SAS Visual Analytics

10月 082018

When was the last time you or your colleagues wanted access to data and tools to produce reports and dashboards for a business need? Probably within the last hour. Self-service BI applications – gaining popularity as we speak – make gaining insights and decision making faster. But they've also generated a greater need for governance.

Part of governance is understanding the data lifecycle or data lineage. For example, a co-worker performed some modifications to a dataset and used it to produce a report that you would like to use to help solve a business need.  How can you be sure that the information in this report is accurate? How did the producer of the report calculate certain measures? From what original data set was the report based?

SAS provides many tools to help govern platforms and solutions.  Let’s look at one of those tools to understand the data lifecycle: SAS Lineage Viewer.

Here we have a report created to explore and visualize telecommunications data using SAS Visual Analytics.  The report shows our variable of interest, cross-sell and up-sell flag, and its relationship to other variables. This report will be used to target customers for cross-sell or up-sell.

This report is based on an Analytical Base Table (ABT) that was created by joining two data sets:

  1. Usage information from a subset of customers who have contacted customer care centers.
  2. Cleansed demographics data.

The name of the joined dataset the report is based on is LG_FINAL_ABT.

To make sure we understand the data behind this report we’ll explore it using a lineage viewer (you will need to be assigned to the "Data Management Business User” or “Data Management: Lineage” group, which an administrator can help you with).  From the applications menu, select Explore Lineage.


We’ll click on Search for Subjects and search for the report we were just reviewing: Telecommunications.

I’ll enter Telecommunications in the search field then select the Telecommunications report.

The first thing I see is the LG_Final_ABT CAS table my report is dependent on.

If I click on the + sign on the top right corner of the data, LG_Final_ABT, I can see all the other relationships to that CAS table.  There is a Model Studio project, two Visual Analytics Reports (including the report we looked at), and a data view that are all dependent on the LG_FINAL_ABT CAS Table.  This diagram also shows us that the LG_FINAL_ABT CAS table is dependent on the Public CAS library.  We also see that the LG_FINAL_ABT CAS table was loaded into CAS from the LG_FINAL_ABT.sashdat file.

Let’s explore the LG_FINAL_ABT.sashdat file to see its lineage. Clicking on the + expands the view. In the following diagram, I expanded all the remaining items to see the full data lifecycle.

This image shows us the whole data life cycle.  From LG_FINAL_ABT.sashadat we see that it is dependent on the Create Final LG ABT data preparation plan.  That plan is dependent on two CAS tables; LG_CUSTOMER and LG_ORIG_ABT.  The data lineage viewer shows us that the LG_CUSTOMER table was loaded in from a csv file (lg_customer.csv) and the LG_ORIG_ABT CAS file was loaded in from a sas data set (lg_orig_abt.sas7dbat).

To dive deeper into the mashups and data manipulations that took place to produce LG_FINAL_ABT.sashdat, we can open the data preparation plan.  To do this I’ll right click on Create Final LG ABT and select Actions then Prepare Data.

Here is the data preparation plan.  At the top you can see that the creator of this data set performed five steps – Gender Analysis, Standardize, Remove, Rename and Join.

To get details into each of these steps, click on the titles at the top.  Clicking on Gender Analysis, I see that a gender analysis was performed based on the customer_name field and the results were added to the data set in a variable named customer_name_GND.

Clicking on the Standardize title, I see that there were two standardization tasks performed on the original data set. One for customer state and the other for customer phone number.  I can also see that the results were placed in new fields (customer_state_STND and customer_primary_phone_STND).

Clicking on the Remove title, I see that three variables were dropped from the final dataset.  These variables were the original ones that the user had “fixed” in the previous steps: customer_gender, customer_state, and customer_primary_phone.

Clicking on the Rename title, I see that the new variables have been renamed.

The last step in the process is a join. Clicking on the Join title I see that LG_CUSTOMER was joined with LG_ORIG_ABT based on an inner join on Customer_ID.

We have just walked through the data lineage or data lifecycle for the dataset LG_FINAL_ABT, using SAS tools. I now understand how the data in the report we were looking at was generated. I am confident that the information that I gain from the report will be accurate.

Since sharing information and data among co-workers has become so common, it's now more crucial than ever to think about the data lifecycle. When you gain access to a report that you did not create it is always a good idea to check the underlying data to ensure that you understand the data and that any business insights gained are accurate. Also, if you are sharing data with others and you want to make modifications to it, you should always check the lineage to ensure that you won’t be undermining someone else’s work with changes you make. Thanks to SAS Visual Analytics, all the necessary tools one needs to review data lineage are all available within one interface.

Keep track of where data originated with data lineage in SAS was published on SAS Users.

10月 052018

In my earlier blog, I described how to create maps in SAS Visual Analytics 8.2 if you have an ESRI shapefile with  granular geographies, such as counties, that you wish to combine into regions. Since posting this blog in January 2018, I received a lot of questions from users on a range of mapping topics, so I thought a more general post on using – and troubleshooting - custom polygons in SAS Visual Analytics on Viya was in order. Since version 8.3 is now generally available, this post is tailored to the 8.3 version of SAS Visual Analytics, but the custom polygon functionality hasn’t really changed between the 8.2 and 8.3 releases.

What are custom polygons?

Custom polygons are geographic boundaries that enable you to visualize data as shaded areas on the map. They are also sometimes referred to as a choropleth maps. For example, you work for a non-profit organization which is trying to decide where to put a new senior center. So you create a map that shows the population of people over 65 years of age by US census tract. The darker polygons suggest a larger number of seniors, and thus a potentially better location to build a senior center:

SAS Visual Analytics 8.3 includes a few predefined polygonal shapes, including countries and states/provinces. But if you need something more granular, you can upload your own polygonal shapes.

How do I create my own polygonal shapes?

To create a polygonal map, you need two components:

  1. A dataset with a measure variable and a region ID variable. For example, you may have population as a measure, and census tract ID as a region ID. A simple frequency can be used as a measure, too.
  2. A “polygon provider” dataset, which contains the same region ID as above, plus geographic coordinates of each vertex in each polygon, a segment ID and a sequence number.

So where do I get this mysterious polygon provider? Typically, you will need to search for a shapefile that contains the polygons you need, and do a little bit of data preparation. Shapefile is a geographic data format supported by ESRI. When you download a shapefile and look at it on the file system, you will see that it contains several files. For example, my 2010 Census Tract shapefile includes all these components:

Sometimes you may see other components present as well. Make sure to keep all components together.

To prepare this data for SAS Visual Analytics, you have two options.

Preparing shapefile for SAS Visual Analytics: The long way

One method to prepare the polygon provider is to run PROC MAPIMPORT to convert the shapefile into a SAS dataset, add a sequence ID field and then load into the Cloud Analytic Services (CAS) server in SAS Viya. The sequence ID is mandatory, as it helps SAS Visual Analytics to draw the lines connecting vertices in the correct order.

A colleague recently reached out for help with a map of Census block groups for Chatham County in North Carolina. Let’s look at his example:

The shapefile was downloaded from here. We then ran the following code on my desktop:

libname geo 'C:\...\Data;
proc mapimport datafile="C:\...\Data\Chatham_County__2010_Census_Block_Groups.shp"
data geo.chatham_cbg;
set  chatham_cbg;

We then manually loaded the geo.chatham_cbg dataset in CAS using self-service import in SAS Visual Analytics. If you are not sure how to upload a dataset to CAS, please check the %SHIMPR. The macro will automatically run PROC MAPIMPORT, create a sequence ID variable and load the table into CAS. Here’s an example:

%shpimprt(shapefilepath=/path/Chatham_County__2010_Census_Block_Groups.shp, id=GEOID, outtable=Chatham_CBG,,   casport=5570, caslib='Public');

For this macro to work, the shapefile must be copied to a location that your SAS Viya server can access, and the code needs to be executed in an environment that has SAS Viya installed. So, it wouldn’t work if I tried to run it on my desktop, which only has SAS 9.4 installed. But it works beautifully if I run it in SAS Studio on my SAS Viya machine.

Configuring the polygon provider

The next step is to configure the polygon provider inside your report. I provided a detailed description of this in my earlier blog, so here I’ll just summarize the steps:

  • Add your data to the SAS Visual Analytics report, locate the region ID variable, right-click and select New Geography
  • Give it a name and select Custom Polygonal Shapes as geography type
  • Click on the Custom Polygon Provider box and select Define New Polygon Provider
  • Configure your polygon provider by selecting the library, table and ID column. The values in your ID column must match the values of the region ID variable in the dataset you are visualizing. The ID column, however, does not need to have the same name as in the visualization dataset.
  • If necessary, configure advanced options of the polygon provider (more on that in the troubleshooting section of this blog).

If all goes well, you should see a preview of your polygons and a percentage of regions mapped. Click OK to save your geographic item, and feel free to use it in the Geo Map object.

I followed your instructions, but the map is not working. What am I missing?

I observed a few common troubleshooting issues with custom maps, and all are fairly easy to fix. The table below summarizes symptoms and solutions.

Symptom Solution
In the Geographic Item preview, 0% of the regions are mapped. For example:
Check that the values in the region ID variable match between the main dataset and the polygon provider dataset.
I successfully created the map, but the colors of the polygons all look the same. I know I have a range of values, but the map doesn’t convey the differences. In your main dataset, you probably have missing region ID values or region IDs that don’t exist in the polygon provider dataset. Add a filter to your Geo Map object to exclude region IDs that can’t be mapped.

Only a subset of regions is rendered. You may have too many points (vertices) in your polygon provider dataset. SAS Visual Analytics can render up to 250,000 points. If you have a large number of polygons represented in a detailed way, you can easily exceed this limit. You have two options, which you can mix and match:

(1)    Filter the map to show fewer polygons

(2)    Reduce the level of detail in the polygon provider dataset using PROC GREDUCE. See example here. Also, if you imported data using the %shpimprt macro, it has an option to reduce the dataset. Here’s a handy link to In the Geographic Item preview, the note shows that 100% of the regions are mapped, but the regions don’t render, or the regions are rendered in the wrong location (e.g., in the middle of the ocean) and/or at an incorrect scale.

This is probably the trickiest issue, and the most likely culprit is an incorrectly specified coordinate space code (EPSG code). The EPSG code corresponds to the type of projection applied to the latitude and longitude in the polygon provider dataset (and the originating shapefile). Projection is a method of displaying points from a sphere (the Earth) on a two-dimensional plane (flat surface). See this tutorial if you want to know more about projections.

There are several projection types and numerous flavors of each type. The default EPSG code used in SAS Visual Analytics is EPSG:4326, which corresponds to the unprojected coordinate system.  If you open advanced properties of your polygon provider, you can see the current EPSG code:

Finding the correct EPSG code can be tricky, as not all shapefiles have consistent and reliable metadata built in. Here are a couple of things you can try:

(1)    Open your shapefile as a layer in a mapping application such as ArcMap (licensed by ESRI) or QGIS (open source) and view the properties of the layer. In many cases the EPSG code will appear in the properties.

(2)    Go to the location of your shapefile and open the .prj file in Notepad. It will show the projection information for your shapefile, although it may look a bit cryptic. Take note of the unit of measure (e.g., feet), datum (e.g., NAD 83) and projection type (e.g., Lambert Conformal Conic). Then, go to and search for your geography.  Going back to the example for Chatham county, I searched for North Carolina. If more than one code is listed, select a few codes that seem to match your .prj information the best, then go back to SAS Visual Analytics and change the polygon provider Coordinate Space property. You may have to try a few codes before you find the one that works best.

I ruled out a projection issue, the note in Geographic Item preview shows that 100% of the regions are mapped, but the regions still don’t render. Take a look at your polygon provider preparation code and double-check that the order of observations didn’t accidentally get changed. The order of records may change, for example, if you use a PROC SQL join when you prepare the dataset. If you accidentally changed the order of the records prior to assigning the sequence ID, it can result in an illogical order of points which SAS Visual Analytics will have trouble rendering. Remember, sequence ID is needed so that SAS Visual Analytics can render the outlines of each polygon correctly.

You can validate the order of records by mapping the polygon provider using PROC GMAP, for example:

proc gmap map=geo.chatham_cbg data=geo.chatham_cbg;
   id geoid;
   choro geoid / nolegend levels=1;

For example, in image #1 below, the records are ordered correctly. In image #2, the order or records is clearly wrong, hence the lines going crisscross.


As you can see, custom regional maps in SAS Visual Analytics 8.3 are pretty straightforward to implement. The few "gotchas" I described will help you troubleshoot some of the common issues you may encounter.

P.S. I would like to thank Falko Schulz for his help in reviewing this blog.

Troubleshooting custom polygon maps in SAS Visual Analytics 8.3 was published on SAS Users.

10月 042018

You can now conduct a live demo of SAS Visual Analytics on your mobile device to participants who are geographically dispersed by using the Present Screen feature, a tucked-away option in the SAS Visual Analytics app for iPad, iPhone, and Android devices. Let’s say I am looking at a report on my mobile device, and I have questions about a couple of items for my colleagues Joe and Anita, both of whom are located in two different cities. The three of us are able to see the report while I demo it, drawing their attention to specific areas of interest in the report.

Sitting in my office, or from any location where I have a Wi-Fi or cellular connection, I can use the Present Screen feature to do a live shared presentation with Joe and Anita. And I don't have to present just one report. During the live presentation, I can close a report, open a different one, perform interactions, and move around in the app between different reports.

And here’s the real beauty of this feature. Neither Joe nor Anita need to have the SAS Visual Analytics app on a mobile device, or SAS Visual Analytics running on a desktop. The only requirement for participants is that they have a mobile device (could be an iOS, Android, or Windows device), a laptop, or a desktop system.  Internet access via Wi-Fi (or a cellular connection for mobile devices), an email client for receiving an email notification, and a Web browser are necessary. VPN connectivity might be required if the participants' organization requires VPN.

Plus, you can conduct your live presentation for up to 10 people! Before we move on, note that you can also use the iOS feature, AirDrop, on your iOS device to engage participants with the Present Screen feature. This is useful if you’re in a room with a bunch of folks who have iOS devices, and you want to do live sharing.

Ready to try it? Here’s a short checklist of what you need to do a live presentation of SAS Visual Analytics reports.

Requirements for the presenter

  • Use any one of these devices to present your screen for a shared presentation to your participants: iPad, iPhone, Android tablet or smartphone
  • SAS Visual Analytics app installed on your device
  • Connection via Wi-Fi or cellular to the server where the report(s) reside
  • Subscription to the reports that you want to present and share
  • Email client on the iPad or phone
  • VPN connectivity if your organization requires it

Couple of things to note

Say you're presenting to 10 participants via email or AirDrop.  Note that as the presenter, you must have the SAS Visual Analytics app open in your device with the Present Screen feature selected and active for your guests to be able to see your reports. Once you exit the app, the screen presentation session ends and the email or AirDrop invitations are no longer valid.

And a note on participant information. After a participant forwards an email invitation to view the presentation to a colleague, the recipient is required to enter a name and email address before joining your live screen presentation. You, as the presenter, get to see the names of the folks that have joined your screen presentation. Participant names and email addresses simply let the presenter know who all have joined to presentation. Neither the participants' names nor their email addresses are validated by the SAS Visual Analytics server.

Supported versions for SAS Visual Analytics reports

You can present and share reports with the Present Screen feature for these versions of SAS Visual Analytics:

7.3, 7.4, 8.1, 8.2, 8.3

How to start the screen presentation

In the Analytics report I'm subscribed to from the SAS Visual Analytics app on my iPad, I choose Present Screen.

Choosing Present Screen in the subscribed report

I am reminded that I can present my screen to a maximum of 10 participants. I click OK.

The app prompts me to send email or choose AirDrop to present my screen to participants - I choose email.

My email client opens, and the app includes instructions along with the link that takes them to my live screen presentation. I enter the email addresses for my participants and send the email.

In the email that Anita receives on her desktop PC, she taps on the link which takes her to her Web browser where my screen presentation is set to start soon.

Anita enters her name and email address, and notes that the presentation has not yet started.

Joe, who has logged on to the presentation from his Android phone, is also presented with the same message on his smartphone.

To begin the screen presentation, I tap on the blinking cursor in the app.

The app reminds me that now my participants can see everything on my iPad screen.

Next, a blue bar at the top of the report indicates that my screen presentation is live and can be seen by Anita and Joe. Now I can begin my report presentation, or exit this report and open a different report to share.

Here is my screen on the iPad Mini where I started my screen presentation:

Anita's screen on her Windows desktop monitor:

Joe's screen on the Android smartphone:

When I am finished with the presentation, I tap on the 'stop' button to end it.

A message displays to indicate that the presentation has ended. Here's an example of that message from Joe's Android smartphone.

Do it live! How to present your screen from the SAS Visual Analytics app was published on SAS Users.

9月 252018

If you use SAS Visual Analytics and don’t have the SAS Visual Analytics app, you're missing out on a ton of convenience and interaction you could be having while on-the-go. And even if you don’t have access to SAS Visual Analytics today, you can still download and try the mobile app with some cool sample reports.

Ready to take a quick dive and look at the app?

How to get the app

Download and install the free app to your Apple, Android or Windows device from the app store:

Apple iTunes Store
Google Play
Microsoft Store

When you open the app, you are greeted with an introductory launch screen:

In the introductory launch screen that displays when you first open the iOS or Android app, go to the third screen and tap on Learn how to use the Tray.

You are taken to the SAS Help Center. Watch the short slide show at the help center to understand the special Tray feature in the iOS or Android app or to find out what’s new in the app.

Using the Windows-based app? Here’s what you see:

Sample reports on the SAS Demo Server

In the app, sample charts and reports are instantly made available to you in the Subscriptions view via a connection to the SAS Demo Server. This server hosts a nice variety of reports that you can view on your phone or tablet. Interact with a wide spectrum of sample SAS Visual Analytics reports for different industries.

Subscriptions View in the App With Sample Reports

Tap on Add to view the different folders that contain additional sample reports for you to browse, subscribe, and view.

Additional Sample Reports on the SAS Demo Server

When you select and subscribe to the additional reports that are available on the SAS Demo Server, these reports are downloaded to the Subscriptions view in the mobile app. Just tap on the tile for any report in the Subscriptions view to open it and view the charts, graphs, and their associated data.

Here are a couple of reports as viewed in the Windows 10 app:

Already have SAS Visual Analytics in your organization?

If you view SAS Visual Analytics reports on your laptop or a desktop computer, this app extends your ability to view those same reports on your phone or tablet. If your organization has deployed SAS Visual Analytics, but is not taking advantage of extending report viewing ability to mobile devices, I urge you to consider it.

The app supports SAS Visual Analytics 8.3, 8.2, 7.4, and 7.3. Almost every type of interaction that you have with a SAS Visual Analytics report on your desktop can be done with reports viewed in the app on your phone or tablet!

If you have SAS Visual Analytics deployed in your organization, reach out to the SAS Visual Analytics administrator in your organization and ask them to enable support for mobile devices so that you can start viewing your reports in the app.

To give you a little more guidance, here are some FAQs about the app.

If we have reports in our organization that were created with SAS Visual Analytics, can we view those reports in this app?

Yes. The same reports that you view in your web browser on a desktop can be viewed in the mobile app.

How do I view our organization’s reports in the app?

Access from your mobile device to SAS Visual Analytics reports on your company’s server is granted by your SAS Administrator. Live data access requires either a Wi-Fi or cellular connection, and your company may require VPN access or other company-specific security measures.

Contact your SAS Visual Analytics Administrator to request access from your mobile device to the server hosting your SAS Visual Analytics reports. Your administrator ensures that your mobile device is registered as a valid device in the SAS Environment Manager where mobile device access to your organization’s server is managed.

How do I add a server connection?

When your mobile device is registered for access to the SAS Visual Analytics server, simply create a server connection within the app to your company server and browse for reports.

Here’s a nice slide show with the steps you follow to create a server connection to the SAS Visual Analytics server by entering the complete server name, port number, your username, and password:

Quick primer on the SAS Visual Analytics app was published on SAS Users.

9月 052018

Typically, when filters are applied in SAS Visual Analytics it affects all the records and aggregations in linked objects. For example, in a typical sales report below, when filters are applied, it changes all the measures of linked objects.
















With this kind of filtering, it becomes difficult to calculate measures which requires a different level of aggregation. In above image the expectation is that the ‘Total Customers’ should not be changing irrespective of ‘Region’, ‘State’, ‘Category’ and ‘Subcategory’ control selections. ‘Total Customers (Geo)’ should be changing only based on ‘Region’ and ‘State’ control selections. ‘Total Customers (Geo and Prod)’ should be changing based on all the controls mentioned above. In the above example only, a ‘Total Customers (Geo and Prod)’ calculation is correct.

We will learn to create measures with different levels of aggregation by using ‘Customer Penetration’ measure as an example.

          Customer Penetration = Distinct customers at selected geography and product level/ Distinct customers at selected geography level

Selective filtering may be used for creating similar reports like: Dealer Participation, Sales Contribution, etc. The below section exemplifies the creation of a customer penetration report with selective filtering.

Customer penetration using SAS Visual Analytics 8.2 (selective filtering)

Customer penetration is used to analyze whether marketing and sales strategies are working or not. Managers often uses customer penetration or dealer participation measures along with other measures to measure the popularity of a product, category or brand.

This report requirement is such that the numerator in the ‘Customer Penetration’ formula should be filtered based on region and state list control selections, while the denominator should be filtered based on region, state, category and subcategory list control selections. This is not the same requirement as filtering the whole table through common list controls. In general, if you link a table with any control, all the measures in that table will be filtered as per selected value(s) in controls. However, our requirement is not like that. Instead of linking control and tables we will use control parameters to achieve our objective.

Assume we have a customer transaction table with following variables:







Before we move, be ready with the basic report as per below image:


Once you are ready with the report as per the above image, create parameters for ‘Region’, ‘State’, ‘Category’, ‘SubCategory’:

Region Parameter


State Parameter


Category Parameter


SubCategory Parameter


Now create the following two calculated items derived from ‘Customer_ID’:

Equivalent to ‘Customer_ID’. However, populated only for selected geography levels and rest would be filled with missing.







Equivalent to ‘Customer_ID’. However, populated only for selected geography and product levels and rest would be filled with missing.


Create the following two aggregated measures:

Total Customers (Geo)
You need to subtract the distinct count related to missing ‘Geo_Customer_ID’, which is 1.


Total Customers (Geo and Prod)
You need to subtract the distinct count related to missing ‘Geo_and_Prod_Customer_ID’, which is 1.


Now you can create an aggregated measure ‘Customer Penetration’.

Customer Penetration = Total Customers (Geo and Prod) / Total Customers (Geo)


Final report will look like this:














Comparative images with default and selective filtering implementation:


If you compare the above images, you will find the difference in highlighted measures where the first image aggregation level is based on selective filtering, while in second image aggregation level is uniform.

Note – ‘Total Customers’ is count of distinct ‘Customer_ID’ i.e., total customers count is independent from geography and product hierarchy selection.


This process allows you to use control parameters in ‘If Then Else…’ statements to create a variable (calculated item) having character values. You can utilize this feature in several other applications – this is just one way you can use parameters to fulfil a business requirement.

Selective filtering in SAS Visual Analytics 8.2 was published on SAS Users.

8月 172018

Data density estimation is often used in statistical analysis as well as in data mining and machine learning. Visualization of data density estimation will show the data’s characteristics like distribution, skewness and modality, etc. The most widely-used visualizations people used for data density are boxplot, histogram, kernel density estimates, and some other plots. SAS has several procedures that can create such plots. Here, I'll visualize the kernel density estimates superimposing on histogram using SAS Visual Analytics.

A histogram shows the data distribution through some continuous interval bins, and it is a very useful visualization to present the data distribution. With a histogram, we can get a rough view of the density of the values distribution. However, the bin width (or number of bins) has significant impact to the shape of a histogram and thus gives different impressions to viewers. For example, we have same data for the two below histograms, the left one with 6 bins and the right one with 4 bins. Different bin width shows different distribution for same data. In addition, histogram is not smooth enough to visually compare with the mathematical density models. Thus, many people use kernel density estimates which looks more smoothly varying in the distribution.

Kernel density estimates (KDE) is a widely-used non-parametric approach of estimating the probability density of a random variable. Non-parametric means the estimation adjusts to the observations in the data, and it is more flexible than parametric estimation. To plot KDE, we need to choose the kernel function and its bandwidth. Kernel function is used to compute kernel density estimates. Bandwidth controls the smoothness of KDE plot, which is essentially the width of the sliding window used to generate the density. SAS offers several ways to generate the kernel density estimates. Here I use the Proc UNIVARIATE to create KDE output as an example (for simplicity, I set c = SJPI to have SAS select the bandwidth by using the Sheather-Jones plug-in method), then make the corresponding visualization in SAS Visual Analytics.

Visualize the kernel density estimates using SAS code

It is straightforward to run kernel density estimates using SAS Proc UNIVARIATE. Take the variable MSRP in SASHELP.CARS dataset as an example. The min/max value of MSRP column is 10280 and 192465 respectively. I plot the histogram with 15 bins here in the example. Below is the sample codes segment I used to construct kernel density estimates of the MSRP column:

title 'Kernel density estimates of MSRP';
proc univariate data = noprint;	
   histogram MSRP / kernel (c = SJPI) endpoints = 10280 to 192465 by 12145 outkernel = KDE  odstitle = title; 

Run above code in SAS Studio, and we get following graph.

Visualize the kernel density estimates using SAS Visual Analytics

  1. In SAS Visual Analytics, load the SASHELP.CARS and the KDE dataset (from previous Proc UNIVARIATE) to the CAS server.
  2. Drag and drop a ‘Precision Container’ in the canvas, and put a histogram and a numeric series plot in the container.
  3. Assign corresponding data to the histogram plot: assign CARS.MSRP as histogram Measure, and ‘Frequency Percent’ as histogram Frequency; Set the options of the histogram with following settings:
    Object -> Title: No title;

    Graph Frame: Grid lines: disabled

    Histogram -> Bin range: Measure values; check the ‘Set a fixed bin count’ and set ‘Bin count’ to 15.

    X Axis options:

       Fixed minimum: 10280

       Fixed maximum: 192465

       Axis label: disabled

       Axis Line: enabled

       Tick value: enabled

    Y Axis options:

       Fixed minimum: 0

       Fixed maximum: 0.5

       Axis label: disabled

       Axis Line: disabled

       Tick value: disabled

  1. Assign corresponding KDE data to the numeric series plot. Define a calculated item: Percent as (‘Percent of Observations Per Data Unit’n / 100) with the format of ‘PERCENT12.2’, and assign it to the ‘Y axis’; assign the ‘Data Value’ to the ‘X axis.’ Now set the options of the numeric series plot with following settings:
    Object -> Title: No title;

    Style -> Line/Marker: (change the first color to purple)

    Graph Frame -> Grid lines: disabled

    Series -> Line thickness: 2

    X Axis options:

       Axis label: disabled

       Axis Line: disabled

       Tick value: disabled

    Y Axis options:

       Fixed minimum: 0

       Fixed maximum: 0.5

       Axis label: enabled

       Axis Line: enabled

       Tick value: enabled


       Visibility: Off

  1. Now we can start to overlay the two charts. As can be seen in the screenshot below, SAS Visual Analytics 8.3 provides a smart guide with precision container, which shows grids to help you align the objects in it. If you hold the ctrl button while dragging the numeric series plot to overlay the histogram, some fine grids displayed by the smart guide to help you with basic alignment. It is a little tricky though, to make the overlay precisely, you may fine tune the value of the Left/Top/Width/Height in the Layout of VA Options panel. The goal is to make the intersection of the axes coincides with each other.

After that, we can add a text object above the charts we just made, and done with the kernel density estimates superimposing on a histogram shown in below screenshot, similarly as we got from SAS Proc UNIVARIATE. (If you'd like to use PROC KDE UNIVAR statement for data density estimates, you can visualize it in SAS Visual Analytics in a similar way.)

To go further, I make a KDE with a scatter plot where we can also get impression of the data density with those little circles; another KDE plot with a needle plot where the data density is also represented by the barcode-like lines. Both are created in similar ways as described in above histogram example.

So far, I’ve shown you how I visualize KDE using SAS Visual Analytics. There are other approaches to visualize the kernel density estimates in SAS Visual Analytics, for example, you may create a custom graph in Graph Builder and import it into SAS Visual Analytics to do the visualization. Anyway, KDE is a good visualization in helping you understand more about your data. Why not give a try?

Visualizing kernel density estimates in SAS Visual Analytics was published on SAS Users.

8月 152018

SAS Viya logoSAS Viya 3.4 has some new functionality that provides real help for those who want to transition from SAS Visual Analytics on 9.4 to SAS Viya. In prior releases of SAS Viya you could promote reports and explorations (and a few other supporting objects). In SAS Viya 3.4, promotion support is added for many additional SAS 9.4 resources, making it easier to make the leap to SAS Viya. In this blog, I will review this new functionality.

In SAS Viya 3.4, the following objects participate in promotion from SAS 9.4.

  • Configuration
    • Identities
    • Authorization
    • Data definitions
  • Content
    • Folders
    • Reports
    • Explorations
    • Stored processes
    • Supporting resources (such as themes, images, graphs templates)

The details of support for each resource are unique and are discussed below.


User and group promotion from SAS 9.4 to SAS Viya is used to support the transition to the target environment of authorization settings that are associated with content.  Metadata is exported to support the mapping of SAS 9.4 identity metadata (Users and Groups) to SAS Viya identities (Users, Groups and Custom groups).

During promotion of identity metadata:

  • Users connections are mapped using metadata DefaultAuth:logonid to SAS Viya identity id
  • Metadata-only groups from SAS 9.4 are converted to SAS Viya Custom groups (except SAS General Servers and SAS System Services)
  • If custom groups of the same name (or sometimes the same purpose but a different name) exist in the target, the group is preserved and any mapped members from the source system are added to the group.


Identities are “promoted” to support re-implementation of authorization. You do not have to explicitly export authorization as it is included with libraries, tables, folders and reports when they are exported. Promotion of authorization is optional. If you don’t wish to include authorization, but rather re-implement it in
SAS Viya, you can switch this functionality off at import time.

SAS Viya has two authorization systems, the general authorization system for folders and content, and the CAS authorization system for data. These authorization systems are different than the metadata authorization model in SAS 9.4. So what happens when you promote content that includes authorization?

General Authorization (folders and content)

Promotion will attempt to convert SAS 9.4 authorization to rules in the General authorization system.  During the process:

  • Explicit Access Control Entries are converted to SAS Viya Rules
  • Access Control Entries with denials are discarded
  • Access Control Templates are not promoted

In addition, if an object (folder/report):

  • does not exist in the target environment,relevant authorization is set for the object and the access control entries from the source are implemented as rules on the object.
  • existsin the target environment, then access control entries from the source are merged with any pre-existing authorizations in the target environment.

CAS Authorization

The CAS authorization system covers CASlibs and data.  Promotion will attempt to convert SAS 9.4 authorization on libraries and tables to access controls in the CAS authorization system. During the process:

  • Access Control Entries are not promoted unless they are applied directly to a library or table.
  • Access Control Entries are converted to CAS access controls.
  • Row-level permissions are preserved.
  • If an object exists in the target environment no authorization settings are imported.
  • Access Control Templates are not promoted.

For details of how individual permissions for both data and content are mapped from SAS 9.4 to SAS Viya see the documentation has great coverage of the steps to follow.

The Process

To finish off, I'll share few observations on the process of exporting from 9.4 and importing in SAS Viya. Like SAS 9.4 promotion, you need to import in a specific order. This allows the software to make the relevant connections to dependent resources. For example, if the CASLIB already exists in the target, then import tables can be mapped to it. Typically, the order is: identities > library definitions > tables > reports and folders. To support this process, make sure, during export, you have a separate package for each resource type. Some considerations for the export process.

You should export:

  • Identities (users and groups) from the security folders in SAS 9.4 metadata to a separate package.
  • Only groups that you need in the target environment (you can subset any irrelevant SAS 9 groups at export time).
  • LASR and Base Libraries and tables directly from the library definition in the folder tree (this prevents extraneous folders being created in the target environment).
  • Libraries in a separate package from tables so that they may be imported first and be available for mapping when the tables are imported.
  • Content and reports from the base of the folder tree so that all directly applied access control entries will be included in the package.

Prior to importing, make sure that users and groups are configured correctly in LDAP. As I already mentioned, physical data is not promoted so ensure that required data and formats are accessible to the SAS Viya environment.

The new functionality for promotion is a great start in helping with the transition from SAS 9.4 to SAS Viya. Look for more functionality in future releases.

New functionality for transitioning from SAS Visual Analytics on 9.4 to SAS Viya was published on SAS Users.

8月 022018

Recently, Scott Jackson, Director Business Intelligence at the University of North Carolina at Chapel Hill shared their data quality, reporting and analytics journey. They're using SAS in a multitude of ways – from operations, institutional research, athletics – and are now looking to scale to the enterprise. They've been so successful [...]

Scaling data and analytics across the University of North Carolina was published on SAS Voices by Georgia Mariani

7月 252018

In SAS Visual Analytics 8.3, a Data View is a reusable and shareable template for a data source. That means that the data view is tied to the data source, and not to the report. If you update a data view it will not automatically propagate those changes into a report.
So, what can a data view do for you? Plenty! Here are just a few of the settings and customizations that a data view can save for a data source: (taken from documentation here):

  • Data item settings such as names, formats, classifications, and aggregations
  • Data source filters
  • Hierarchies
  • Derived data items
  • Calculated items
  • Custom categories
  • Duplicate data items
  • Show / hide status for data items
  • Unique row identifier selection

Create a Data View

Now you must be wondering, how do you save all these wonderful customizations for your data source? Answer: by creating a Data View.
To get started, use the Data Source menu and select Save data view…. In this example, I created a hierarchy for the SASHELP CARS data set but as you can see from the list above you could have created many more calculations, custom categories, etc.

Then give the Data View a name. A few other things you may notice about this Save Data View dialogue are the options for: Default data view and Shared data view.

Default data view

A default data view is automatically applied whenever the data source is added to the report.
Each user can create their own data view of the source data and select their own default data view. This could lead to each user having a personalized default view. But, what if you want share your data views with others on your team? Or have everyone start with the same default view? That is when you need to first be an Application Administrator and second use the Shared data view option.

Shared data view

In order to be able to share a data view, you must be an Application Administrator. Then the option to share a data view will be available. Once a data view is shared for a data source, other users with access to that data source will be able to apply that data view.

Apply a Data View

Data views are templates of saved settings, hierarchies, custom categories, calculated data item, etc. which can be combined in an infinite amount of ways. Therefore, it follows that multiple data views can be applied to the same data source. In the example above, I created a new hierarchy for the SASHELP CARS data set. But I could also create a new data view which changes the aggregation of the MPG measures to reflect the average aggregation and not the default sum aggregation.

To apply a data view: open a new report, select your data source, then use the Data Source menu and select Data views…. You will see any individually created data views as well as any shared data views. Highlight the data view you wish to apply, then select Apply. Repeat for all of the data views you wish to apply.

If any data items are duplicated with the addition of data views then, as shown below, those data items are given a (n) after their names.

Administrator-controlled Default Data View

We've learned what Data Views are and that we can share them. How can we ensure that all the users who select a data source get the same starting point with a particular data view? To set this up, you must be an Application Administrator and the Data View must be Shared.
Once these two criteria are met, you can navigate to the report's overflow menu and select Edit administration settings. Then select the data source and which data view to apply as the default for all users.

Caution: If the user has already selected a personal default data view, then the personal default data view overrides the administrator-set default data view. Remember that an individual user can apply a personal or another shared data view and override the default data view.


Data Views are just one of the exciting new features in SAS Visual Analytics 8.3. A few key points to remember:

  • Data Views are tied to a data source, not a report. If a data view is edited, those edits do not propagate to the reports that applied that Data View.
  • A data source can have multiple Data Views applied.
  • Only an Application Administrator can share a data view with other users as well as define a default data view for a data source for all users. Any personal defined default data views override the administrator-set default data view.
  • Data Views are a template of data settings and edits – not a fully robust semantic layer where updates are pushed to all instances of usage. While Data Views can be used to assist in defining commonly used calculations and custom categories, remember that each user can still create their own data views and thus override the administrator-set default.

Using Data Views in SAS Visual Analytics was published on SAS Users.

6月 302018

Introduced with SAS Visual Analytics 8.2 is a new object named: Key Value. The intent of this object is call attention to an aggregated value for a measure, a category, or both. For additional specifics,

I’ve mocked up several reports to show some of the combinations available to give you an idea of what the Key Value object can look like. Toward the end of the blog, I will add additional reports to provide design ideas for placement and action assignments. Click on any image to enlarge.

Text Style with Measure Value Highlight

Here you can see in the report, I am highlighting two measure values. Both are representing the Highest value but I selected one to show the aggregation and the other not. I was able to mimic similar headings by renaming my data item. Be sure to look at the Options and Roles pane for the assignments to understand how I accomplished this.

Infographic Style with Measure Value Highlight

Here is the same information represented using the Infographic style. Seeing the same report using the different styles allows you to quickly determine the most powerful and appropriate visualization to meet your needs. We cannot control the size of the circle, only the color. In this case, the circles are different thicknesses because of the number of characters used to represent the measure values inside the visual.

Text Style displaying both Measure Value and Category

In this report, I have shown how to use the Text style to display both a Category value and a Measure value. As you can see, only one can be Highlighted, i.e. given the largest font. Notice that in this report I used the object’s Title to help explain the key value being displayed, this is a recommended best practice.

Infographic Style displaying both Measure Value and Category

Below I am using the Infographic style and notice in the Options pane below that I had to use the Additional information attribute to better label the data. Make sure that when you are in the design phase and toggling between text and infographic to review and test the available Key Value Style attributes to better label the visual.

Text Style with Category Value Highlight

In this report, I show how to highlight the Category value using the Text style. Since I chose to not use any of the available label attributes it is critical that I use the object’s Title to better explain the key value displayed.

Infographic Style with Category Value Highlight

In this report, you can see how I changed the layout from the previous report to make the Key Value object side-by-side the other report objects. If you are interested in using the Infographic style with the circle enabled then you may have to adjust your report design to accommodate for the space the circle needs to display. Remember not to shy away from adding white space to your report, it can assist when adding emphasis to a particular visual, in this case a key value.


Some important things to remember about using the Key Value object in your reports:

  • Use the Key Value object’s Title to inform your users what the number or category value represents so there is no ambiguity as to if they are looking at the maximum or minimum value.
  • When determining which style you prefer, Text or Infographic, it may be easier to make a duplicate of the Page and then adjust the style attributes till you find the desired combination.
  • Take time to adjust the arrangement of objects on your report to get the most pleasing configuration. Don’t shy away from leaving white space in your report. You can also experiment with the Container object using the Precision container type to layer the Key Value object.
  • The Key Value object will be affected by Report and Page Prompts like any other report object and you can even define Actions to filter the Key Value object.

Here are some additional examples of using the Key Value object:

In this example, you can see from the Actions Diagram how the Key Value object is being filtered. First, by the two page prompts and second, there is a direct filter action defined from the List Control object

In this example, we can see from the Actions Diagram that I used the Container object. I then selected the Precision container type and overlaid the Key Value object on the Line Chart. The only filters applied to these report objects are the page prompts.

And in this last example, you can see how I have no Report or Page prompts or any other filters impacting the Key Value objects. Therefore, these values are representative for the entire data.

Key Value Object in SAS Visual Analytics was published on SAS Users.