Sample 42281: Delete observations when all values are missing
The sample code on the Full Code tab illustrates how to use arrays and iterative DO loops to determine whether there are missing values across an observation. Observations are deleted when all values are missing.
Please click the Output tab to see the output.
These sample files and code examples are provided by SAS Institute
Inc. "as is" without warranty of any kind, either express or implied, including
but not limited to the implied warranties of merchantability and fitness for a
particular purpose. Recipients acknowledge and agree that SAS Institute shall
not be liable for any damages whatsoever arising out of their use of this material.
In addition, SAS Institute will provide no support for the materials contained herein.
The sample code below illustrates how to use arrays and iterative DO loops to determine whether there are missing values across an observation. Observations are deleted when all values are missing.
/* Create sample data */
data testdata;
input ch1 $ ch2 $ num1 num2 num3;
datalines;
aaa bbb 1 2 3
. bbb 1 1 1
. . . . .
aaa bbb 1 1 1
aaa bbb 1 . 2
aaa . 1 1 .
;
run;
data nomiss;
set testdata;
/* Use the reserved word _NUMERIC_ to load all numeric variables */
/* into the NUM array. Use the reserved word _CHARACTER_ to */
/* to load all character variables into the CHR array. */
array chr(*) _character_;
array num(*) _numeric_;
/* Initialize COUNT to 0. */
count=0;
/* Increment COUNT when a non-missing value is found */
do i = 1 to dim(chr);
if not missing(chr(i)) then count+1;
end;
do j = 1 to dim(num);
if not missing(num(j)) then count+1;
end;
/* COUNT=0 indicates that all values are missing. */
/* Delete the observation in this case. */
if count=0 then delete;
drop i j count;
run;
proc print;
run;
These sample files and code examples are provided by SAS Institute
Inc. "as is" without warranty of any kind, either express or implied, including
but not limited to the implied warranties of merchantability and fitness for a
particular purpose. Recipients acknowledge and agree that SAS Institute shall
not be liable for any damages whatsoever arising out of their use of this material.
In addition, SAS Institute will provide no support for the materials contained herein.
Obs ch1 ch2 num1 num2 num3
1 aaa bbb 1 2 3
2 bbb 1 1 1
3 aaa bbb 1 1 1
4 aaa bbb 1 . 2
5 aaa 1 1 .
This sample shows how to delete observations from a data set when all variable values across the observation are missing.
| Date Modified: | 2011-02-08 10:21:40 |
| Date Created: | 2011-02-03 10:56:26 |
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