CHAPTER 7 COMBINING AND MANAGING SAS DATA SETS
a. Multiple input data sets
b. SAS special variables
c. Reshaping SAS data sets
d. Data library management
Revised Fall 2000
7-1
Multiple Input Data Sets
Data Sets can be combined in various ways using the following SAS statements to specify the input
data sets:
v SET
Reads observations from one or more SAS data sets.
v MERGE Joins observations from multiple SAS data sets.
v UPDATE Modifies an existing "Master File" SAS data set with a
transaction file" SAS data set creating a new "Master File"
NOTE: These statements can be controlled by using the BY statement in the DATA step. When the
BY statement is used, all the input data sets must contain the BY variables and must be
sorted by the BY variables. Observations that have the same values of all BY variables are
said to belong to the same BY group.
7-2
The SET Statement
The SET statement names the SAS input data sets from which observations will be read.
SYNTAX
SET SASDS1 SASDS2....;
where
SASDS is a Name of the Form LIBRARY.DATASET.
NOTES:
v By default SAS will concatenate all data sets in the order listed in the SET statement. All
observations will be read from the first data set listed, then all observations will be read from
the second data set listed, and so on.
v When used with a BY statement, SAS will interleave observations from the data sets listed in
the SET statement. The observations are selected based on the sort order determined by the BY
variables. All input data sets must be sorted by the BY variables and the resulting output data
set will be sorted by the BY variables as well.
v Up to 50 data sets can be listed in a single SET statement.
7-3
Concatenation Example
Create data sets for short and tall students;
DATA WORK.SHORT;
SET CLASSLIB.CLASS;
If HT LT 60;
RUN;
DATA WORK.TALL;
SET CLASSLIB.CLASS;
IF HT GE 60;
RUN;
*CONCATENATE THE TWO DATA SETS;
DATA WORK.BOTH;
SET WORK.SHORT WORK.TALL;
RUN;
DATA SET WORK.BOTH
NAME
Piggy M
Frog K
Gonzo
Christiansen
Hosking J
Helms R
SEX
F
M
AGE
.
3
14
37
31
41
M
M
M
DATA SET WORK.SHORT
NAME
Piggy M
Frog K
Gonzo
SEX
F
M
.
AGE
.
3
14
HT
8
12
25
HT
48
12
25
71
70
74
WT
.
1
45
195
160
195
DATA SET WORK.TALL
WT
.
1
45
NAME
Christiansen
Hosking J
Helms R
.
7-4
SEX
M
M
M
AGE
37
31
41
HT
71
70
74
WT
195
160
195
Interleave Example
* Sort data sets by NAME;
Proc sort data=work.short;
By Name;
RUN;
Proc sort data=tall;
By Name;
RUN;
*Interleave the two data sets;
Data work.bothsort;
Set short tall;
By Name,
RUN;
DATA SET WORK.SHORT
NAME
Frog K
Gonzo
Piggy M
SEX
M
F
AGE
3
14
.
HT
12
25
48
DATA SET WORK.TALL
WT
1
45
.
NAME
Christiansen
Helms R
Hosking J
SEX
M
M
M
AGE
37
41
31
HT
71
74
70
WT
195
195
160
DATA SET WORK.BOTHSORT
NAME
Christiansen
Frog K
Gonzo
Helms R
Hosking J
Piggy M
SEX
M
M
AGE
37
3
14
41
31
.
M
M
F
HT
71
12
25
74
70
48
WT
195
1
45
195
160
.
The MERGE Statement
The MERGE statement joins observations from the input data sets into a single observation in the
output data set.
SYNTAX
7-5
MERGE SASDS1
SASDS2....;
Where
SASDS...are data set names.
NOTES:
v One-to-one merging is performed when no BY statement is used. The first observation in each
data set is joined to all the other first observations, the second to the second, etc. One-to-one
merging should be performed with caution, especially when the data sets contain different
numbers of observations.
v Match merging (with the BY statement) joins the observations only when the values of the BY
variables match.
v One-to-many match merging allows variable values to be spread or "splayed" across all
observations with the same BY values.
v Many-to-many match merging joins observations one to one within the BY group until one data
set reaches the last observation within the current BY group. This observation is then splayed
across the remaining observations in the BY group.
v Up to 50 SAS data sets can be listed in a single MERGE statement.
7-6
One-to-One Merge
Combine the LIST and DEMO data sets to produce the NEWCLASS data set.
WORK.LIST
NAME
Christiansen
Frog K
Gonzo
Helms R
Hosking J
Piggy M
WORK.DEMO
SEX
M
M
M
M
F
NAME
Christiansen
Frog K
Gonzo
Helms R
Hosking J
Piggy M
AGE
37
3
14
41
31
.
HT
71
12
25
74
70
48
WT
195
1
45
195
160
.
DATA NEWCLASS;
MERGE LIST DEMO;
RUN;
WORK.NEWCLASS
NAME
Christiansen
Frog K
Gonzo
Helms R
Hosking J
Piggy M
SEX
M
M
AGE
37
3
14
41
31
.
M
M
F
7-7
HT
71
12
25
74
70
48
WT
195
1
45
195
160
.
ONE-TO-ONE MERGE
Join two data sets with unequal numbers of observations.
WORK.LIST2
NAME
Christiansen
Frog K
Gonzo
Hosking J
Piggy M
WORK.DEMO2
SEX
M
M
M
F
NAME
Christiansen
Frog K
Helms R
Piggy M
AGE
37
3
41
.
HT
71
12
74
48
WT
195
1
195
.
DATA NEWCLASS;
MERGE LIST2 DEMO2;
RUN;
WORK.NEWCLASS
NAME
Christiansen
Frog K
Helms R
Piggy M
Piggy M
SEX
M
M
AGE
37
3
41
.
.
M
F
7-8
HT
71
12
74
48
.
WT
195
1
195
.
.
MATCH MERGING
Join only those observations with the same values of the BY variables. Note that both input data
sets are sorted by the BY variable.
WORK.LIST2
NAME
Christiansen
Frog K
Gonzo
Hosking J
Piggy M
WORK.DEMO2
SEX
M
M
M
F
NAME
Christiansen
Frog K
Helms R
Piggy M
AGE
37
3
41
.
HT
71
12
74
48
WT
195
1
195
.
DATA NEWCLASS;
MERGE LIST2 DEMO2;
BY NAME;
RUN;
WORK.NEWCLASS
NAME
Christiansen
Frog K
Gonzo
Helms R
Hosking J
Piggy M
SEX
M
M
AGE
37
3
.
41
.
.
M
F
7-9
HT
71
12
.
74
.
48
WT
195
1
.
195
.
.
ONE TO ONE MATCH MERGE EXAMPLE
Put blood pressure and lipid data into one file.
WORK.SBP
WORK.LIPIDS
ID
101
102
103
104
ID
101
102
103
105
SBP
83
100
120
118
HDL
45
50
60
65
DATA ALL;
MERGE SBP LIPIDS ;
BY ID
RUN;
WORK.ALL
ID
101
102
103
104
105
SBP
83
100
120
118
.
HDL
45
50
60
.
65
LDL
180
200
210
.
220
7-10
LDL
180
200
210
220
ONE TO ONE MATCH MERGE EXAMPLE
MERGE PRERAND FILE WITH POST RAND FILE
Problem: Both files contain common variables other than the by variables. Notice the resulting
Data set.
WORK.PRERAND
ID
101
102
103
104
HDL
40
45
55
60
WORK.POSTRAND
LDL
200
210
215
220
ID
101
102
103
105
HDL
45
50
60
65
DATA ALL;
MERGE PRERAND
POSTRAND;
BY ID ;
RUN;
WORK.ALL
OBS
1
2
3
4
5
ID
HDL
LDL
101
102
103
104
105
45
50
60
60
65
180
200
210
220
230
7-11
LDL
180
200
210
230
ONE TO ONE MATCH MERGE EXAMPLE
MERGE PRERAND FILE WITH POST RAND FILE
Problem: Both files contain common variables other than the by variables.
Solution: Use the rename data option
WORK.PRERAND
ID
101
102
103
104
HDL
40
45
55
60
WORK.POSTRAND
LDL
200
210
215
220
ID
101
102
103
105
HDL
45
50
60
65
LDL
180
200
210
230
DATA ALL;
MERGE PRERAND(RENAME=(HDL=HDL0 LDL=LDL0))
POSTRAND(RENAME=(HDL=HDL1 LDL=LDL1)) ;
BY ID ;
HDLDIFF = HDL1 - HDL0 ;
LDLDIFF = LDL1 - LDL0 ;
RUN;
WORK.ALL
ID
101
102
103
104
105
HDL0
40
45
55
60
.
LDL0
200
210
215
220
.
HDL1
45
50
60
.
65
LDL1
180
200
210
.
230
7-12
HDLDIFF
5
5
5
.
.
LDLDIFF
-20
-10
-5
.
.
EXAMPLE: ONE TO ONE MERGE WITH TWO BY VARIABLES
LDL
OBS
ID
1
2
3
4
5
6
7
8
9
10
11
12
1
1
1
2
2
2
3
3
4
4
5
5
62
63
64
65
66
SBP
VISIT
LDL
OBS
1
2
3
1
2
3
1
3
1
2
1
2
130
135
140
145
150
155
160
165
170
175
180
185
1
2
3
4
5
6
7
8
9
ID
1
1
1
2
2
3
3
3
4
VISIT
SBP
1
2
3
1
3
1
2
3
3
100
105
110
115
120
122
125
130
135
data all ;
merge sbp
lipids ;
by id visit ;
run;
NOTE: The data set WORK.ALL has 14 observations and 4 variables.
NOTE: The DATA statement used 1.12 seconds.
ALL
OBS
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
1
1
2
2
2
3
3
3
4
4
4
5
5
VISIT
SBP
LDL
1
2
3
1
2
3
1
2
3
1
2
3
1
2
100
105
110
115
.
120
122
125
130
.
.
135
.
.
130
135
140
145
150
155
160
.
165
170
175
.
180
185
7-13
ONE-TO-MANY MATCH MERGING
v One data set in a MERGE statement may contain multiple observations with the same values of
the BY variables within each BY group. MERGE joins the observation from the "one" data set
with each observation in the "many" data set.
v In match merging, the program data vector is initialized to missing only at the beginning of a
new BY group. This allows information from the "one" data set to be splayed across the
corresponding BY group observations in the "many" data set.
7-14
ONE TO MANY MATCH MERGE EXAMPLE
WORK.LIST
NAME
CHRISTIANSEN
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
WORK.HW
SEX
M
M
NAME
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
FROG K
FROG K
FROG K
GONZO
HOSKING J
PIGGY M
PIGGY M
M
M
F
HWNUM
1
2
4
5
1
2
3
1
1
2
3
DATA GRADES;
MERGE LIST HW;
BY NAME;
RUN;
NAME
PDV
|
SEX
|
HWNUM
|
SCORE
|
|
WORK.GRADES
NAME
SEX
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
FROG K
FROG K
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
PIGGY M
M
M
M
M
M
M
M
NUMBER
1
2
4
5
1
2
3
1
.
1
2
3
M
M
F
F
7-15
SCORE
10
3
9
10
10
10
8
.
.
10
10
10
SCORE
10
3
9
10
10
10
8
.
10
10
10
ONE TO MANY MATCH MERGE EXAMPLE
When the same variables (other than BY variables) are in more than one data set, the value of the
variable from the last data set mentioned in the MERGE statement is used.
WORK.GRADES
NAME
SEX
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
FROG K
FROG K
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
PIGGY M
M
M
M
M
M
M
M
M
M
F
F
WORK.FIXHW
HWNUM SCORE
1
2
4
5
1
2
3
1
.
1
2
3
10
3
9
10
10
10
8
.
.
10
10
10
NAME
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
FROG K
HOSKING J
DATA GRADES2;
MERGE GRADES FIXHW;
BY NAME NWNUM;
RUN;
WORK.GRADES2
NAME
SEX
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
FROG K
FROG K
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
PIGGY M
M
M
M
M
M
M
M
M
M
M
F
F
HWNUM
1
2
2
4
5
1
2
3
1
.
1
2
3
SCORE
9
10
2
.
10
10
.
8
.
.
.
10
10
NOTE: Missing values in FIXHW overwrite values in GRADES.
7-16
HWNUM SCORE
1
2
2
4
2
1
9
10
2
.
.
.
ONE TO MANY MATCH MERGE EXAMPLE
SBP
OBS
1
2
3
4
5
6
7
8
9
ID
1
1
1
2
2
3
3
3
4
DEM
VISIT
SBP
OBS
1
2
3
1
3
1
2
3
3
100
105
110
115
120
122
125
130
135
1
2
3
4
5
ID
1
2
3
4
5
DATA ALL ;
MERGE SBP
DEM;
BY ID ;
RUN;
ALL
OBS
ID
1
2
3
4
5
6
7
8
9
10
1
1
1
2
2
3
3
3
4
5
VISIT
SBP
SEX
1
2
3
1
3
1
2
3
3
.
100
105
110
115
120
122
125
130
135
.
F
F
F
M
M
F
F
F
F
M
AGE
30
30
30
35
35
40
40
40
45
50
7-17
SEX
F
M
F
F
M
AGE
30
35
40
45
50
ONE TO MANY MATCH MERGE EXAMPLE
SBP
OBS
1
2
3
4
5
6
7
8
9
ID
1
1
1
2
2
3
3
3
4
DEM
VISIT
SBP
OBS
1
2
3
1
3
1
2
3
3
100
105
110
115
120
122
125
130
135
1
2
3
4
5
ID
1
2
3
4
5
DATA ALL ;
MERGE SBP
DEM;
BY ID ;
AGE=AGE*10 ;
RUN;
ALL
OBS
ID
1
2
3
4
5
6
7
8
9
10
1
1
1
2
2
3
3
3
4
5
VISIT
SBP
SEX
AGE
1
2
3
1
3
1
2
3
3
.
100
105
110
115
120
122
125
130
135
.
F
F
F
M
M
F
F
F
F
M
300
3000
30000
350
3500
400
4000
40000
450
500
What’s wrong with age?
7-18
SEX
F
M
F
F
M
AGE
30
35
40
45
50
ONE TO MANY MATCH MERGE EXAMPLE
Merge the department specific means onto each record.
24 PROC MEANS DATA=SC.SALES NWAY;
25 CLASS DEPT ;
26 VAR COST ;
27 OUTPUT OUT=OUT1 MEAN=MCOST ;
28 RUN;
NOTE: The data set WORK.OUT1 has 2 observations and 4 variables.
NOTE: The PROCEDURE MEANS used 0.56 seconds.
29
30 DATA SALES2 ;
31
MERGE SALES
32
OUT1 ;
33
BY DEPT ;
34 RUN;
NOTE: The data set WORK.SALES2 has 50 observations and 9 variables.
NOTE: The DATA statement used 0.78 seconds.
35
36 PROC PRINT ;
37 RUN;
NOTE: The PROCEDURE PRINT used 0.44 seconds.
Analysis Variable : COST
DEPT
N Obs
N
Mean
Std Dev
Minimum
Maximum
-----------------------------------------------------------------------FURS
6
6
203.6700000
22.1033337
180.0100000
240.0000000
SHOES
44
44
35.0363636
4.9674752
23.7600000
43.1800000
------------------------------------------------------------------------
7-19
OBS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
DEPT
CLERK
FURS
FURS
FURS
FURS
FURS
FURS
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
SHOES
BURLEY
BURLEY
AGILE
BURLEY
BURLEY
AGILE
CLEVER
AGILE
CLEVER
CLEVER
AGILE
AGILE
BURLEY
CLEVER
CLEVER
CLEVER
CLEVER
CLEVER
AGILE
CLEVER
CLEVER
CLEVER
BURLEY
CLEVER
CLEVER
AGILE
CLEVER
CLEVER
AGILE
CLEVER
AGILE
CLEVER
CLEVER
CLEVER
CLEVER
AGILE
CLEVER
AGILE
AGILE
CLEVER
CLEVER
CLEVER
BURLEY
CLEVER
CLEVER
AGILE
BURLEY
BURLEY
CLEVER
CLEVER
PRICE
599.95
800.00
590.00
499.95
700.00
700.00
99.95
95.00
65.00
65.00
49.95
69.95
69.95
84.95
54.95
95.00
139.95
59.95
54.95
94.95
65.00
89.95
75.00
54.95
65.00
74.95
39.95
85.00
95.00
44.95
75.00
55.00
104.95
49.95
94.95
89.95
74.95
84.95
59.95
84.95
94.95
55.00
59.95
84.95
54.95
64.95
54.95
55.00
64.95
129.95
COST
WEEKDAY
180.01
240.00
182.00
200.01
210.00
210.00
41.21
40.49
33.44
33.44
28.07
34.93
34.93
38.65
30.00
40.49
42.96
31.78
30.00
40.48
33.44
39.63
36.31
30.00
33.44
36.30
23.76
38.66
40.49
25.99
36.31
30.02
41.83
28.07
40.48
39.63
36.30
38.65
31.78
38.65
40.48
30.02
31.78
38.65
30.00
33.43
30.00
30.02
33.43
43.18
THR
MON
SAT
SAT
THR
WED
TUE
WED
WED
WED
THR
THR
THR
SAT
SAT
SAT
MON
MON
TUE
TUE
TUE
TUE
WED
WED
WED
THR
THR
FRI
SAT
SAT
TUE
TUE
WED
FRI
FRI
SAT
SAT
MON
MON
MON
TUE
TUE
WED
WED
THR
FRI
FRI
FRI
FRI
FRI
7-20
DAY
5
9
14
14
19
25
3
4
4
4
5
5
5
7
7
7
9
9
10
10
10
10
11
11
11
12
12
13
14
14
17
17
18
20
20
21
21
23
23
23
24
24
25
25
26
27
27
27
27
27
_TYPE_
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
_FREQ_
6
6
6
6
6
6
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
44
MCOST
203.670
203.670
203.670
203.670
203.670
203.670
35.036
35.036
35.036
35.036
35.036
35.036
35.036
35.036
35.036
35.036
35.036
35.036
35.036
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
35.0364
ONE TO MANY MATCH MERGE EXAMPLE
Merge the overall mean onto each record.
20
21
22
23
PROC MEANS DATA=SC.CLASS NOPRINT ;
VAR WT ;
OUTPUT OUT=OUT1 MEAN=MWT ;
RUN;
NOTE: The data set WORK.OUT1 has 1 observations and 3 variables.
NOTE: The PROCEDURE MEANS used 0.38 seconds.
24
25
26
27
28
29
30
31
32
33
/* MERGE MEAN ONTO EVERY OBSERVATION */
DATA NEW ;
SET SC.CLASS ;
IF _N_=1 THEN SET OUT1 ;
DROP _TYPE_ _FREQ_ ;
RUN;
NOTE: The data set WORK.NEW has 6 observations and 6 variables.
NOTE: The DATA statement used 0.56 seconds.
34
35
36
PROC PRINT ;
RUN;
NOTE: The PROCEDURE PRINT used 0.11 seconds.
work.new
OBS
1
2
3
4
5
6
NAME
CHRISTIANSEN
HOSKING J
HELMS R
PIGGY M
FROG K
GONZO
SEX
M
M
M
F
M
AGE
HT
WT
37
31
41
.
3
14
71
70
74
48
12
25
195
160
195
.
1
45
7-21
MWT
119.2
119.2
119.2
119.2
119.2
119.2
MANY-TO-MANY MERGE EXAMPLE
WORK.SBP
ID
101
101
102
102
102
WORK.LIPIDS
SBP
80
85
90
95
100
ID
101
101
102
102
HDL
45
50
60
65
DATA ALL;
MERGE SBP LIPIDS ;
BY ID ;
RUN;
WORK.ALL
ID
101
101
102
102
102
SBP
80
85
90
95
100
HDL
45
50
60
65
65
LDL
180
200
210
220
220
7-22
LDL
180
200
210
220
The UPDATE Statement
The UPDATE statement joins observations from two SAS data sets, an "old master file" data set
and a "transaction file" data set. The resulting observations form a "new master file" data set.
These observations contain the values of the variables in the old master file, except for those
modifications specified in the transaction file.
SYNTAX
UPDATE SASDS1 SASDS2;
BY Variables;
where
SASDS1 is the old master file; it must be sorted by the BY variables and must contain only one
observation for each BY group and
SASDS2 is the transaction file; it must be sorted by the BY variables, but may have multiple
observations per BY group.
NOTES:
v Variables with non-missing values in the transaction file will replace those values in the PDV.
Missing values will not replace values in the PDV.
v The PDV is initialized to missing only at the beginning of a new BY group.
v The PDV is output to the new master file only at the end of a BY group.
7-23
UPDATE EXAMPLE
WORK.GRADES
NAME
SEX
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
FROG K
FROG K
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
PIGGY M
M
M
M
M
M
M
M
M
M
F
F
WORK.FIXHW
HWNUM SCORE
1
2
4
5
1
2
3
1
.
1
2
3
10
3
9
10
10
10
8
.
.
10
10
10
NAME
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
FROG K
HOSKING J
DATA GRADES2;
UPDATE GRADES FIXHW;
BY NAME HWNUM;
RUN;
WORK.GRADES2
NAME
SEX
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
FROG K
FROG K
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
PIGGY M
M
M
M
M
M
M
M
M
M
F
F
HWNUM
1
2
4
5
1
2
3
1
.
1
2
3
SCORE
9
2
9
10
10
10
8
.
.
10
10
10
7-24
NUMBER
1
2
2
4
2
1
SCORE
9
10
2
.
.
.
UPDATING VALUES TO MISSING
In order to set the value of a variable to missing using an UPDATE statement, the variable must be
set to the special missing value (_) in the transaction file.
WORK.GRADES
NAME
SEX
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
FROG K
FROG K
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
PIGGY M
M
M
M
M
M
M
M
WORK.FIXHW
HWNUM SCORE
1
2
4
5
1
2
3
1
.
1
2
3
M
M
F
F
10
3
9
10
10
10
8
.
.
10
10
10
NAME
HWNUM
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
FROG K
HOSKING J
DATA GRADES2;
UPDATE GRADES FIXHW;
BY NAME HWNUM;
RUN;
WORK.GRADES2
NAME
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
CHRISTIANSEN
FROG K
FROG K
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
PIGGY M
SEX
M
M
M
M
M
M
M
M
M
F
F
HWNUM
1
2
4
5
1
2
3
1
.
1
2
3
7-25
SCORE
9
2
.
10
10
10
8
.
.
.
10
10
1
2
2
4
2
1
SCORE
9
10
2
__
.
__
UPDATE EXAMPLE: SPECIAL MISSING VALUES
master
trans
ID
X
ID
1
2
3
10
20
30
1
1
2
3
3
X
A
.
C
10
.
data all ;
update master trans;
by id;
run;
all
ID
X
1
2
3
A
C
10
7-26
SAS SPECIAL VARIABLES
The following special variables can be created in DATA steps using SET, MERGE, or UPDATE
statements.
v The IN variable indicates if the data set contributed to the current observation.
v The END variable indicates that the current observation is the last to be processed.
v FIRST.byvariable and LAST.byvariable indicate if the current observation is the first or last
observation in the BY group.
SYNTAX:
SET
MERGE
UPDATE
Datasetname(IN=variable)...END=endvarible;
For the BY Statement
BY var1 var2 ...;
FIRST.var1, LAST.var1, FIRST.var2, LASAT.var2 . . . are automatically created.
NOTES:
v
v
v
v
The special variables exist in the PDV, but not in the output data sets.
The special variables are indicator variables with values of 0 (False) and 1 (True).
Invariables can be created for each data set in a SET, MERGE, or UPDATE statement.
Endvariables can be created for each SET, MERGE, or UPDATE statement.
7-27
Example: Special Variables
JAN
MAKE
CHEVY
CHEVY
CHEVY
FORD
FORD
YEAR
76
72
69
69
76
FEB
PRICE
3550
1350
1165
700
3245
MAKE
CHEVY
FORD
FORD
OLDS
YEAR
76
75
63
72
PRICE
2525
2985
695
2383
DATA CARSALES;
SET JAN(IN=INJ) FEB(IN=INF) END=EOF;
BY MAKE;
RUN;
PDV
CARSALES
MAKE
CHEVY
CHEVY
CHEVY
CHEVY
FORD
FORD
FORD
FORD
OLDS
YEAR
76
72
69
76
69
76
75
63
72
SPECIAL VARIABLES
PRICE
INJ
3550
1350
1165
2525
700
3245
2985
695
2383
1
1
1
0
1
1
0
0
0
7-28
INF
EOF
0
0
0
1
0
0
1
1
1
0
0
0
0
0
0
0
0
1
FIRST.MAKE
1
0
0
0
1
0
0
0
1
LAST.MAKE
0
0
0
1
0
0
0
0
1
Example: Special Variables
Create a data set containing only observations where a match occurred on the BY variable.
WORK.LIST2
NAME
CHRISTIANSEN
FROG K
GONZO
HOSKING J
PIGGY M
WORK.DEMO2
SEX
M
M
M
F
DATA MATCHES;
MERGE LIST2(IN=LIST)
BY NAME;
IF LIST AND DEMO;
RUN;
NAME
CHRISTIANSEN
FROG K
HELMS R
PIGGY M
AGE
37
3
41
.
HT
71
12
74
48
WT
195
1
195
.
DEMO2(IN=DEMO);
PDV
LIST
NAME
SEX
DEMO
AGE
HT
WT
FIRST.
NAME
LAST.
NAME
WORK.MATCHES
NAME
CHRISTIANSEN
FROG K
PIGGY M
SEX
M
M
F
7-29
AGE
37
3
.
HT
71
12
48
WT
195
1
.
ONE TO ONE MATCH MERGE EXAMPLE WITH AN IN STATEMENT
Merge pre-rand and post-rand files. Only keep subjects who have a record in both files. Then create
variables for difference between pre-rand and post-rand values.
WORK.PRERAND
WORK.POSTRAND
ID
101
102
103
104
ID
101
102
103
105
HDL
40
45
55
60
LDL
200
210
215
220
HDL
45
50
60
65
LDL
180
200
210
230
DATA ALL;
MERGE PRERAND(IN=INPRE RENAME=(HDL=HDL0 LDL=LDL0))
POSTRAND(IN=INPOST RENAME=(HDL=HDL1 LDL=LDL1)) ;
BY ID ;
IF (INPOST=1) & (INPRE=1) ;
HDLDIFF = HDL1 - HDL0 ;
LDLDIFF = LDL1 - LDL0 ;
RUN;
WORK.ALL
ID
101
102
103
HDL0
40
45
55
LDL0
200
210
215
HDL1
45
50
60
LDL1
180
200
210
7-30
HDLDIFF
5
5
5
LDLDIFF
-20
-10
-5
MATCH MERGE EXAMPLE WITH THE IN STATEMENT
Merge SBP and LIPID files. Only keep subjects who have a record in both files.
WORK.SBP
ID
WORK.LIPIDS
VISIT
SBP
101
1
80
101
2
102
ID
VISIT
HDL
LDL
101
1
45
180
85
101
2
50
200
1
90
102
1
60
210
102
2
95
102
2
65
220
102
4
100
102
3
70
230
103
1
110
DATA ALL;
MERGE SBP(IN=INS)
LIPIDS(IN=INL) ;
BY ID VISIT ;
IF (INS & INL) ; /* INCLUDE IF IN SBP & LIPID FILES */
RUN;
WORK.ALL
ID
101
101
102
102
VISIT
1
2
1
2
SBP
80
85
90
95
HDL
45
50
60
65
LDL
180
200
210
220
PDV
_______________________________________________________________
ID
101
101
102
102
102
102
103
VISIT
1
2
1
2
3
4
1
SBP
80
85
90
95
.
100
110
HDL
LDL
INS
45
50
60
65
70
.
.
180
200
210
220
230
.
.
1
1
1
1
0
1
1
7-31
INL
1
1
1
1
1
0
0
MATCH MERGE EXAMPLE WITH THE IN AND FIRST.BY/LAST.BY STATEMENT
Merge SBP and LIPID files. Only keep subjects who have a record in both files and only keep the
first record for each subject.
WORK.SBP
ID
101
101
102
102
102
103
103
VISIT
1
2
1
2
4
1
1
WORK.LIPIDS
SBP
80
85
90
95
100
110
120
ID
101
101
102
102
102
VISIT
1
2
1
2
3
HDL
45
50
60
65
70
LDL
180
200
210
220
230
DATA ALL;
MERGE SBP(IN=INS)
LIPIDS(IN=INL) ;
BY ID VISIT ;
IF (INS & INL) & (FIRST.ID) ;
RUN;
WORK.ALL
ID
101
102
VISIT
1
1
SBP
80
90
HDL
45
60
LDL
180
210
PDV
__________________________________________________________________
ID
101
101
102
102
102
102
103
103
VISIT
1
2
1
2
3
4
1
1
INS
1
1
1
1
0
1
1
1
INL
1
1
1
1
1
0
0
0
FIRST.ID
1
0
1
0
0
0
1
0
LAST.ID
0
1
0
0
0
1
0
1
7-32
FIRST.VISIT
1
1
1
1
1
1
1
0
LAST.VISIT
1
1
1
1
1
1
0
1
EXAMPLE: EOF Statement
DATA ONE ;
SET SC.CLASS END=EOF;
KEEP N F ;
N + 1; /* or RETAIN N 0 ; N=N+1 ; */
F + (SEX=’F’); /* or RETAIN F 0 ; F=F+(SEX=’F’) ; */
IF (EOF=1);
/* OUTPUT IF LAST RECORD */
RUN;
PROC PRINT ;
RUN;
_______________________WORK.ONE________________________________
_
OBS
N
F
1
6
1
7-33
Example: Transpose data set LIPIDS from multiple records per subject to
1 record per subject
DATA TWO;
13
SET LIPIDS ;
14
15
BY ID ;
16
17
RETAIN LDL1 LDL2 HDL1 HDL2 ;
18
19
20
ARRAY VARS LDL1 LDL2 HDL1 HDL2 ;
21
IF FIRST.ID THEN DO OVER VARS ;
22
VARS=. ;
23
END ;
24
25
IF VISIT=1 THEN DO;
26
LDL1 = LDL ;
27
HDL1 = HDL ;
28
END ;
29
30
ELSE IF VISIT=2 THEN DO;
31
LDL2 = LDL ;
32
HDL2 = HDL ;
33
END ;
34
35
KEEP ID LDL1 LDL2 HDL1 HDL2 ;
36
37
IF LAST.ID THEN OUTPUT ;
38 RUN;
NOTE: The data set WORK.TWO has 3 observations and 5 variables.
NOTE: The DATA statement used 1.4 seconds.
LIPIDS
OBS
1
2
3
4
5
ID
101
101
102
102
103
VISIT
LDL
HDL
1
2
1
2
1
180
185
160
170
190
45
50
40
.
60
HDL1
HDL2
45
40
60
50
.
.
TWO
OBS
1
2
3
ID
LDL1
LDL2
101
102
103
180
160
190
185
170
.
7-34
MULTIPLE OUTPUT DATASETS
A single DATA step can create more than one SAS data set using the more general forms of the
DATA and OUTPUT statements:
DATA SASDS1 SASDS2 . . . SASDSN,
OUTPUT SASDSX . . .;
where
SASDS1 . . . SASDSN are the names of up to 50 SAS data sets to be created
in this step, and
SASDSX is a subset of the N data set names listed in the DATA statement.
NOTES:
v A DATA step may contain multiple OUTPUT statements.
v Each OUTPUT statement may specify any combination of the SAS data sets listed in the
DATA statement.
v The current contents of the PDV are written to these data sets each time the OUTPUT statement
is executed.
v The statement OUTPUT; (without a list of data set names) writes the PDV to all data sets in the
DATA statement.
v A DATA step without an OUTPUT statement defaults to writing to all output data sets each
time the bottom of the program is reached.
7-35
EXAMPLE: Create SHORT and TALL datasets from CLASS in one DATA step.
WORK.CLASS
NAME
CHRISTIANSEN
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
SEX
M
M
AGE
37
3
14
41
31
.
M
M
F
HT
71
12
25
74
70
48
DATA SHORT TALL;
SET CLASS;
IF HT LT 60 THEN OUTPUT SHORT;
IF HT GE 60 THEN OUTPUT TALL;
RETURN;
WORK.SHORT
NAME
FROG K
GONZO
PIGGY M
SEX
M
F
WORK.TALL
AGE
HT
WT
NAME
3
14
.
12
25
48
1
45
.
CHRISTIANSEN
HELMS R
HOSKING J
7-36
SEX AGE
HT
WT
M 37
M 41
M 31
71
74
70
195
195
160
WT
195
1
45
195
160
.
EXAMPLE: COMBINING MULTIPLE INPUT AND OUTPUT DATASETS
WORK.LIST
NAME
SEX
CHRISTIANSEN
FROG K
GONZO
HELMS
HOSKING J
PIGGY M
M
M
M
M
F
WORK.DEMO
NAME
AGE
HT
WT
CHRISTIANSEN
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
37
3
14
41
31
.
71
12
25
74
70
48
195
1
45
195
160
.
DATA TALL BOYS ALL;
MERGE LIST DEMO
BY NAME;
IF HT GE 60 THEN OUTPUT TALL;
IF SEX EQ ’M’ THEN OUTPUT BOYS;
OUTPUT ALL;
RUN;
7-37
WORK.TALL
NAME
CHRISTIANSEN
HELMS R
HOSKING J
SEX
AGE
HT
WT
M
M
M
37
41
31
71
74
70
195
195
160
SEX
M
M
M
M
AGE
37
3
41
31
HT
71
12
74
70
WT
195
1
195
160
SEX
M
M
AGE
37
3
14
41
31
.
HT
71
12
25
74
70
48
WT
195
1
45
195
160
.
WORK.BOYS
NAME
CHRISTIANSEN
FROG K
HELMS R
HOSKING J
WORK.ALL
NAME
CHRISTIANSEN
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
M
M
F
7-38
EXAMPLE: Output Multiple Data Sets
/* output 2 data sets: 1) one contains main data set ; 2) counts contains count variables */
data one(drop=n f)
counts(keep=n f) ;
set sc.class end=eof;
n+1;
f + (sex=’F’) ;
if (eof) then output counts;
output one ;
run;
7-39
RESHAPING SAS DATASETS
v Multiple output observations can be created from a single input observation by using several
OUTPUT statements.
v The DROP and KEEP data set options can be used to control which variables are included or
excluded from the PDV and the output data sets.
v The RENAME data set option can change the names of variables in the PDV and output data
sets.
7-40
EXAMPLE: MULTIPLE OUTPUT OBSERVATIONS FROM SINGLE INPUT
OBSERVATIONS
Reshape the data set from one observation per pupil to one observation per test.
WORK.STUDENTS
NAME
JOHN
ALBERT
SALLY
MARY
TEST1
90
100
95
50
TEST2
70
33
80
100
TEST3
50
89
90
92
DATA TEST;
SET WORK=STUDENT;
EXAM=1;
GRADE=TEST1; OUTPUT;
EXAM=2;
GRADE=TEST2; OUTPUT;
EXAM=3;
GRADE=TEST3; OUTPUT;
RUN;
WORK.TEST
NAME
JOHN
JOHN
JOHN
ALBERT
ALBERT
ALBERT
SALLY
SALLY
SALLY
MARY
MARY
MARY
TEST1
90
90
90
100
100
100
95
95
95
50
50
50
TEST2
70
70
70
33
33
33
80
80
80
100
100
100
TEST3 EXAM
50
1
50
2
50
3
89
1
89
2
89
3
90
1
90
2
90
3
92
1
92
2
92
3
7-41
GRADE
90
70
50
100
33
89
95
80
90
50
100
92
EXAMPLE: DROPPING UNWANTED VARIABLES WITH THE DROP STATEMENT
DATA TEST;
SET WORK.STUDENT;
DROP TEST1-TEST3 I;
ARRAY TEST(I) TEST1-TEST3;
DO I=1 TO 3;
EXAM=I;
GRADE=TEST;
OUTPUT;
END;
RUN;
WORK.TEST
NAME
JOHN
JOHN
JOHN
ALBERT
ALBERT
ALBERT
SALLY
SALLY
SALLY
MARY
MARY
MARY
EXAM
1
2
3
1
2
3
1
2
3
1
2
3
GRADE
90
70
50
100
33
89
95
80
90
50
100
92
NOTE:Keep name EXAM GRADE; would produce the same results as the DROP
statement.
7-42
THE DROP AND KEEP DATASET OPTIONS
The DROP and KEEP data set options can be used on each output data set named in the DATA
statement to control which of the variables in the PDV get written to which output data sets.
DATA LIST (KEEP=NAME SEX) DEMO(DROP=SEX AGE);
SET CLASSLIB.CLASS;
RUN;
CLASSLIB.CLASS
NAME
SEX
AGE
HT
WT
PDV
NAME
LIST
NAME
SEX
SEX
AGE
DEMO
7-43
HT
NAME
HT
WT
WT
The DROP and KEEP options can be used on the input data sets listed in SET, MERGE, and
UPDATE statements to control which variables get read into the PDV.
EXAMPLE
DATA BOTH;
MERGE CLASS1(DROP=AGE) CLASS2(DROP=SEX);
BY NAME;
RUN;
CLASS1
FROG K
GONZO
HELMS R
PIGGY M
NAME
M
CLASS2
M
F
3
14
41
.
FROG K
HELMS R
HOSKING J
PIGGY M
SEX
AGE
NAME
PDV
BOTH
NAME
SEX
A
B
C
D
SEX
AGE
.
41
31
39
AGE
HT
NAME
SEX
AGE
HT
FROG K
GONZO
HELMS R
HOSKING J
PIGGY M
M
.
.
41
31
39
12
.
74
70
48
7-44
M
F
12
74
70
48
HT
THE RENAME DATASET OPTION
SYNTAX:
SASDS(RENAME=(OLDNAME=NEWNAME . . .))
The RENAME option can be used on SET, MERGE and UPDATE statements to change the names
of SAS variables between the input data sets and the PVD, and on the DATA statement to change
names between the PDV and the output data sets.
DATA COMBO;
SET FALL SPRING(RENAME=(TEST=EXAM));
RUN;
FALL
NAME
SPRING
EXAM
PDV
NAME
EXAM
COMBO
NAME
EXAM
7-45
NAME
TEST
SAS DATA LIBRARY MANAGEMENT
v A SAS data library may be permanent or temporary. Permanent libraries have a library name
specified by the user, which must correspond to a libname statement. Temporary libraries have
a library name of WORK.
v There are several SAS procedures that are useful in the management of SAS data libraries.
PROC COPY can be used to copy all or some of the SAS data sets from one SAS data library
to another. PROC DATASETS can be used to delete or modify data sets on a SAS data library.
v A single data set can be copied from one data library to another with a DATA step of the form
DATA newlib.SASds; SET oldlib.SASds;
v Either of the above methods of copying data sets can be used to "back-up" data sets. "Backingup" of a data set means making one or more copies of the data set and storing the copies in a
safe place. One then has:
v Backup copies stored safely away, and
v A working copy used for calculations or other processing.
7-46
THE COPY PROCEDURE
The COPY procedure is used to copy some or all of the SAS data sets from one library to another.
Syntax:
PROC COPY IN=Libraryname OUT=Libraryname:
Statements used with COPY:
SELECT list of dataset names;
EXCLUDE list of dataset names;
The SELECT statement causes only the data sets listed to be copied. The EXCLUDE statement
causes all data sets except those listed to be copied.
NOTES:
v If neither a SELECT or an EXCLUDE statement is used, all data sets in the input library are
copied.
v If the output library contains a data set with the same name as a selected data set, it will be
replaced.
v The DOS COPY Command can also be used to copy one or more SAS DATASETS.
7-47
EXAMPLE:
1
2
3
4
Libname classlib ’a:\’;run;
proc copy in=classlib out=work;
title ’proc copy example’;
run;
NOTE:Copying CLASSLIB.CLASS to WORK.CLASS (MEMTYPE=DATA).
NOTE:The data set WORK.CLASS has 6 observations and 5 variables.
NOTE:Copying CLASLIB.SALES to WORK.SALES (MEMTYPE=DATA).
NOTE:The data set WORK.SALES has 50 observations and 6 variables.
NOTE:The PROCEDURE COPY used 6.00 second.
5
proc copy in=classlib out=work;
6
select sales;
7
title1 ’proc copy example’;
8
title2 ’using the select statement’;
9
run;
NOTE:Copying CLASSLIB.SALES to WORK.SALES (MEMTYPE=DATA).
NOTE:The data set WORK.SALES has 50 observations and 6 variables.
NOTE:
The PROCEDURE COPY used 5.00 seconds.
7-48
THE DATASETS PROCEDURE
v The DATASETS procedure is used to manage a SAS library
v With PROC DATASETS you can list, copy, rename, and delete SAS data sets in a SAS data
library.
v For SAS data sets, PROC DATASETS can alter the descriptor portion of the SAS data set.
PROC DATASETS can be used to rename variables, and to change formats, informats, or
labels.
SYNTAX:
PROC DATASETS LIBRARY=libref ;
DELETE mem_list ; /* delete files specified in mem_list */
CHANGE OLD=NEW ; /* rename SAS dataset from old name to new */
COPY OUT=libref2 ; /* copy to another SAS library */
SELECT MEM_LIST ;
CONTENTS DATA=sas_data_set ; /* get contents of a sas_data_set */
CONTENTS DATA=libref._ALL_ nods; /* get contents of a sas library */
MODIFY sas_data_set_ ; /* modify descriptor part of sas_data_set */
RENAME old_name=new_name ; /* rename a variable */
LABEL var=’label’ ; /* add or change variable label */
FORMAT var format; /* add, change, or delete a format */
RUN ;
7-49
DATASETS EXAMPLE:
LIBNAME SC2 V612 ’D:\BIOS111\SASDATA2’ ;
PROC DATASETS LIBRARY=SC2 ;
DELETE HW3 ;
CHANGE SCORES=TEST ;
CONTENTS DATA=CLASS ;
COPY IN=SC2 OUT=WORK;
SELECT CLASS ;
MODIFY CLASS ;
LABEL WT=’WT IN LBS.’
FORMAT HT 5.0 ;
RENAME SEX = GENDER ;
CONTENTS DATA = CLASS ;
QUIT ;
RUN;
7-50
1
LIBNAME SC2 V612 ’D:\BIOS111\SASDATA2’ ;
NOTE: Libref SC2 was successfully assigned as follows:
Engine:
V612
Physical Name: D:\BIOS111\SASDATA2
3
PROC DATASETS LIBRARY=SC2 ;
-----Directory----Libref:
SC2
Engine:
V612
Physical Name: D:\BIOS111\SASDATA2
# Name
Memtype Indexes
--------------------------1 CLASS
DATA
2 HW3
DATA
3 HW4
DATA
4 SALES
DATA
5 SCORES DATA
4
5
NOTE:
NOTE:
6
7
8
NOTE:
NOTE:
9
10
DELETE HW3 ;
CHANGE SCORES=TEST ;
Deleting SC2.HW3 (memtype=DATA).
Changing the name SC2.SCORES to SC2.TEST (memtype=DATA).
CONTENTS DATA=CLASS ;
COPY IN=SC2 OUT=WORK;
SELECT CLASS ;
Copying SC2.CLASS to WORK.CLASS (MEMTYPE=DATA).
The data set WORK.CLASS has 6 observations and 5 variables.
MODIFY CLASS ;
LABEL WT=’WT IN LBS.’
11
FORMAT HT 5.0 ;
12
RENAME SEX = GENDER ;
NOTE: Renaming variable SEX to GENDER.
13
CONTENTS DATA = CLASS ;
14
QUIT ;
NOTE: The PROCEDURE DATASETS used 7.75 seconds.
15
RUN;
7-51
DATASETS PROCEDURE
Data Set Name: SC2.CLASS
Observations:
6
Member Type:
DATA
Variables:
5
Engine:
V612
Indexes:
0
Created:
10:02 Wednesday, July 21, 1993
Observation Length:
41
Last Modified: 10:02 Wednesday, July 21, 1993
Deleted Observations: 0
Protection:
Compressed:
NO
Data Set Type:
Sorted:
NO
Label:
-----Alphabetic List of Variables and Attributes----#
Variable
Type
Len
Pos
----------------------------------3
AGE
Num
8
17
4
HT
Num
8
25
1
NAME
Char
12
4
2
SEX
Char
1
16
5
WT
Num
8
33
7-52
DATASETS PROCEDURE
Data Set Name: SC2.CLASS
Observations:
6
Member Type:
DATA
Variables:
5
Engine:
V612
Indexes:
0
Created:
10:02 Wednesday, July 21, 1993
Observation Length:
41
Last Modified: 10:40 Friday, October 22, 1993
Deleted Observations: 0
Protection:
Compressed:
NO
Data Set Type:
Sorted:
NO
-----Alphabetic List of Variables and Attributes----#
Variable
Type
Len
Pos
Label
-------------------------------------------------------------------3
AGE
Num
8
17
2
GENDER
Char
1
16
4
HT
Num
8
25
1
NAME
Char
12
4
5
WT
Num
8
33
7-53
WT IN LBS.
FORMAT HT 5.0
TRANSPORTING SAS FILES BETWEEN HOSTS
v The process of moving SAS files from one host environment to another is called
TRANSPORTING.
v To move a SAS file from one host to another, you must convert the file to a format that can be
recognized by the SAS system on other host systems. This format is called TRANSPORT
format.
v The transport process includes:
v
exporting the SAS file(putting the file into transport format)
v
moving the transport file to another host environment via communications software
v
importing the transport(restoring the file to the format appropriate to the receiving host).
v Transport format is necessary when moving SAS files between machines running two different
operating systems.
v If both the sending and the receiving host are running the same operating system, transport
format is not necessary.
v SAS data sets in using the V7 engine are in a format that is recognized by all operating systems.
Therefore V7 SAS data sets can be used on multiple operating systems without going through
the transport process.
v A transport file is a sequential file that contains one or more SAS data sets in SAS transport
format.
v With the XPORT engine, a SAS data set in transport format can be used as input in a DATA
step or processed by SAS procedures, including the COPY procedure.
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TRANSPORTING WITH RELEASE 6.06 OR LATER
v In release 6.06 or later the XPORT engine used in conjunction with PROC COPY can be used
for creating or reading SAS data sets in transport format.
v The CPORT and CIMPORT procedures can be used to transport SAS catalogs. Use the
CPORT and CIMPORT procedures to transport catalogs and catalog entries from one machine
to another machine running under a different operating environment.
EXAMPLE: EXPORTING A SAS LIBRARY/DATASET
libname tran XPORT ’c:\bios111\class.dat’ ;
libname sc ’c:\bios111\sasdata’ ;
/* convert an entire library to transport format */
proc copy in=sc out=tran ;
run;
/* convert a single SAS data set to transport format */
proc copy in=sc out=tran ;
select class ;
run;
7-55
EXAMPLE: IMPORTING A SAS LIBRARY/DATASET
libname tran XPORT ’c:\bios111\class.dat’ ;
libname sc ’c:\bios111\sasdata’ ;
/* import all files in a SAS transport file */
proc copy in=tran out=sc ;
run;
/* import a single file in a SAS transport file */
proc copy in=tran out=sc ;
select class ;
run;
7-56