An Introduction to SAS Visual Analytics: How to Explore Numbers, Design Reports

✍ Scribed by Tricia Aanderud, Rob Collum, Ryan Kumpfmiller

• Publisher: SAS Institute
• Year: 2017
• Tongue: English
• Leaves: 293
• Edition: 1
• Category: Library

✦ Synopsis

When it comes to business intelligence and analytical capabilities, SAS Visual Analytics is the premier solution for data discovery, visualization, and reporting. An Introduction to SAS Visual Analytics will show you how to make sense of your complex data with the goal of leading you to smarter, data-driven decisions without having to write a single line of code – unless you want to! You will be able to use SAS Visual Analytics to access, prepare, and present your data to anyone anywhere in the world.

SAS Visual Analytics automatically highlights key relationships, outliers, clusters, trends and more. These abilities will guide you to critical insights that inspire action from your data. With this book, you will become proficient using SAS Visual Analytics to present data and results in customizable, robust visualizations, as well as guided analyses through auto-charting. With interactive dashboards, charts, and reports, you will create visualizations which convey clear and actionable insights for any size and type of data.

This book largely focuses on the version of SAS Visual Analytics on SAS 9.4, although it is available on both 9.4 and SAS Viya platforms. Each version is considered the latest release, with subsequent releases planned to continue on each platform; hence, the Viya version works similarly to the 9.4 version and will look familiar. This book covers new features of each and important differences between the two.

With this book, you will learn how to:

Build your first report using the SAS Visual Analytics Designer
• Prepare a dashboard and determine the best layout
• Effectively use geo-spatial objects to add location analytics to reports
• Understand and use the elements of data visualizations
• Prepare and load your data with the SAS Visual Analytics Data Builder
• Analyze data with a variety of options, including forecasting, word clouds, heat maps, correlation matrix, and more
• Understand administration activities to keep SAS Visual Analytics humming along
• Optimize your environment for considerations such as scalability, availability, and efficiency between components of your SAS software deployment and data providers

✦ Table of Contents

contents
about this book
Is this book for you?
Prerequisites
Scope of this book
About the examples
Software used to develop the book's content
Example data and reports
We Want to Hear from You
Subscribe to the SAS Learning Report
Publish with SAS
acknowledgments
Tricia Aanderud
Rob Collum
Ryan Kumpfmiller
about these authors
introduction
Application introduction
Understanding in-memory data storage
Understanding the application
Figure 1 Application overview
How to use this book
part one
accessing content
Methods of accessing content
Accessing content with a web browser
Accessing content through the public portal
Figure 1.1 SAS Visual Analytics in a public kiosk
Accessing content with the mobile bi app
Understanding roles
Accessing SAS Visual Analytics
Transformation of the homepage
Figure 1.2 Classic mode homepage in release 7.3
Figure 1.3 Modern mode homepage in release 7.3
Figure 1.4 Homepage in SAS Visual Analytics 8.1
Understanding SAS home
Figure 1.5 Modern mode homepage in SAS Visual Analytics 7.3
Opening a report
Creating a shortcut
Creating a collection or content tile
Using the report viewer
Figure 1.6 Report Viewer
Navigating a report
Opening other sections
Figure 1.7 Open other report sections
View additional report information
Other report viewer options
References
building your first report
Accessing the designer
Figure 2.1 How to get to the Designer from the Hub
Introducing the designer layout
Figure 2.2 Areas of the Designer
Using the canvas
Figure 2.3 Tiers of the canvas
Using the left pane
Figure 2.4 Left pane of the Designer
Using the right pane
Figure 2.5 Tabs on the right pane of the Designer
Figure 2.6 Report and object level Styles tabs
Building your first report
Figure 2.7 Final result of our first report
Adding a data source
Working with data sources
Figure 2.8 Bringing in a data source to the Designer
Figure 2.9 Adding new data items
Data item properties
Figure 2.10 Property options for data items
Creating new data items
Change the data item format
Using a derived data item
Working with the layout
Starting the layout
Populating your objects
Improving the data object appearance
Changing the properties
Changing the appearance
Adding a reference line
Figure 2.11 Bar chart with a reference line added
Adding data to other objects
Pie chart (top right corner)
Line chart (lower left corner)
Bar chart (lower right corner)
Figure 2.12 Report with all chart objects and data items added
Working with data objects
Using the Filter tab
Filtering with report and section prompts
Adding a slider object
Adding object interactions
Create an interaction
Using the interactions view
Figure 2.13 Interactions view
Saving the report
Figure 2.14 Save As window
Reviewing the report
References
building your first dashboard
Figure 3.1 Difference in reports and dashboards
Dashboard building process
Understanding your customer
Establishing objectives
Determine supporting information
Planning the data and data objects
Creating a mock layout
Considering your layout
Figure 3.2 Dashboard layout
Creating a workable layout
Figure 3.3 Determining interactivity
Tips for more useable dashboards
Building the dashboard
Figure 3.4 Regional manager dashboard and sales rep report
Adding the data objects
Creating new data items
Working with data items
Figure 3.5 Showing and hiding data items
Applying a format
Creating custom categories
Changing the aggregation method
Figure 3.6 Measures
Create calculated items
Figure 3.7 Creating a calculated item
Creating aggregated measures
Figure 3.8 Calculated item versus aggregated measure
Figure 3.9 Aggregated measurement types
Creating the layout
Figure 3.10 Creating the layout
Adding sections or pages
Adding containers
Figure 3.11 Container layout
Adding section filters
Figure 3.12 Adding the section filters
Working with data objects
Using gauges in a container
Figure 3.13 Dashboard gauges
Using parameters for targets
Understanding parameters
Figure 3.14 Using parameters for targets
Creating a parameter
Adding data objects to a container
Using a targeted bar chart
Figure 3.15 Targeted bar chart show individual performance
Using a dual axis bar-line chart
Figure 3.16 Dual axis bar chart allows comparisons
Adding controls to a container
Figure 3.17 Establishing interactivity between objects
Adding a list table
Figure 3.18 Using display rules with a list table
Linking to another section
Figure 3.19 Linking objects to sections
Understanding section linking
Figure 3.20 Applying section filtering
Figure 3.21 Removing filtering
Applying section linking
Other dashboard enhancements
Adding text boxes
Adding artwork
Figure 3.22 Using image and text objects in your report
Embedding a stored process
Summary
References
using the data builder
Using the Data Builder
Creating a data query
How does the Data Builder work?
Before you begin
Opening the Data Builder
Figure 4.1 Getting to the Data Builder
Understanding the Data Builder layout
Figure 4.2 Data Builder layout
Building your first query
Creating the query
Modify the query
Adding a numeric calculation
Adding a character data item
Filtering the data
Adding a WHERE clause
Adding a HAVING clause
Create a summary data query
Steps to summarize data
Updating the code
Figure 4.3 Data Builder layout
Scheduling a query
Figure 4.4 Schedule window
Figure 4.5 New Time Event window
References
part two
visualizing your data
Elements of an effective data visualization
Your message: know your point
Your audience: know who is listening
Your technique: follow the KISS principle
Line charts
Interpreting the results
Figure 5.1 Line charts display trends
Line charts: guidelines
Use 0 as y-axis value
Figure 5.2 Adding drama to a line chart
Remember the KISS principle
Figure 5.3 Keep your categories simple
Be careful with stacking area plots
Figure 5.4 Overlay stacked line chart
Figure 5.5 Use the lattice feature to understand individual categories
Line charts: tips and tricks
Tip 1: Dealing with a long timeline
Figure 5.6 Sliding window to see more data
Tip 2: Avoiding chart junk
Figure 5.7 Manage your data
Tip 3: Transparency can be your enemy
Figure 5.8 Colors do not match the Legend
Figure 5.9 Stack the grouped items to clarify your point
Tip 4: Keeping the date intervals
Changing the data
Figure 5.10 Add dates without values
Figure 5.11 Modifying your data source
Using a time series chart
Figure 5.12 Time series plot
Bar charts
Interpreting the results
Figure 5.13 Example bar chart
Bar charts: guidelines
Choosing a line chart or a bar chart
Figure 5.14 Bar chart versus a line chart
Choosing a grouped chart or a stacked chart
Figure 5.15 Part to the whole
Figure 5.16 Contribution by category
Bar charts: tips and tricks
Tip 1: Rescue your long labels and your viewer
Figure 5.17 Use a horizontal bar chart
Figure 5.18 Using the ranks pane
Tip 2: Show the complete percentage
Figure 5.19 Change the grouping scale to show 100%
Tip 3: Using a butterfly chart
Figure 5.20 Using a butterfly chart
Pie and donut charts
Interpreting the results
Figure 5.21 Easy-to-understand pie charts
Figure 5.22 Table compared to a pie chart
Figure 5.23 Example of why pie charts are ineffective
Pie and donut charts: guidelines
Removing the legend
Figure 5.24 Good pie charts don't need a legend
Is the comparison effective?
Figure 5.25 Too many comparisons
Pie and donut charts: tips and tricks
Tip 1: Limit the categories to focus the reader’s attention
Figure 5.26 When a bar chart works better
Tip 2: Keep categories a consistent color
Figure 5.27 Setting color-mapped values
Tip 3: Pie chart as a dashboard gauge
Treemaps
Interpreting the results
Figure 5.28 Treemap example
Treemaps: guidelines
Add two measures – one for size and one for difference
Add the legend
Figure 5.29 Find the right location for your legend
Treemaps: tips and tricks
Tip 1: Gradient values are easier to interpret
Figure 5.30 Gradients are easier to understand
Tip 2: Hierarchies make it easier to navigate the tree
Figure 5.31 Users can drill-down with a hierarchy
Waterfall charts
Interpreting the results
Figure 5.32 Example waterfall chart shows revenue change
Waterfall charts: guidelines for use
Add the initial and final values
Figure 5.33 Adding the initial and final values
Adding the response sign
Figure 5.34 Creating a calculated item
Waterfall charts: tips and tricks
Tip 1: Consider a summary data source
Figure 5.35 Wide data
Figure 5.36 Tall data
Figure 5.37 Creating summary data
Tip 2: Use a custom sort for the category
Figure 5.38 Use a custom sort
Tip 3: Use section filtering for different data sources
Figure 5.39 Section filtering for different data sources
Figure 5.40 Mapping data sources to the controls
Gauges
Interpreting results
Figure 5.41 Using dashboard gauges
Gauges: Guidelines
Choose the correct gauge
Figure 5.42 Available gauges
Use data that makes sense
Figure 5.43 Gauges that do not make sense
Gauges: tips and tricks
Tip 1: Use display rules
Figure 5.44 Setting gauge by 20% intervals
Figure 5.45 Setting gauge by single intervals
Figure 5.46 Auto populate intervals
Tip 2: Add a shared rule
Tables and cross tabs
Interpreting the results
Figure 5.47 Sales rep ratings in a table
Figure 5.48 Using a hierarchy with a crosstab
Tables and crosstabs: guidelines for use
Tables and crosstabs: tips and tricks
Tip 1: Add a sparkline or gauge
Tip 2: Use a small table for single values
Figure 5.49 Adding a single value
Tip 3: Check your aggregations and derived measures
Figure 5.50 Adding a derived data items
Figure 5.51 Derived measures in a table
Bubble plots
Interpreting the results
Figure 5.52 Bubble plot
Bubble plots: guidelines
Data preparation is key
A legend is a requirement
Bubble plots: tips and tricks
Tip 1: Use the transparency setting so users see all the data
Figure 5.53 Use transparency for multiple bubbles
Tip 2: Animating the data
Figure 5.54 Use animation wisely
References
the where of data
Using geospatial data effectively
When location is not part of the data story
Figure 6.1 Location is not part of this data story
When location is the data story
Figure 6.2 Location matters in this story
Preparing data for geospatial visualizations
Creating a predefined geographic data item
Creating a predefined geographic data item
Dealing with location accuracy
Figure 6.3 MAPSGFK world data set values
Creating a custom geospatial data item
Figure 6.4 Airports with latitude and longitude
Creating a custom geographic data item
Figure 6.5 Adding a custom data point
Displaying geospatial objects
Get to the point with geo coordinate data objects
Figure 6.6 F5/EF5 tornado locations
Tip 1: Dealing with odd locations
Figure 6.7 Tornados in the ocean
Tip 2: Controlling the data
Figure 6.8 There is too much data at one time!
Figure 6.9 Add filters to keep data visualization manageable
Compare area with geo regional data objects
Figure 6.10 Understanding regional events
Tip 1: Improving your geo regional map
Tip 2: Adding rich details for exploration
Figure 6.11 Use a pop-up window to provide more details
Adding an info window to your map
Show overall trends with bubble plots data objects
Tip 1: Ensure that the legend is visible
Tip 2: Watch the default colors
Expanding location intelligence
Understanding details about mapping technologies
References
approachable analytics
About the Explorer
Figure 7.1 Explorer layout
Figure 7.2 Creating visualizations
Automatic chart feature
Figure 7.3 Using the automatic chart feature
Figure 7.4 Removing roles in an automatic chart
Box plots
Interpreting the results
Figure 7.5 Box plot example
Figure 7.6 Box plot example with outliers
Figure 7.7 Box plot ignoring outliers
Adding more data items
Figure 7.8 Box plots with a category
Figure 7.9 Box plot with a category and multiple measures
When to use Box Plots
Histograms
Changing objects in a visualization
Figure 7.10 Where to change an object
Figure 7.11 Change visualization to histogram
Histogram options
Figure 7.12 Histogram example
Using a correlation matrix
Calculating a correlation
Figure 7.13 How SAS categorizes correlation values
Understanding the matrix
Figure 7.14 Correlation matrix example
Figure 7.15 Correlation example between two sets of measures
Interpreting a correlation value
Forecasting
Working with the forecasting option
Figure 7.16 Forecasting example
Figure 7.17 Forecasting options
How is the data modeled?
Figure 7.18 Forecast analysis tab
Look for underlying factors
Figure 7.19 Forecasting with underlying factors
Using the scenario analysis
Figure 7.20 Forecasting with scenario analysis
Figure 7.21 Forecasting with goal seeking
Word clouds
Loading social media data
Figure 7.22 How to load social media data
Figure 7.23 Import twitter data window
Setting up the word cloud
Using category values
Figure 7.24 Word cloud example
Figure 7.25 Word cloud with a measure
Using text analytics
Figure 7.26 Using text analytics
Figure 7.27 Text analytics with sentiment analysis
Scatter plot
Data analysis
Figure 7.28 Scatter plot example
Figure 7.29 Scatter plot with a fit line
Figure 7.30 Scatter plot with best fit option
Interpreting lines of best fit
Adding categories
Figure 7.31 Scatter plot with categories
Heat map
Data analysis
Figure 7.32 Heat map example
Figure 7.33 Heat map with fit line
Using a category
Figure 7.34 Heat map with a category
Other tips when using the Explorer
Include and exclude
Moving visualizations to the Designer
References
part three
loading data
In-memory is different
It’s about speed
Understanding the non-distributed deployment
Figure 8.1 SAS Visual Analytics non-distributed deployment
Understanding the distributed deployment
Figure 8.2 SAS Visual Analytics distributed deployment
Loading data to LASR from HDFS
Figure 8.3 LASR deployed symmetrically alongside HDFS
Enabling support for SASHDAT files
The exception to the rule
Figure 8.4 A remote (or asymmetric) MapR Hadoop cluster can also host SASHDAT files
SASHDAT does not require SAS/ACCESS
Loading data to LASR from Base SAS
Figure 8.5 Some of the default data sources available to Base SAS
Figure 8.6 Some of the additional data sources available when optional SAS software is installed
Figure 8.7 Using SAS PROCs or LIBNAME engines to load data into or out of LASR
Loading data to LASR with SAS In-Database technology
Figure 8.8 The SAS Embedded Process is often deployed to a separate cluster of machines apart from LASR
Figure 8.9 Each EP node will distribute its data evenly to each of the LASR Workers
Loading data to LASR from a different LASR Analytic Server
Figure 8.10 Use PROC IMXFER to copy data from one LASR Analytic Server to another
Loading data into LASR automatically
SAS Autoload to LASR facility
Figure 8.11 The SAS Autoload Facility works with SAS data sets, Excel documents, and CSV files
LASR Reload-on-Start feature
Figure 8.12 Reload-on-Start relies on SAS data sets as a backing store for data loaded from user-imported data, Google Analytics, Facebook, and Twitter
References
LASR administration
Administration overview
Administration tools
SAS Management Console
Figure 9.1 Logged on to SAS Management Console as the Unrestricted User with full control over all items
SAS Visual Analytics Administrator
Figure 9.2 Using VA Administrator to monitor system resource use
SAS Environment Manager
Figure 9.3 A dashboard shown in SAS Environment Manager for monitoring the metrics captured for our environment
SAS Program Code
Figure 9.4 Using the SAS Studio web app to submit SAS program code to work with LASR
Other tools
Interesting LASR Administration Tasks
The role of SAS metadata
Defining new LASR Analytic Servers
Figure 9.5 Using the SAS Management Console to create a new LASR Analytic Server
Figure 9.6 The New Server Wizard for creating a new metadata definition of a SAS LASR Analytic Server
Figure 9.7 Specifying memory limits of the LASR Analytic Server
Figure 9.8 Creating a new LASR Analytic Server for SAS Visual Analytics using the SAS Environment Manager administration tool
Defining new LASR libraries
Managing LASR Analytic Servers with code
Distributed Mode LASR
Non-Distributed Mode LASR
Working with the Autoloader Facility
Figure 9.9 The SAS Visual Analytics Autoloader Facility will ensure that the provided data is available in the LASR Server
Monitoring resources used by LASR
Figure 9.10 Monitoring the memory that is used in LASR Servers
LASR Server status
Figure 9.11 The execution state of each LASR Server
LASR memory usage
Figure 9.12 SAS Visual Analytics Administrator reports on LASR memory usage
Figure 9.13 RAM utilization gauge for the LASR cluster with details in the tooltip.
Resource Monitoring
Figure 9.14 The Resource Monitor in SAS Visual Analytics Administrator tracking CPU, RAM, and I/O across all nodes of the LASR cluster
Usage Reports
Figure 9.15 SAS Visual Analytics Administrator provides usage reports
Figure 9.16 The LASR Server tab in the Administrator Overview usage report
References
performance considerations
LASR performance
Figure 10.1 A distributed LASR Analytic Server acts as a single service while running in parts across multiple host machines
Figure 10.2 A non-distributed LASR Analytic Server runs on a single machine as part of a SAS deployment
Non-Distributed LASR (SMP)
Distributed LASR (MPP)
Load balancing by data distribution
Figure 10.3 LASR distributes incoming data equally across the LASR Workers
High-volume access to smaller tables
Figure 10.4 Smaller tables copied to non-distributed LASR Analytic Server for more efficient processing
Figure 10.5 Enabling full copies of smaller tables in a distributed LASR Analytic Server
Fast loading of data to distributed LASR Analytic Server
Figure 10.6 SAS supports a wide variety of data sources for serially loading data into LASR
LASR and a remote data provider (asymmetric)
LASR symmetrically co-located with HDFS
SASHDAT Tables
LASR co-located with dedicated HDFS and loading data from remote HDFS
Figure 10.7 Dedicated HDFS for storing SASHDAT
References
part four
introducing the SAS Viya platform
Overview of the SAS Viya platform
Figure 11.1 SAS Viya Platform
Understanding the CAS In-Memory Analytics Server
Introducing massively parallel analytics
Adding persistence
Providing more flexibility
Figure 11.2 CAS accessing SASHDAT data using DNFS
SAS Viya and SAS 9.4 together
Managing the SAS Viya environment
Opening the application
Figure 11.3 SAS Environment Manager
Managing users and groups
Managing data
Viewing data tables
Figure 11.4 Viewing tables
Viewing libraries
Managing content
References
wrangling your data
Introducing a modern user interface
Figure 12.1 SAS Visual Data Builder Welcome Mat
Importing data
Figure 12.2 Open Data Source Window
Viewing the data
Figure 12.3 Open Data Source Window
Profiling your data set
Figure 12.4 Table Profile
Figure 12.5 Column Profile
Creating a new data item
Figure 12.6 Add Calculated Column Window
Using in-memory joins
Figure 12.7 Join Tables Window
Figure 12.8 Preview a Join
Plans and tables
Figure 12.9 View Plan instructions
Figure 12.10 Saving a plan
New features
Transformations
Figure 12.11 Data Manipulation Functions
Figure 12.12 Quick Split Example
Figure 12.13 Split Column Window
Transposing tables
Figure 12.14 Transpose Diagram
Figure 12.15 Transpose example
Figure 12.16 Transpose data items
Figure 12.17 Transpose Table Window
Figure 12.18 Transpose Table Window
Figure 12.19 Final transposed data set
Filtering data
Figure 12.20 Filter Example
References
visualizing and exploring your data
Introducing the new layout
Figure 13.1 SAS Visual Analytics layout
Top toolbar
Figure 13.2 Report and page prompts
Figure 13.3 Undo button
Starting a new report
Figure 13.4 SAS Visual Analytics welcome mat
Importing data
Figure 13.5 Open Data Source window
Exploring data
Figure 13.6 Data panel
Adding objects
Figure 13.7 Objects panel
Figure 13.8 Adding an object
Figure 13.9 Adding roles to an object
Figure 13.10 List table with data
Figure 13.11 Adding multiple objects to the canvas
All-in-one application
Auto-chart and changing objects
Figure 13.12 Dragging data items to a blank canvas
Figure 13.13 Using the auto chart
Figure 13.14 Changing the auto chart object
Getting more measure details
Figure 13.15 Measure details for a table
Objects and Data Analysis Features
Figure 13.16 Forecasting feature in a line chart
Launch into analytics with visual statistics objects
Figure 13.17 Launch option
Figure 13.18 Cluster analysis
Additional features
Hiding pages
Figure 13.19 Hiding a page
Adding the donut chart
Figure 13.20 New pie chart with donut style
Add padding to objects
Figure 13.21 Padding feature on the options tab
Keeping fonts consistent
Figure 13.22 Fonts available in SAS Visual Analytics
References
index
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