITLE1 'JOHN DOE';
RUN;

the result of this program SAS gives some tables. SAS program constructs a table which counts the number of observations with respect to status and sex in the data.

advanced examples

2007-10-04T09:29:00.000-07:00

example:

In a class of 88 students, the exam score of “Mechanics (X1)”, “Vector Analysis (X2)”, “Algebra (X3)”, “Calculus(X4)” and “Statistics (X5)” are given in the file exam.dat.

Calculate the coefficient of correlation matrix of all variables.
Calculate the descriptive statistics of “Mechanics” and “Statistics” by using PROC UNIVARATE with options “normal” and “plot”
Answer the following parts:

i) Is the average of Algebra results different than the average of Vector Analysis?

ii) Does any difference between the averages of Calculus and Statistics exist?

(HINT: Think of t-test for two samples’ mean.)

Testing Differences Between Two Means

2007-10-04T09:22:00.000-07:00

For a paired t-test use Proc means or proc univariate. The paired t-test looks at the differences between two measures that dependent or correlated and tests whether or not the mean difference equals zero. To use either procedure for a paired t-test , create a new variable that is the difference between the two measures and test whether the difference is equal to 0.

proc t-test procedure

2007-10-04T09:13:00.000-07:00

proc ttest procedure tests whether two means are equal. It reports p values for the case where the two variances are equal which is called the two sample t-test or the unequal variances t-test and for the case where the two variances are equal ,the pooled t-test. For the two sample t-test it computes the approximate degrees of freedom. Proc ttest also reports of testing whether the two variances are equal for deciding which test is appropriate.
The Class statement identifies the variable that divides the data set into two groups. The CLASS variable must have only two values(can be either numeric or character)

Proc ttest general form:
....
...
....
...
run;
proc ttest data=dataset;
by variables;
class variable;
var variales;
run;

one simple example

2007-10-04T08:10:00.000-07:00

EXAMPLE:
Plant wages: Weekly wages($) of 60 wage earners in a plant during the week of Jan 5 were as follows:
609 601 592 604 569 625 655 582 583 610 582 589 586 625 610 598 608 600 595 598 589 621 605 650 610 602 627 600 599 576 591 621 603 597 605 565 627 579 601 610 578 615 575 646 587 572 618 645 575 609 631 631 653 615 607 635 586 637 609 585

Use sas to produce descriptive statistics. comment on the distribution of the given data set.

answer:
the program must be

data plant_wages;
INPUT id 1-2 wages 4-6;
cards;
1 609
2 601
3 592
4 604
5 569
6 625
7 655
8 582
9 583
10 610
11 582
12 589
13 586
14 625
15 610
16 598
17 608
18 600
19 595
20 598
21 589
22 621
23 605
24 650
25 610
26 602
27 627
28 600
29 599
30 576
31 591
32 621
33 603
34 597
35 605
36 565
37 627
38 579
39 601
40 610
41 578
42 615
43 575
44 646
45 587
46 572
47 618
48 645
49 575
50 609
51 631
52 631
53 653
54 615
55 607
56 635
57 586
58 637
59 609
60 585
;
proc sort;
by wages;
run;
proc means;
var wages;
run;
proc univariate normal plot;
var wages;
run;

comment:
The distribution of these data set is normal.
Ho: data is normally dist.
H1: data is not normally dist.
Prob
In the box plot median and mean number are approx same.

proc univariate statement

2007-10-04T07:59:00.000-07:00

proc univariate generates the descriptive statistics. (skewness, kurtosis, t-test, sign test, rank test, median, mode etc...)
the plot option: will generate a stem leaf plot box plot and a normal probability plot if we are dealing with large data sets a horizontal bar chart may be produced instead of stem and leaf plot.
the normal option: generates a statistic to test for normality and its p-value. here our hypothesis tests are
Ho: the data set is distributed normally
H1: the data set is not distributed normally
by looking p-value we can/not reject hypothesis.
if p value< style="font-weight: bold;">proc univariate general form :
....
....
...
run;
proc univariate data=dataset ;
by variable ;
var variable;
run;

proc means statement

2007-10-04T07:43:00.000-07:00

proc means is a procedure to use when you are only interested in the basic descriptive statistics.
proc means general statement:
...
...
..
run;
proc means data=dataset ;
by variables;
var variables;
run;
we have some options there. when we write only proc means SAS system gives us sample size, minimum maximum values,average and standard deviation.
some useful options:
range: the range
sum:the sum
var:the variance
stderr:the standard error of the mean
prt: p value for this test different from "0"
clm: two sided 95% confidence interval

example:

DATA WAGES;
INPUT SUBJECT WAGE;
DATALINES;
1 609
2 601
3 592
4 604
5 569
6 625
7 655
8 582
9 583
10 610
;
PROC MEANS DATA=WAGES clm;
VAR WAGE;
RUN;

this program gives us 10 workers' mean wage ,standard deviation min and max

if we add clm it gives 2 sided confidence interval for the mean.

proc print statement

2007-10-03T08:36:00.000-07:00

this procedure tells SAS to print out certain variables in the data set.
proc print general form:

data dataset;
input variales;
datalines;
...
...
...
;
run;
proc print data=dataset;
by variables;
var variables;
run;

the keyword var is short for variable list. You list the variables you want to print after var in the order you want them printed.

note: when you write input statement if your variable data are not number you write "$"after variable for example
data dataname;
input name $ age city $ ---> name and city variables are not number

general sas structure

2007-10-03T08:31:00.000-07:00

general form of simple data step

data dataset; ---> the data statement names the data set.
input variales;----> input is the keyword that defines the names of variables in the data set.
datalines;----->this statement signals the begining of variables
the lines of data
...
...
...
;
run;

sas introduction

2007-10-03T07:46:00.000-07:00

When SAS program is started it has 4 main windows open:
1-the program editor
2-the log window
3-the output window
4-the explorer window
we write all codes to editor window.
SAS is organized into two steps. there are two types of steps DATA steps, which put data in a form that the SAS program can use an PROC steps which use procedures to do sth. to the data, such as sorting it, analyzing it or printing it.
A semicolon (;) is required to denote the end of a statement. spacing does not matter.You can put as many spaces between word/ keywords of the statements as you like.

what is sas?

2007-10-03T07:39:00.000-07:00

The SAS system is a combination of programs originally designed to perform statistical analysis of data. Comparing to the other programs SAS system provides very many statistical and nonstatistical functions. usage of SAS is very simple comparing to the others.