And, the second SET statement reads the second observation from data set two. Because there are no more observations in the two data set, processing stops. That is, the DATA step does not read the third observation from the one data set.
Now, launch and run the SAS program, and review the output to convince yourself that the one and two data sets are combined as described. One more comment. Note that although each of our one-to-one reading examples involved combining just two data sets, you can specify any number of SET statements when one-to-one reading At first glance, one-to-one merging appears to be the same as one-to-one reading, since it too combines two or more SAS data sets, one "to the right" of the other into a single "fat" data set.
That is, just like one-to-one reading, one-to-one merging combines observations from two or more data sets into a single observation in a new data set. There is just one primary difference though — SAS continues to merge observations until it has read all of the observations from all of the data sets.
For example, suppose again that our patients data set contains three variables: patient ID number ID , gender Sex , and age of the patient Age :. Then, when we one-to-one merge the two data sets, we get a data set, called say one2onemerge , that looks like this:. Again, the observations are combined based on their relative position in the data set.
The first observation of patient is combined with the first observation of scale to create the first observation in one2onemerge ; the second observation of patient is combined with the second observation of scale to create the second observation in one2onemerge ; and so on. When SAS performs a one-to-one merge, the DATA step continues to read observations until the last observation is read from the largest data set. That's why the one2onemerge data set has one more observation than the one2oneread data set.
In general, the number of observations in a data set created by a one-to-one merge always equals the numbers of observations in the largest data set named for one-to-one merging. The following program uses one-to-one merging to combine the patients data set with the scale data set:.
You should see that the first observation in one2onemerge contains the first observation of patients and scale , the second observation in one2onemerge contains the second observation of patients and scale , and so on. Since there are seven observations in patients and six observations in scale , the new one2onemerge data set contains seven observations, with missing values for the Height and Weight variables in the seventh observation.
Note that although this example only combined two data sets, the MERGE statement can contain any number of input data sets. Just as is true for one-to-one reading, if data sets that are being one-to-one merged contain variables that have the same names, the values that are read in from the last data set overwrite the values that were read in from earlier data sets.
Let's go back to our contrived example to illustrate this point. The following program uses one-to-one merging to combine the one data set with the two data set to create a new data set called onetwomerged :. Note again that the one and two data sets share two variables, namely ID and VarB.
Being at the end of the first iteration of the DATA step, SAS writes the contents of the program data vector as the first observation in the onetwomerged data set. Being at the end of the second iteration of the DATA step, SAS writes the contents of the program data vector as the second observation in the onetwomerged data set. Now this is where things get different! The program data vector looks like this at the beginning of the third iteration of the DATA step:.
SAS attempts to read a third observation from the two data set, but instead encounters an end-of-data set marker. Therefore, as is always the case in this kind of situation , SAS sets the values of all of that data set's variables in the program data vector to missing:.
Because there are no more observations in either the one or the two data set, processing stops. Thank goodness! One more closing comment. One-to-one reading and one-to-one merging require users to exercise extreme caution when combining two or more data sets based on relative position only.
It would just take one of the data sets to be "shifted" ever so slightly to get really messed up results. It's for this reason that I personally don't find the one-to-one read or the one-to-one merge all that practical.
The more useful and therefore much more common merge performed in SAS is what is called match-merging. We'll learn about it in the next lesson. For example, suppose the data set store1 contains three variables, store number , day of the week , and sales in dollars :.
Note that the number of observations in the new data set is the sum of the numbers of observations in the original data sets. The following program concatenates the store1 and store2 data sets to create a new "tall" data set called bothstores :. Note that the input data sets — store1 and store2 — contain the same variables — Store , Day , and Sales — with identical attributes. Note that although we have specified only two input data sets here, the SET statement can contain any number of input data sets.
Launch and run the SAS program, and review the output from the PRINT procedure to convince yourself that SAS did indeed concatenate the store1 and store2 data sets to make one "tall" data set called bothstores. You might then want to edit the SET statement so that store1 follows store2 , and re-run the SAS program to see that then the contents of store1 follow the contents of store2 in the bothstores data set.
In general, a data set that is created by concatenating data sets contains all of the variables and all of the observations from all of the input data sets. Therefore, the number of variables the new data set contains always equals the total number of unique variables among all of the input data sets. And, the number of observations in the new data set is the sum of the numbers of observations in the input data sets. Let's return to the contrived example we've used throughout this lesson.
The following program concatenates the one and two data sets to create a new "tall" data set called onetopstwo :. Therefore, we can expect the concatenated data set onetopstwo to contain four variables and five observations. Launch and run the SAS program, and review the output to convince yourself that SAS did grab first all of the variables and all of the observations from the one data set and then all of the variables and all of the observations from the two data set.
As you can see, to make it all work out okay, observations arising from the one data set have missing values for VarC , and observations from the two data set have missing values for VarA. As you know, variable attributes include the type of variable character vs.
Concatenating data sets when variable attributes differ across the input data sets may pose problems for SAS and therefore you :. Hi, what is the effect if we don't sort the data sets before appending? If you are using BY statement in merging but dont sort the data then the code will error out. As we know the mining services and its use then here is the advanced version of it for you by using it you can earn the more money and values, before using it's you have to read the best cloud mining companies reviews after reading the reviews you can select the best cloud mining services provider for you to start your mining.
In SAS, there are various method to append data sets. It is one of the most frequently data manipulation task in analytics work. For example, you have multiple human records files from various departments of your company and you are asked to join them so that there would be a single file containing information of all the departments.
Warning : It overwrites data. The question arises " why do we use multiple set statement if it overwrites data". Merge is done most often in the data step, either with the merge or the update statement. Concatenate : add a dataset on top or to the bottom of another one. Append : Just another word for concatenate.
Merge : add a dataset to the side right, generally of another one. It function the same way as the append statement in the datasets statement. MERGE:you should have a common variable or several variables which taken together uniquely identify each observations,it sequentaly checks observation for each by-value you have to sort your data sets before you can merge them ,then write the combined observation to the new data set.
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You should rearrange your question: it is on charge to you write the code you have and exposing your doubt about it. I disagree; there's nothing wrong with asking about fundamental terminology on SO. I'd like to see more research done before asking for such a simple topic, but asking about the idea is perfectly fine.
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