Transpose function neooffice license#

MPLv2.0 (secondary license GPL, LGPLv3+ or Apache License 2.0)

Transpose function neooffice windows#

Linux, macOS, Microsoft Windows and FreeBSD Other options available in the PROC TRANSPOSE statement that can be found in the SAS Help Guide. The resulting dataset will have one row for each variable identified in the VAR statement. For wide-to-long datasets, there are usually multiple variables in the VAR statement.For long-to-wide datasets, there is usually one variable in the VAR statement.

These are the values that will appear in the cells of the transposed variables.

The VAR statement is where you actually tell SAS what variables you want transposed.

However, if you do supply an ID variable, it will determine the column structure.

For wide-to-long transposes, you typically do not need an ID variable.

There will be one column for each unique value of the ID variable (or if multiple ID variables are present, one column for each unique combination of values).

For long-to-wide transposes, the ID variable(s) determine the structure of the columns in the transposed dataset.

The ID statement assigns names to the transposed value columns that match the values in the variable listed in the ID statement.

For wide-to-long transposes, the BY variable(s) determine the row structure of the long data that is, it determines the repetition of the rows.

For long-to-wide transposes, the BY variable(s) should uniquely identify each row.

Your data must be sorted on your BY variables before running PROC TRANSPOSE. You can include more than one variable in the BY statement.

The BY statement is used to determine the row structure of the transposed dataset.

The OUT keyword says that the transposed dataset should be created as a new dataset called New-dataset-name.

The PROC TRANSPOSE statement tells SAS to execute the transpose procedure on an existing dataset called Dataset-name.

The general format of PROC TRANSPOSE is: PROC TRANSPOSE DATA=Dataset-name OUT=New-dataset-name In SAS, the TRANSPOSE procedure can perform simple transposes, wide-to-long, and long-to-wide restructuring of datasets.

Statistical software packages typically require data to be in 'wide' format for procedures like: You can also imagine the reverse situation where you have one line of data per observation (a wide dataset) but you want multiple lines of data with unique values of a certain variable (i.e., a long dataset). Statistical software packages typically require data to be in 'long' format for procedures like: If you want to compare the patientвЂ™s weight at visit 1 to their weight at visit 2, you might want to transpose the data so that each patient has one line of data that includes both weight values (i.e., a wide dataset), like below: Patient1 A single visit record might contain information about the patient's name, the date of the visit, and the weight of the patient during that visit. That is, each row of the 'appointments' dataset corresponds to visit. As patients come into the clinic, each visit is recorded in the clinicвЂ™s records. Panel (or longitudinal) data is often recorded in this 'long' format.Д¬onsider a clinic where patients come in for appointments. Notice that the first row of matrix 1 becomes the first column of matrix 2.ДЄ 'long' dataset contains more than one row per subject, and uses a unique ID to identify each subject. Transposing this matrix would turn it into a 3x2 matrix (3 rows, 2 columns): 1 4 If applied to a traditional dataset, this would make it so that there was one row per variable, and one column per subject.ДЇor example, consider the following 2x3 matrix (2 rows, 3 columns): 1 2 3 The simplest possible case of transposing switches the rows and columns of a matrix. This is where transposing or reshaping a dataset comes in to play. Furthermore, we may need our data to be arranged in a particular format in order to use a specific analysis or procedure. However, data can be recorded or collected in many different arrangements, depending on what is convenient or cost-effective for the data collector. Ideally, datasets are structured so that each row corresponds to one unique subject or object, and each column corresponds to a single variable.