One To Many Merge Sas

1. One-to-one merge

Below we have a file containing family id, father's name and income. We also have a file containing income information for multiple years. We would like to match merge the files together so we have the dads observation on the same line with the faminc observation based on the key variable famid. In proc sql we use where statement to do the matching as shown below.

2. One-to-many merge

Overview of Methods for Combining SAS Data Sets Methods for Combining SAS Data Sets. The following figure shows the results of concatenating two SAS data sets. Concatenating the data sets. The following figure shows the results of interleaving two SAS data sets. This is a basic how-to for merging SAS files that have the same unique ID's in both files, AKA a match-merge. Hi, I have 2 datasets that do not share any variables. One has 100(cats) observations and the other has 1000(dogs) observations. I would like to create a new dataset where each of the 1000 dogs gets all of the data from each of the 100 cats appended onto it. The final dataset would have 100000 o.

Imagine that we had a file with dads like we saw in the previous example, and we had a file with kids where a dad could have more than one kid. Matching up the 'dads' with the 'kids' is called a 'one-to-many' merge since you are matching one dad observation to possibly many kids records. The dads and kids records are shown below. Notice here we have variable fid in the first data set and famid in the second. These are the variables that we want to match. When we merge the two using proc sql, we don't have to rename them, since we can use data set name identifier.

3. Renaming variables with the same name in merging

Below we have the files with the information about the dads and family, but look more closely at the names of the variables. In the dads file, there is a variable called inc98, and in the family file there are variables inc96, inc97 and inc98.

Let's merge them using the same strategy used in our previous example on merging. We see below that we lost variable inc98 from the second dataset faminc. Proc sql uses the column from the first data set in case of same variable names from both datasets. This may not be what we want.

In proc sql we can rename the variables using the as statement shown below.

4. Using full join to handle mismatching records in a one-to-one merge

The two datasets may have records that do not match. Below we illustrate this by including an extra dad (Karl in famid 4) that does not have a corresponding family, and there are two extra families (5 and 6) in the family file that do not have a corresponding dad.

Let's apply the previous example to these two datasets. We see that the unmatched records have been dropped out in the merged data set, since the where statement eliminated them.

What if we want to keep all the records from both datasets even they do not match? The following proc sql does it in a more complex way. Here we create two new variables. One is indic, an indicator variable that indicates whether an observation is from both datasets, 1 being from both datasets and 0 otherwise. Another variable is fid, a coalesce of famid from both datasets. This gives us more control over our datasets. We can decide if we have a mismatch and where the mismatch happens.

5. Producing all the possible distinct pairs of the values in a column

Let's say that we have a data set containing a variable called city. We want to create allpossible distinct pairs of cities appeared in the variable. This would be reallytricky to do if we only use a data step. But it can be accomplished fairly straightforwardly with SAS proc sql asshown below. Proc sql is first used to select distinctcities and to save them to a new dataset. It is used again to create alldistinct pairs of cities. As shown below, there are seven different places. Therefore there will be 7*6/2 =21 pairs of cities.

Application requirements vary, but there are common factors for allapplications that access, combine, and process data. Once you have determinedwhat you want the output to look like, you must

• determine how the input data is related
• ensure that the data isproperly sorted or indexed, if necessary
• select the appropriate access method to process the input data
• select theappropriate SAS tools to complete the task.

You must be able to identify the existing relationships in your data.This knowledge is crucial for understanding how to process input data in orderto produce desired results. All related data fall into one of these four categories,characterized by how observations relate among the data sets:

• one-to-one
• one-to-many
• many-to-one
• many-to-many.

Toobtain the results you want, you should understand how each of thesemethods combines observations, how each method treats duplicate values ofcommon variables, and how each method treats missing values or nonmatchedvalues of common variables. Some of the methods also require that you preprocessyour data sets by sorting them or by creating indexes. See the descriptionof each method in Combining SAS Data Sets: Methods.

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One-to-One

In the following example, observations in data sets SALARY and TAXESare related by common values for EmployeeNumber.

One-to-One Relationship

One-to-Many and Many-to-One

In the following example, observations in data sets ONE and TWO arerelated by common values for variable A. Values of A are unique in data setONE but not in data set TWO.

One-to-Many Relationship

In the following example, observations in data sets ONE, TWO,andTHREE are related by common values for variable ID. Values of ID are uniquein data sets ONE and THREE but not in TWO. For values 2 and 3 of ID, a one-to-manyrelationship exists between observations in data sets ONE and TWO, and a many-to-onerelationship exists between observations in data sets TWO and THREE.

One-to-Many and Many-to-One Relationships

Many-to-Many

In the following example, observations in data sets BREAKDOWN and MAINTENANCEare related by common values for variable Vehicle. Values of Vehicle are notunique in either data set. A many-to-many relationship exists between observationsin these data sets for values AAA and CCC of Vehicle.

Many-to-Many Relationship

Overview

Once you have established data relationships, the next step is to determinethe best mode of data access to relate the data. You can access observationssequentially in the order in which they appear in the physical file. Or youcan access them directly, that is, you can go straight to an observation ina SAS data set without having to process each observation that precedes it.

Sequential Access

The simplest and perhaps most common way to process data with a DATAstep is to read observations in a data set sequentially. You can read observationssequentially using the SET, MERGE, UPDATE, or MODIFY statements. You can alsouse the SAS File I/O functions, such as OPEN, FETCH, and FETCHOBS.

Direct Access

• by an observation number
• by the value of one or more variables through a simple or compositeindex.

To access observations directly by their observation number, use thePOINT= option with the SET or MODIFY statement. The POINT= option names avariable whose current value determines which observation a SET or MODIFYstatement reads.

To access observations directly based on the values of one or more specifiedvariables, you must first create an index for the variables and then readthe data set using the KEY= statement option with the SET or MODIFY statement.An index is a separate structure that contains the data valuesof the key variable or variables, paired with a location identifier for theobservations containing the value.

Note: You can also use the SAS FileI/O functions such as CUROBS, NOTE, POINT and FETCHOBS to access observationsby observation number.

• concatenating
• interleaving
• one-to-onereading
• one-to-one merging
• matchmerging
• updating.

Concatenating

Concatenating Two Data Sets

Interleaving

Interleaving Two Data Sets

One-to-One Reading and One-to-One Merging

One-to-One Reading and One-to-One Merging

Match-Merging

Match-Merging Two Data Sets

Updating

UPDATE replaces an existing file with a new file, allowing you to add,delete, or rename columns. MODIFY performs an update in place by rewritingonly those records that have changed, or by appending new records to the endof the file.

Note that by default, UPDATE and MODIFY do not replace nonmissing valuesin a master data set with missing values from a transaction data set.

Updating a Master Data Set

Let's apply the previous example to these two datasets. We see that the unmatched records have been dropped out in the merged data set, since the where statement eliminated them.

What if we want to keep all the records from both datasets even they do not match? The following proc sql does it in a more complex way. Here we create two new variables. One is indic, an indicator variable that indicates whether an observation is from both datasets, 1 being from both datasets and 0 otherwise. Another variable is fid, a coalesce of famid from both datasets. This gives us more control over our datasets. We can decide if we have a mismatch and where the mismatch happens.

5. Producing all the possible distinct pairs of the values in a column

Let's say that we have a data set containing a variable called city. We want to create allpossible distinct pairs of cities appeared in the variable. This would be reallytricky to do if we only use a data step. But it can be accomplished fairly straightforwardly with SAS proc sql asshown below. Proc sql is first used to select distinctcities and to save them to a new dataset. It is used again to create alldistinct pairs of cities. As shown below, there are seven different places. Therefore there will be 7*6/2 =21 pairs of cities.

Application requirements vary, but there are common factors for allapplications that access, combine, and process data. Once you have determinedwhat you want the output to look like, you must

• determine how the input data is related
• ensure that the data isproperly sorted or indexed, if necessary
• select the appropriate access method to process the input data
• select theappropriate SAS tools to complete the task.

You must be able to identify the existing relationships in your data.This knowledge is crucial for understanding how to process input data in orderto produce desired results. All related data fall into one of these four categories,characterized by how observations relate among the data sets:

• one-to-one
• one-to-many
• many-to-one
• many-to-many.

Toobtain the results you want, you should understand how each of thesemethods combines observations, how each method treats duplicate values ofcommon variables, and how each method treats missing values or nonmatchedvalues of common variables. Some of the methods also require that you preprocessyour data sets by sorting them or by creating indexes. See the descriptionof each method in Combining SAS Data Sets: Methods.

New customers will no longer be able to purchase Tableau Desktop, but will be purchasing the Tableau Creator offering which includes Tableau Desktop, Tableau Prep and a Creator license of Tableau Online or Tableau Server. With Tableau Prep Conductor, you can easily publish and run flows in your server environment. Share your data sources securely using Tableau Server or Tableau Online. Create an environment where everyone in your organization can work with prepped and up-to-date data. Tableau Prep Conductor is part of the Data Management Add-on. Prep Conductor is automatically enabled on your Tableau Online site after you purchase the Data Management Add-on. For information on how to purchase the Data Management Add-on, contact your account manager. Step 1: Install Tableau Prep Builder Install Tableau Prep Builder on your local machine. Tableau Software. Self-service data preparation Regardless of your skill level with data preparation, Tableau Prep makes it easier to combine, clean, shape, and share your data.

One-to-One

In the following example, observations in data sets SALARY and TAXESare related by common values for EmployeeNumber.

One-to-One Relationship

One-to-Many and Many-to-One

In the following example, observations in data sets ONE and TWO arerelated by common values for variable A. Values of A are unique in data setONE but not in data set TWO.

One-to-Many Relationship

In the following example, observations in data sets ONE, TWO,andTHREE are related by common values for variable ID. Values of ID are uniquein data sets ONE and THREE but not in TWO. For values 2 and 3 of ID, a one-to-manyrelationship exists between observations in data sets ONE and TWO, and a many-to-onerelationship exists between observations in data sets TWO and THREE.

One-to-Many and Many-to-One Relationships

Many-to-Many

In the following example, observations in data sets BREAKDOWN and MAINTENANCEare related by common values for variable Vehicle. Values of Vehicle are notunique in either data set. A many-to-many relationship exists between observationsin these data sets for values AAA and CCC of Vehicle.

Many-to-Many Relationship

Overview

Once you have established data relationships, the next step is to determinethe best mode of data access to relate the data. You can access observationssequentially in the order in which they appear in the physical file. Or youcan access them directly, that is, you can go straight to an observation ina SAS data set without having to process each observation that precedes it.

Sequential Access

The simplest and perhaps most common way to process data with a DATAstep is to read observations in a data set sequentially. You can read observationssequentially using the SET, MERGE, UPDATE, or MODIFY statements. You can alsouse the SAS File I/O functions, such as OPEN, FETCH, and FETCHOBS.

Direct Access

• by an observation number
• by the value of one or more variables through a simple or compositeindex.

To access observations directly by their observation number, use thePOINT= option with the SET or MODIFY statement. The POINT= option names avariable whose current value determines which observation a SET or MODIFYstatement reads.

To access observations directly based on the values of one or more specifiedvariables, you must first create an index for the variables and then readthe data set using the KEY= statement option with the SET or MODIFY statement.An index is a separate structure that contains the data valuesof the key variable or variables, paired with a location identifier for theobservations containing the value.

Note: You can also use the SAS FileI/O functions such as CUROBS, NOTE, POINT and FETCHOBS to access observationsby observation number.

• concatenating
• interleaving
• one-to-onereading
• one-to-one merging
• matchmerging
• updating.

Concatenating

Concatenating Two Data Sets

Interleaving

Interleaving Two Data Sets

One-to-One Reading and One-to-One Merging

One-to-One Reading and One-to-One Merging

Match-Merging

Match-Merging Two Data Sets

Updating

UPDATE replaces an existing file with a new file, allowing you to add,delete, or rename columns. MODIFY performs an update in place by rewritingonly those records that have changed, or by appending new records to the endof the file.

Note that by default, UPDATE and MODIFY do not replace nonmissing valuesin a master data set with missing values from a transaction data set.

Updating a Master Data Set

Using Statements and Procedures

PROC SQL is the SAS implementationof Structured Query Language. In addition to expected SQL capabilities, PROCSQL includes additional capabilities specific to SAS, such as the use of formatsand SAS macro language.

Using Error Checking

You can use the _IORC_ automatic variable and the SYSRC autocall macroto perform error checking in a DATA step. Use these tools with the MODIFYstatement or with the SET statement and the KEY= option. For more informationabout these tools, see Error Checking When Using Indexes to Randomly Access or Update Data.

• Know the structure and the contents of the data sets.
• Look at sources of commonproblems.
• Ensure that observations are in the correct order, or that theycan be retreived in the correct order (for example, by using an index).
• Test your program.

One To Many Merge Sas Proc Sql

Knowing the Structure and Contents of the Data Sets

To help determine how your data are related, look at the structure ofthe data sets. To see the data set structure, execute the DATASETS procedure,the CONTENTS procedure, or access the SAS Explorer window in your windowingenvironment to display the descriptor information. Descriptor informationincludes the number of observations in each data set, the name and attributesof each variable, and which variables are included in indexes. To print asample of the observations, use the PRINT procedure or the REPORT procedure.

You can also use functions such as VTYPE, VLENGTH, and VLENGTHX to showspecific descriptor information. For a short description of these functions,see the Variable Information functions in Functions and CALL Routines. For complete information about these functions, see 'Functions andCALL Routines' in SAS Language Reference: Dictionary.

Looking at Sources of Common Problems

• variables that have the same name but that represent differentdata

  SAS includes only one variable of a given name in the new data set.If you are merging two data sets that have variables with the same names butdifferent data, the values from the last data set that was read are writtenover the values from other data sets.

  To correct the error, you can rename variables before you combine thedata sets by using the RENAME= data set option in the SET, UPDATE, or MERGEstatement, or you can use the DATASETS procedure.

• common variables with the same data but differentattributes

  The way SAS handles these differences depends on which attributes aredifferent:

  • type attribute

    If the type attribute is different, SAS stops processing the DATA stepand issues an error message stating that the variables are incompatible.

    To correct this error, you must use a DATA step to re-create the variables.The SAS statements you use depend on the nature of the variable.

  • length attribute

    If the length attribute isdifferent, SAS takes the length from thefirst data set that contains the variable. In the following example, all datasets that are listed in the MERGE statement contain the variable Mileage.In QUARTER1, the length of the variable Mileage is four bytes; in QUARTER2,it is eight bytes and in QUARTER3 and QUARTER4, it is six bytes. In the outputdata set YEARLY, the length of the variable Mileage is four bytes, which isthe length derived from QUARTER1.

    To override the default and set the length yourself, specify the appropriatelength in a LENGTH statement that precedes the SET, MERGE, MODIFY,or UPDATE statement.

  • label, format, and informat attributes

    If any of these attributes are different, SAS takes theattribute fromthe first data set that contains the variable with that attribute. However,any label, format, or informat that you explicitly specify overrides a default.If all data sets contain explicitly specified attributes, the one specifiedin the first data set overrides the others. To ensure that the new outputdata set has the attributes you prefer, use an ATTRIB statement.

    You can also use the SAS File I/O functions such as VLABEL, VLABELX,and other Variable Information functions to access this information. Fora short description of these functions, see the Variable Information functionsin Functions and CALL Routines by Category. For complete information about these functions, see 'Functions and CALLRoutines' in SAS Language Reference: Dictionary.

Sas One To Many Merge Not Working

Ensuring Correct Order

If you use BY-group processing withthe UPDATE, SET, and MERGE statementsto combine data sets, ensure that the observations in the data sets are sortedin the order of the variables that are listed in the BY statement, or thatthe data sets have an appropriate index. If you use BY-group processing ina MODIFY statement, your data does not need to be sorted, but sorting thedata improves efficiency. The BY variable or variables must be common to bothdata sets, and they must have the same attributes. For more information, see BY-Group Processing in the DATA Step.

One To Many Merge Sas

Testing Your Program

One To Many Join Sas