In that case, you can use the underscore (_) character in the MISSING statement. Suppose you want the resulting value in the master data set to be a regular missing value in the updated master data set. Run Special Missing Values (A-Z) Underscore ( _ ) You can define a special missing value with the MISSING statement and then place that in the transaction data set where the special character should appear.Īny single character from A to Z is a valid value for these special missing value characters.Įven if UPDATEMODE=MISSINGCHECK is enabled, you can use the MISSING statement with a letter assignment to replace existing values with missing values. Suppose you want to update some variables in the transaction data set that have missing values, but not all of them. Run Updating with Missing Values Special Missing Values (A-Z) Update paylist2 paylist_update updatemode=nomissingcheck Specify the UPDATEMODE=NOMISSINGCHECK option in the UPDATE statement if you want missing values in the transaction data set to replace existing values in the master data set. The UPDATEMODE=MISSINGCHECK option is enabled by default, which means that missing values in the transaction data set do not overwrite existing values in the master data set. Run Updating By Renaming Variables Updating with Missing Values Update cert.health(rename=(weight=Original) in=a) health_update(in=b) The WEIGHT variable is renamed in the master dataset- health, and a new WEIGHT variable is calculated. This example shows renaming a variable in the health_update data set so that the same variable in the program data vector is not overwritten. Run Basic SAS Update Statement Updating By Renaming Variables Input IdNum $ gender $ Jobcode $ Salary Birth hired The BY variable IdNum must appear in both paylist2 and paylist_update and its values in the master data set should be unique: option yearcutoff=1930 The program statements below create a new dataset paylist_new By applying transactions to a master data set ( paylist2). (Multiple transaction observations are all applied to the master observation before it is written to the output file.) A Basic SAS Update Statement Example The transaction data set can contain more than one observation with the same BY value. If there are multiple values for the BY variable, only the first observation is updated. Each observation in the master data set should have a unique value of the BY variable or BY variables.The data sets listed in the UPDATE statement must be sorted by the BY variable values or have an appropriate index.The SAS UPDATE statement must be followed by a BY statement that specifies the variables by which observations are matched.The rules and requirements for using the UPDATE statement are: However, special missing values are the exception and replace values in the master data set even when MISSINGCHECK (the default) is in effect. It specifies whether missing variable values in a transaction data set are allowed to replace existing variable values in a master data set. It prevents a transaction data set’s missing variable values from being replaced by values in the master data set. Observations that will not be updated can be excluded from the transaction data set. If any transaction observations do not match master observations, they are added as new observations to the output data set. The transaction data set may also include new variables added to the output data set.Įach observation in the output data set corresponds to one in the master data set. To reduce processing time, create a transaction dataset containing only the variables that need to be updated. The master and transaction data sets typically contain the same variables. It specifies the SAS data set used as the master file. Syntax: UPDATE master-data-set transaction-data-set
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