![]() Now if you use PROC IMPORT to let SAS guess at how to format your data from Excel of text files then you can end up with the variable in one file being of length 10 and in another file of length 20. SAS has an extremely nasty habit when reading from Excel or external databases of permanently attaching formats to character variables. Is there a quicker way to solve this problem? In a perfect world, I would go back and read-in the data for each worksheet and set the attributes. There are only a handful of character variables that I would need to be worried about here. Not surprisingly, according to PROC CONTENTS, the length of this variable is different in each of the 5 merged datasets (Time period 1, 2, 3, 4, 5). No truncation occurs in these datasets. The truncation only occurs in the final dataset, after these 5 datasets are joined into one.Ĭan I assign length, or other attributes, to certain variables in the DATA step before the SET statement for the final dataset? I am seeing truncated values in at least 1 character variable - an open text box - in the final dataset only. I used PROC IMPORT for each of 25 Excel worksheets (5 files w/ 5 tabs each). I will never do this again! There are 500 variables and 2000 observations. Physical activity, nutrition, personal health, etc.). Each of these 5 datasets was the result of a merge of 5 datasets (e.g. ![]() That sequential comparison process continues until all rows are read from each table listed on the merge statement.I am stacking 5 datasets into 1 (Time periods 1-5 = Final dataset). Again the values in name match so both rows are read into the PDV, and so on. SAS returns to the top of the data step for the next iteration and advances to row two in both tables. If they match then both rows are read into the PDV, additional statements are executed, and at the end of the data step the row is written to the output table. In the execution phase, SAS begins by examining the by column value for the first row in each table. Finally any other compile-time statements are processed. If there are any other statements in the data step that create new columns, they are also added to the PDV. Any additional columns and their attributes that are not already in the PDV are added. SAS then examines the second table on the merge statement. In the compilation phase, all of the columns from the first table listed on the merge statement and their attributes are added to the PDV. SAS simply compares rows sequentially as it reads from the multiple tables matching rows based on the value of the common column. The data step merge process is very similar to how you would envision matching two lists by hand if the values are in sorted order. Let's see how SAS processes the code behind the scenes. Both tables are listed in the merge statement, and the common column "name" is listed in the by statement. So here's the data step merge that will join our two tables. Typically you would use PROC SORT steps to arrange the rows of the input tables by the matching column before the data step merge. When a BY statement is used in a data step, the data must be in sorted order. ![]() You can list multiple tables on the merge statement as long as each table has the common matching column that is listed on the by statement. To merge tables in the data step, you use a merge statement rather than a set statement. This is a one-to-one merge since each value of name is in both tables. ![]() Notice that the name column is in both tables, and both tables are sorted by name. Suppose we want to combine class and class teachers in a single table. Let's look at an example of merging tables.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |