![]() Allows you to incorporate fraud detection at the most appropriate claims processing points for example, where anomaly. Reviews claims early in the adjudication process to help stop suspicious activity at the prepayment stage. In the example below, we concatenate all the columns of the my_data dataset, separated by a forward-slash (/). Scores claims for fraud at first submission then rescores them at each processing stage as new claims data is captured. For example, frequently used delimiters are the forward-slash (“/”) or whitespace (” “). You merge data sets using the MERGE statement in a DATA step. Note that the delimiter (like the input items) can be a variable, a constant, or an expression. CATX(delimiter, item-1, item-2, item-3, etc.) After the delimiter follow the items you want to concatenate. ![]() The first argument of the CATX function is the delimiter (of one or more characters). Like the CATS function, the CATX function removing leading and trailing blanks before the concatenation. The CATX function creates a character string by combining multiple variables and separates them with a delimiter. The last method to combine multiple strings in SAS is the CATX function. You can read more about this function, here Method 5: The CATX Function The STRIP function is a very useful function that removes both leading and trailing blanks. In the example below, we use the CAT function to combine the variables name and price from the sample dataset my_data.Ĭoncatenate strings with the CATS functionĪs you can see in the table above, the CATS function combines the power of the normal CAT function and the STRIP function. So, the correct syntax of the CAT function is: CAT(item-1, item-2, item-3, etc.) The input items can be variables, constants, or expressions, and are separated by a comma. To match-merge, you simply specify the data sets you would like to merge in a MERGE statement, and indicate the variables on which you would like to merge in a. However, if the input item is numeric, then its value is converted to a character string, and leading and trailing blanks are removed. This function returns a concatenated string of the input items without removing leading nor trailing blanks. The CAT function is the simplest function of the family of CAT* functions. The second method to concatenate multiple strings in SAS is with the CAT function. Because of this conversion, there are blanks between the dollar sign and the price value in the example. In this process, SAS converts the numeric values into a character before it concatenates the strings. If you would like to use the concatenation operator but get rid of whitespace, you should add the STRIP function.Īlso notice that you can use numeric columns (such as price) when you concatenate variables. Notice that the concatenation operator doesn’t remove leading and/or trailing blanks. It combines two columns ( name and price) and two constants ( The and costs: $). WORK.Concatenate with the Concatenation OperatorĪs the image above shows, we have created a new column, new_var, with the concatenation operator. WORK.t2(RENAME=(age=age2 height=height2)) WORK.t1(RENAME=(age=age1 height=height1)) WORK.wide(RENAME=(age3=age height3=height)) (in=c) WORK.wide(RENAME=(age2=age height2=height)) (in=b) SET WORK.wide(RENAME=(age1=age height1=height)) (in=a) Reshaping data using some very fancy SAS code DATA WORK.long Wide to long using an ARRAY DATA WORK.long We can reshape from wide to long using DATA WORK.long Īnd from long to wide using DATA WORK.t1 It is important to realize that exactly the same information is available in the wide format: id ![]() Typically used with longitudinal where different analyses requires either long or wide format. Remember that ‘missing’ will form a separate group. Requires that data are sorted LIBNAME dat 'p:\sas' ) and runs analyses within groups (stratified analyses). The BY statement is used in many procedures ( MEANS, REG, GLM. One-to-one merging is similar to a one-to-one reading, with two exceptions: you use the MERGE statement instead of multiple SET statements, and the DATA step. Can use the option DESCENDING LIBNAME dat 'p:\sas' If OUT= is omitted, original data set is ‘overwritten’ with the sorted data set. Sorting of data is often used because other procedures require sorted data LIBNAME dat 'p:\sas'
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