For example, In ID variable, there are 2 1's in dat1 dataset and 3 1's in dat2 dataset, the cartesian product would be (3*2 = 6 Observations) in the final result. Cartesian product is a collection of all pairs of two given sets. 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. PROC SQL JOIN creates all possible combinations of matching observations in case of a many-to-many relationship. So it would return 3 1s in the merged dataset. The maximum number of 1s in both the tables is 3. Note : In this example, we have 2 1s in dat1 and 3 1s in dat2. It will return number of records for a duplicate value equal to maximum number of the duplicate value in both the table. Data Step MERGE does not create a cartesian product in case of a many-to-many relationship. For example, if there are three records that match from one contributing data set to two records from the other, the resulting data set should have 3 × 2 = 6 records. To merge our two datasets and get the same result as in the code above, the SQL code would look something similar to that below: PROC SQL CREATE TABLE alldata0 AS SELECT a., b. the result should be a cartesian (cross) product of matching observations. MERGE WITH SQL SAS version 6.07 introduced SQL into SAS which gave the ability to merge data using the SQL language. The DATA step Merge does not handle many-to-many matching very well. primary key) of any or both of the datasets. You want to merge both the data sets but there are duplicate values in the common variable (ie. How did you create those four datasets? In each one how did you create the variable COUNTY?ĭid you try sorting datasets by COUNTY before running this data step? It does seem very strange that the first value of COUNTY is "YUMA".In SAS, many-to-many merges are handled very differently via Data Step MERGE and PROC SQL JOIN. NOTE: DATA statement used (Total process time):Ĩ0 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK ĭid you check the datasets to see if COUNTY is defined with the same length in all four of those datasets? You can use PROC CONTENTS to get a report on a dataset. WARNING: Data set WORK.COANLCOMBINED_ANALYSIS was not replaced because this step was stopped. Ideally, you should do such cleaning when data is imported into your data warehouse. Create a new variable with correct, clean values (in the dateset (s) where that conversion is needed) and merge by that. A merge is done with raw, unformatted values. When this step was stopped there were 0 observations and 13 Re: merging 2 datasets by variable - multiple lengths. Because SAS builds the Program Data Vector (PDV) from left to right, the leftmost data set in the MERGE statement will be first to supply variable attributes. attempt at a datastep solution using and some fabricated data. The merge process assumes that the variable lists of the incoming data sets are unique and that the only variables in common are those listed on the BY statement. WARNING: The data set WORK.COANLCOMBINED_ANALYSIS may be incomplete. just filter them out during the merge using the IN trick. NOTE: There were 1 observations read from the data set WORK.OBESE. SAS 9.4 DATA Step Statements: Reference SAS Help Center. NOTE: There were 1 observations read from the data set WORK.LANGUAGEPERCENT. NOTE: There were 2 observations read from the data set WORK.NAMES. NOTE: There were 1 observations read from the data set WORK.HISLATPERCENT. NOTE: The SAS System stopped processing this step because of errors. _NAME_=countyYUMA _LABEL_=countyYUMA COL1=78.16 my_new_county=YUMA This might cause unexpected results.ĮRROR: BY variables are not properly sorted on data set WORK.NAMES.Ĭounty=YUMA languageHisLat=. WARNING: Multiple lengths were specified for the BY variable county by input data sets. Tom, this is the log from where that error is created.ġ OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK Ħ9 ****************CoAnlCombined_Analysis **********
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