![]() Note that this does not cover the situation of merging without a BY statement. In this case, INA will be false and INB will be true. I hope that makes sense.ĭataset A does not have an observation with a given by-value and B does. If you reinitialize any of the variables before the MERGE statement, and there is no new observation, they will keep their reinitialized values but if there is a new observation, SAS will reset them. Generally the performance of straightforward joining them together using data step or proc sql in SAS is very poor. The above code takes advantage of the fact that SAS retains the variables from the last observation contributed to the by-group by the dataset with the smaller number of observations. A common scenario during data abstractionfor research projects is that the investigators want to join a small SAS data set with tables of an external large relational database, such as a SQL server or an Oracle database. As pointed out, you often want to know which dataset is contributing observations. If you want to know whether B still has observations, you need something like the following code. Even after B runs out of observations, both INA and INB will be true. Dataset A and B have different numbers of observations with a given by-value. In this case, INA will be true and INB will be false.ĭataset A and B both have an observation with a given by-value. When merging two datasets with a BY statement, you can have the following situations:ĭataset A has an observation with a given by-value and dataset B does not. Let's say you have MERGE A (in=INA) B (in=INB) This is useful for debugging purposes as the key is unique to each dataset. The last else if will output to the outer dataset which is the equivalent of an outer join.Here you can perform a right join on the data. Note: The FULL OUTER JOIN keyword returns all matching records from both tables whether the other table matches or not. The third else if is just the opposite of the previous.We’ll examine two key topics that most users are confronted with when working with their tables of data, conditional logic scenarios and merges/joins. ![]() If you wanted a full left join you could either append the left dataset to the inner dataset or just change the statement to if (a and b) or (a and not b) then output left. This paper illustrates the similarities and differences between the Base-SAS® software DATA step merge and PROC SQL join techniques. This is the equivalent of a left outer join in SQL. else if a and not b then output left will output only the records that occur in the have1 dataset and not the have2.The statement if a and b then output inner will output only records in which the key is found in the datasets have1 and have2 which is the equivalent of a SQL inner join.The data step will create 4 different datasets which are the basis of an inner join, left join, and right join. Let's say we have the following data step: data inner left right outer To elaborate more on vknowles answer, in=a and in=b are useful in different types of merges.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |