You can use the CMISS function in SAS to count the number of missing values in each row of a dataset.
Here is one common way to use this function in practice:
data new_data;
set my_data;
total_missing = cmiss(of team -- assists);
run;
This particular example creates a new dataset called new_data that includes a column called total_missing that counts the number of missing values in each row between the columns named team and assists.
The following example shows how to use this syntax in practice.
Example: Use CMISS in SAS to Count Number of Missing Values in Each Row
Suppose we have the following dataset in SAS called my_data that contains information about various basketball players:
/*create dataset*/
data my_data;
input team $ points assists;
datalines;
Cavs 12 5
Cavs 14 7
Warriors 15 9
. 18 9
Mavs 31 7
Mavs . 5
. . 3
Celtics 36 9
Celtics 40 7
;
run;
/*view dataset*/
proc print data=my_data;
Notice that several rows have missing values.
We can use the CMISS function to count the number of missing values in each row:
The following examples show how to use each method in practice with the following dataset in SAS:
/*create new dataset that counts number of missing values in each row*/
data new_data;
set my_data;
total_missing = cmiss(of team -- assists);
run;
The new column called total_missing displays the number of missing values in each row.
For example:
- The first row contains 0 missing values.
- The second row contains 0 missing values.
- The third row contains 0 missing values.
- The fourth row contains 1 missing value.
And so on.
Note: You can find the complete documentation for the SAS CMISS function here.
Additional Resources
The following tutorials explain how to perform other common tasks in SAS:
How to Count Missing Values in SAS
How to Remove Rows with Missing Values in SAS
How to Replace Missing Values with Zero in SAS