What's New in SAS Studio 3.4 :: SAS(R) Studio 3.4: User's Guide

Support for z/OS Files

SAS Studio 3.4 provides native file support for z/OS environments. Using SAS Studio 3.4, you can easily access any of your existing z/OS files. You can also use the interface to create sequential and partitioned z/OS data sets.

For more information, see SAS Studio: Administrator's Guide.

Importing Data

You can import Excel files, delimited files, and other DBMS file types. If you are using the Visual Programming perspective, importing data can be a node in your process flow. For more information, see Importing Data.

Enhancements to File Navigation

In SAS Studio, the navigation tree has been enhanced to make it more clear where your files actually reside. If you are running the SAS Studio Enterprise Edition or the SAS Studio Basic Edition, your SAS Studio administrator can control the starting point of the navigation tree. For more information, see SAS Studio: Administrator's Guide.

General Enhancements

In the code editor, autocomplete is now available for librefs and table names. For more information, see Using the Autocomplete Feature.
When you run a query or import data in SAS Studio, an output data set is created. Some predefined tasks (such as the Rank task) also create output data sets. In SAS Studio, these output data sets open on the Output Data tab.
Process flows are available if you are using the Visual Programmer perspective. Starting in this release, you can drag a data set from the Libraries pane and add it to a process flow. For more information, see Add Data to the Process Flow.
You can now open any file in the Text Viewer, which enables you to quickly see the data or code in a file. This functionality might be useful if you are trying to determine the delimiter used in a TXT file.

SAS Studio Tasks

New Tasks

Here are the new tasks for this release:

The Describe Missing Data task displays the frequencies and percentages of missing values for each selected variable. If two or more variables are assigned to this task, the task displays the pattern of missing data across variables. For more information, see Describe Missing Data Task.
The Filter Data task enables you to quickly create a basic filter to subset your input data source. For more information, see Filter Data Task.
There are three new forecasting tasks.
- The Time Series Data Preparation task turns time-stamped transaction data into equally spaced time series data. This format is required for further time series analysis. For more information, see Time Series Data Preparation Task.
- The Time Series Exploration task creates graphs and statistics that enable you to view and analyze your time series data. For more information, see Data Exploration Task.
- The Modeling and Forecasting task creates forecasting models that use your time series data. For more information, see Modeling and Forecasting Task.
The Partition Data task enables you to create partitions of data by simple or stratified random sampling. For more information, see Partition Data Task.
The SAS Rapid Predictive Modeler is designed to build models for data mining classification and regression problems. For more information, see Rapid Predictive Modeler.
The Stack/Split Columns task combines the values of several columns into a single new column. You can use this task to convert multivariate data to a univariate organization. For more information, see Stack Columns Task.
The Standardize Data task enables you to center or standardize one or more numeric variables by using a variety of methods. The standardized variables are saved in an output data set. For more information, see Standardize Data Task.
The Transform Data Task task enables you to transform one or more variables in the input data set. These transformed variables are saved to an output data set. For more information, see Transform Data Task.

Enhanced Tasks

The user interface for these tasks has been simplified, so you can more quickly generate results.

Updates to the Common Task Model

If you create custom tasks for your site, here are the changes to the Common Task Model (CTM) in SAS Studio 3.4.

The combobox control has a new editable attribute, which enables users to enter a value in the combobox control.
In the numstepper control, you can now define customized messages by using the new promptMessage and rangeMessage attributes.
In the Velocity code, the new getRowsCount method and columnExists method enable you to learn more about the Datasource object.
A Velocity variable is now created for the string input type.

For more information, see SAS Studio: Developer's Guide.

Documentation Enhancements

The SAS Studio: User's Guide now includes a “Customized Output Environment” appendix.

You must customize the SAS Studio output environment when you want to perform any of these tasks:

generate output for other output destinations
send results to another location
use a custom style for your output
use an image format other than the default
create a drill-down graph
create an animated GIF or SVG image

For more information, see Customized Output Environment.

What’s New in SAS Studio 3.4

Overview