![]() ![]() The email subject personalization guide is available as a separate article.Īs soon as Mail Merge Toolkit is installed, you can attach files to the message. Insert data fields into the message subject, so that not only the contents of a message but also its subject is personalized. This app allows you to realize really personalized mass mailing easily and efficiently by providing a set of requested and unique features:Īs soon as you need to personalize part of the message subject or the whole subject line, you need Mail Merge Toolkit. Mail Merge Toolkit is a powerful add-in for Microsoft Office extending the mail merging capabilities in Microsoft Outlook, Microsoft Word and Microsoft Publisher. Reporting solutions, add-ons for Microsoft Excel, Outlook Express Web Analytics, HelpDesk and Workflow solutions for SharePoint Multiple Exchange mailboxes search with a range of featuresĭownload emails from external POP3 servers to Exchange Save, remove and manage attachments on server sideĪutomatically print emails and attachments on Exchange Server Solutions for any environment based on Microsoft Exchange Server Prints emails and attachments automaticallyĢ1 apps to improve your daily work with Outlook Personalize emails with advanced mail mergingĬovers all attachments needs: extract, ZIP, manage Upon execution of the above SAS program with the above changed part, we get the following output.The line of Microsoft Outlook tools and appsġ4 add-ins in one bundle for the best priceįinds and removes duplicated in emails and postsįinds and removes duplicated contacts, tasks, etc In the below example, the IN= value keeps only the observations where the values from both the data sets SALARY and DEPT match. The merge statement of the SAS program needs to be changed. To avoid the missing values in the result we can consider keeping only the observations with matched values for the common variable. When the above code is applied, we get the below result. ExampleĬonsider the case of employee ID 3 missing from the dataset salary and employee ID 6 missing form data set DEPT. In such cases the data sets still get merged but give missing values in the result. There may be cases when some values of the common variable will not match between the data sets. Please note that the observations in both the datasets are already sorted in ID column. The above result is achieved by using the following code in which the common variable (ID) is used in the BY statement. The final data set will still have one observation per employee but it will contain both the salary and department variables. In this case to get the complete information for each employee we can merge these two data sets. ExampleĬonsider two SAS data sets one containing the employee ID with name and salary and another containing employee ID with employee ID and department. Let us understand data merging with the help of an example. The basic syntax for MERGE and BY statement in SAS is −įollowing is the description of the parameters used −ĭata-set1,Data-set2 are data set names written one after another.Ĭommon Variable is the variable based on whose matching values the data sets will be merged. input data sets must be sorted by the common variable(s) that will be used to merge on.input data sets must have at least one common variable to merge on.There are two Prerequisites for merging data sets given below − It is because the variables form both data sets get merged as one record based when there is a match in the value of the common variable. The total number of observations in the merged data set is often less than the sum of the number of observations in the original data sets. ![]() This is done using the MERGE statement and BY statement. Multiple SAS data sets can be merged based on a specific common variable to give a single data set. ![]()
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