An Introduction to SAS Visual Analytics: How to Explore Numbers, Design Reports, and Gain Insight into Your Data

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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