Tech

10月 192017
 

The goal of this article is to describe the steps needed to configure the bridge that allows SAS Data Integration 4.902, based on SAS 9.4M4, to load data directly into CAS on SAS Viya 3.2.

Of course, SAS 9.4M5 simplifies this process, as a SAS/CONNECT communication will no longer be required, enabled by a direct function within SAS Data Integration to CAS - but for those of you who may not move immediately from SAS 9.4M4 to SAS 9.4M5, this could be helpful.

It is assumed here that SAS/CONNECT has been installed and configured on both environments, SAS 9.4M4 and SAS Viya 3.2.

Validate the connection from SAS 9.4M4 to SAS Viya 3.2

⇒     Check the status of the SAS/CONNECT Spawner on SAS Viya, on the machine where this service is installed.

SAS Viya 3.2 Bridge for SAS Data Integration Studio

⇒     Note the machine and the listening port of the SAS/CONNECT Spawner on SAS Viya.
⇒     Open SAS Studio on SAS 9.4M4 and sign-in.
⇒     Run the following SAS code with your machine details and a valid SAS Viya user account and check the results.

SAS Viya 3.2 Bridge for SAS Data Integration Studio

⇒     If successful, sign-off the SAS/CONNECT session and sign-out from SAS Studio SAS 9.4M4

Setup SAS9.4M4 metadata

⇒     Open SAS Management Console 9.4M4 as sasadm@saspw.
⇒     On the “Server Manager” plugin, add a “New Server…”

  • Server type: “SAS Application Server”
  • Name: “SASViya”
  • Select “Connect Server” as the sub-type of server you want to add to this SAS Application Server
  • Configure the “Connect Server” as shown below (you might have to create a new authentication domain for SAS Viya) and set the values accordingly (server where the SAS/CONNECT Spawner on SAS Viya is listening)

⇒     On the “Server Manager” plugin, add a “New Server…”

  • Server type: “SAS Cloud Analytic Services Server”
  • Name: “CAS Server”
  • Configure the “CAS Server” as shown below and set the values accordingly (CAS controller)

⇒     On the “User Manager” plugin, set a login for the SASViya application server, on a user or group that you will use in SAS Data Integration Studio

⇒     On the “Data Library Manager” plugin, add a “New Library…”

  • Library type: “SAS Cloud Analytic Services Library”
  • Name: CAS_DATA
  • Assign the library to the SASViya server

⇒     Configure the CAS library as shown below and set the values accordingly (the CASLIB must exist in the SAS Viya environment; here CASPATH is the name of an existing CASLIB).

⇒     Specify the server and connection information as shown below:

Build a SAS Data Integration Studio job to load data into CAS

⇒     Open SAS Data Integration Studio 4.902 as a user who holds, directly or not, a login for the ViyaAuth authentication domain.
⇒     Test the CAS_DATA library by “Register(ing) tables…”

  • In SAS Environment Manager on SAS Viya, some tables must have been loaded before into the CASLIB (the one that is pointed on by the CAS library, here CASPATH), so that you can display some tables in the “Register Tables…” wizard.
  • If you see some tables then it looks like you are all set.

⇒     If you want to go further and test the “Cloud Analytic Services Transfer” DI transformation, create the metadata for a target table in CAS with appropriate columns.

⇒     Build a job that loads a source table to this target table using the “Cloud Analytic Services Transfer” (“Access” folder) as shown below:

⇒     The “Cloud Analytic Services Transfer” is basically a “Data Transfer” transformation that fits with CAS; it enables you to provide some CAS options such as COPIES; by default the table is PROMOTED.

⇒     Run it and check if it ran successfully and if the table is available from the SAS Viya world.

SAS/CONNECT on SAS Viya configuration directory

⇒     The SAS/CONNECT on SAS Viya configuration is located by default here /opt/sas/viya/config/etc/connectserver/default

⇒     You might want to customize/clean some of the files here.

  • Check the CASHOST option in autoexec files, sometimes the value is not appropriate.

Normally, options here are sourced from the CONNECTSERVER_CONFIGURATION in vars.yml during the deployment of SAS Viya.

SAS Viya 3.2 Bridge for SAS Data Integration Studio 4.902 (on SAS 9.4M4) configuration steps was published on SAS Users.

10月 172017
 

moving content between SAS Viya environmentsIn a SAS Viya 3.2 environment two types of content can be created: SAS Visual Analytics Reports and Data Plans. For administrators, who may want to manage that content within a folder structure, there are some things to keep in mind. In the current release, both types of content can be moved around in folders, but the objects cannot be copied. In addition, SAS Viya 3.2 supports the promotion of SAS Visual Analytics Reports, but doesn’t support the promotion of Data Plans (support for Plans is coming in SAS Viya 3.3). So, what if I want to copy a report between, say my personal folders, to a production folder?

If you want copy a Report or Data Plan within an environment there is an easy way. When the object is open in edit mode you can do a Save As to save a copy to a different location in the folder structure.

Between environments, Reports can be exported and imported using the SAS Visual Analytics, when you are editing your content (Report or Data Plan) you can access a “diagnostics” window. The diagnostics window will show you the json (or xml) used to render the Report or Plan. To enter the diagnostics window use the keystrokes:

  • ctl+alt+d for SAS Visual Data Builder.
  • ctl+alt+b for SAS Visual Analytics.

In the steps below I will use the diagnostics window to save a Data Plan so that it can be loaded to a different SAS Viya Environment. The steps for a SAS Visual Analytics report are very similar.

In SAS Visual Data Builder when editing your Data Plan select ctl-alt-d to open the SAS Visual Data Builder Diagnostics window. The source tab of the window shows the json that will render the data plan.

Click Save to save the json to a text file and close the dialog. The json file will be saved in the browsers default downloads folder.

Copy the saved text file to a location accessible to the SAS Viya environment where you want to import the plan. In that environment, open Data Builder and click New to open a new Data Plan.

Click ctl-alt-d on the empty data plan and cut and paste the json from your text file replacing the json in the diagnostics window.

Click Parse to check the json.A message should be displayed indicating that the  “plan text was parsed successfully.”  Once you have parsed the text, click Run and the plan is loaded into SAS Visual Data Builder.

In SAS Visual Data Builder, select Save As and save the plan to any location in the folder structure.

The assumption with this approach is that the data is available in the same location in both environments.

You can do much the same with SAS Visual Analytics reports. The key-stroke is ctl-alt-b to open the SAS Visual Analytics Diagnostics window.  You can see the report xml or json on the BIRD tab.

To copy a single report between environments, you can select json and then save the json to a file. In the target environment open a new report, paste the json in the BIRD tab, parse and load and then save the report to a folder. This can be a useful approach if you want to relocate a report to a different location in your target environment. The transfer service currently will only import reports to the same folder location in the target that they are located in the source environment.

I hope you found this tip useful.

A tip for moving content between SAS Viya environments was published on SAS Users.

10月 132017
 

Every year in early October, the eyes of the world turn to Sweden and Norway, where the Nobel Prize winners are announced to the world. The Nobel Prize is considered the world's most prestigious award. Since 1901, the Prize has been presented to individuals and organizations that have made significant achievements in the fields of physics, chemistry, physiology or medicine, world peace and literature in each year (there were several exceptions during war years). In 1968, Sveriges Riksbank established the Sveriges Riksbank Prize in Economic Sciences in memory of Alfred Nobel, founder of the Nobel Prize. Today, individuals or organizations who are awarded Nobel Prizes and the Prize in Economic Sciences are called Nobel Laureates.

So far, more than 900 Nobel Laureates have been awarded. In this post, I wanted to learn a little more about these impressive individuals. Where were these Nobel Laureates from? Why do they get awarded? Is there any common characteristics you’ll find in these Laureates? Below you’ll find a preliminary analysis of Nobel Laureates using SAS Visual Analytics.

The analysis is based on data from List_of_Nobel_laureates, List of Nobel laureates by university affiliation and Nobel Laureates datasets at Kaggle, which definitely has some missing and inconsistent values. I have cleaned the data to correct for some obvious inconsistency as possible for my analysis.

How many Nobel Laureates have their been so far?

Recently, 12 new Nobel Laureates were awarded by the 2017 Nobel Prizes and Prize in Economic Sciences, and that makes 923 Laureates in total since the first Nobel Prize in 1901. Some Laureates share one prize, so we see more shared Laureates total in below table. While we see 27 organization winners of the Peace prize, most Laureates are individual winners.

analysis of the Nobel Laureates

The chart below shows the overall trend of annual total Nobel Laureates is increasing year-over-year, as more and more winners are sharing the Prize. The purple circle on the plot indicates that there are shared winners in that year. The average number of winners is about eight each year. Yet there was only one winner in 1916 for the Literature Prize. The most winners came in 2001, with 15 Laureates sharing the prizes. I also note from the chart that during the First World War, there were very few Nobel Prizes awarded, and during the Second World War, there were none.

Moreover, we know that most Nobel Laureates are awarded one Nobel Prize, yet I learned from childhood that the female scientist Marie Curie received two Nobel Prizes. If you search the datasets for winners awarded more than one Prize, you’ll find four scientists accomplished this feat. They are: Marie Curie, Linus Pauling, John Bardeen and Frederick Sanger.

Do Nobel Laureates live longer?

The answer is YES, per the research by Prof. Andrew Oswald from University of Warwick. Winning a Nobel prize adds about 1.5 years to the lifespan of Nobel Laureates compared to those who were merely nominated. Of course, it is not because of the monetary benefits that come with the Nobel Prize, but because of ‘the deep links between mind and body’, and that ‘happiness’ may make people live longer, which makes sense to me.

Since I don’t have the data of Nobel Prize nominees, let’s only test the lifespan of the Nobel Laureates and the ages they got awarded. The average life age of all Nobel Laureates is nearly 80, much older than the global average life expectancy of 71.4 years-old (according to World Health Organization 2015). Digging a bit more, we see Martin Luther King is the Nobel laureate (Peace, 1964) who died at youngest age. He was assassinated at 39 years old. Laureates who lived longest are Rita Levi-Montalcini (Medicine, 1986) and Ronald H. Coase (Economics, 1991), who both lived to 103 years old. You may also notice that the distribution of the Laureates’ lifespan is left skewed, the Nobel Prize winners certainly live longer than most.

In addition, something more worth noting:

  • The most laureates with the longest lifespans are from the Economics and Medicine categories. The Nobel Prize winning economists live longer than other categories’ winners on average. The average lifespan of these economists is about 86 years-old, five years longer than the second category of Medicine.
  • Economics winners are winning the awards at the highest age – 67 years-old on average. More digging shows that the oldest awarded age is 90 when Leonid Hurwicz (Economics, 2007) was awarded his Prize. We see the average awarded age of Physics winners is 56, which is 10+ years younger than that of the Economics winners. Thus, we get the impression that economists need more time to have outstanding achievements.
  • If we compare the time span between Laurates’ average awarded ages and their lifespan, the Physics Prize winners enjoy the longest life time after winning the award – about 20 years on average.
  • It is also worth noting that the Nobel Peace winners have the largest span of awarded age, about 70 years’ span. That’s because the youngest Nobel Laureates Malala Yousafzai, who got awarded of Nobel Peace Prize at 17 years-old in 2014.

The chart below is created in SAS Visual Analytics and shows the awarded ages of all individual Nobel Laureates in different prize categories. The reference line is the average awarded age of 59. It is very easy to note that no Nobel Prize was awarded during 1940-1943 due to the Second World War.

From which universities have Nobel Laureates graduated?

Next, let’s look at the educational background of Nobel Laureates. The left chart below obviously shows that much more Nobel winners hold Doctorate degrees than those of Bachelor or Master degrees. If we see the chart for Literature and Peace categories on the right, the difference is not that big. From the data, we know that the educational background of Nobel Laureates in Physics, Chemistry, Medicine and Economics categories (I call these four categories the scientific categories for easier description later) has the higher percentage of doctorate than that of winners in the Literature and Peace categories.

To learn more about the universities the Laureates in these scientific categories are graduated from, I ranked the top 10 university affiliations for the scientific categories in below chart, and their distribution among these categories, as well as the countries in which these universities are located.

The top 10 university affiliations were selected basing on the highest degree of the scientific categories’ Laureates obtained. That is, if one winner held a Master degree from Harvard University and a Doctorate degree from University of Cambridge, he/she is counted in University of Cambridge but not in the Harvard University. From the parallel coordinates plot, you may have noticed that the Physics in University of Cambridge and the Medicine in Harvard University are their greatest majors respectively. On the right, it shows the countries where these top 10 university affiliations are in United States, United Kingdom, France and Germany. The bar charts on the left show the percentage of educational degrees (Doctorate, Master, Bachelor) of each in the scientific categories (according to the available dataset). In the bottom chart, top 10 universities are ranked by their percentages. Perhaps now you have a great university in your mind for future education?

Next, I created the chart below to show the top eight countries having the university affiliations that more Nobel Prize winners graduated from. (Here the chart only shows for scientific categories, thus it excludes the Nobel Literature Prize and Peace prize.). An obvious trend we see from the chart is that the United States has the most Laureates spanning in the scientific categories after the Second World War, while Germany has more Laureates in the scientific categories comparatively before World War II.

Why do the Nobel Laureates get awarded?

Per the ‘nobelprize.org’, in his excerpt of the will, Alfred Nobel (1833-1896) dictates that his entire remaining estate should be used to endow "prizes to those who, during the preceding year, shall have conferred the greatest benefit to mankind." So Alfred's interests are reflected in the Prize, which said “The whole of his remaining realizable estate constitutes a fund, and the annually interest shall be divided into five equal parts, which shall be apportioned as follows: one part to the person who shall have made the most important discovery or invention within the field of physics; one part to the person who shall have made the most important chemical discovery or improvement; one part to the person who shall have made the most important discovery within the domain of physiology or medicine; one part to the person who shall have produced in the field of literature the most outstanding work in an ideal direction; and one part to the person who shall have done the most or the best work for fraternity between nations, for the abolition or reduction of standing armies and for the holding and promotion of peace congresses.”

Since it’s not easy to seek evidence in the datasets that Nobel Laureates are awarded by fulfilling Alfred’s will, what I do is to use SAS Visual Analytics text topics analysis performing some preliminary text analysis of the ‘Motivation’ field in the dataset for a validation to some extent. The ‘Motivation’ is given by ‘nobelprize.org’ for why the Laureate gets awarded. The analysis shows that the most frequently mentioned word is ‘discovery’, while the most 5 frequently appeared words include ‘work’, ‘development’, ‘contribution’, and ‘theory’. And from the topics analysis result, the top 10 topics are about ‘discovery’, ‘human”, “structure”, “economic”,” technique”, etc., which are reflecting Alfred Nobel‘s will in establishing the Prize. Moreover, the sentimental analysis result shows that the statements in the ‘Motivation’ field are mainly neutral (being ‘objective’), even though there are few positive and negative sentimental statements.

 

I hope you’ve found this analysis of Nobel Laureates data interesting. I believe there are still many other perspectives you can analyze to get insights. Is there anything interesting you see?

A preliminary analysis of the Nobel Laureates was published on SAS Users.

10月 122017
 

With SAS Data Management, you can setup SAS Data Remediation to manage and correct data issues. SAS Data Remediation allows user- or role-based access to data exceptions.

When a data issue is discovered it can be sent automatically or manually to a remediation queue where it can be corrected by designated users.

Let’s look how to setup a remediation service and how to send issue records to Data Remediation.

Register the remediation service.

To register a remediation service in SAS Data Remediation we go to Data Remediation Administrator “Add New Client Application.

Under Properties we supply an ID, which can be the name of the remediation service as long as it is unique, and a Display name, which is the name showing in the Remediation UI.

Under the tab Subject Area, we can register different subject categories for this remediation service.  When calling the remediation service we can categorize different remediation issues by setting different subject areas. We can, for example, use the Subject Area to point to different Data Quality Dimensions like Completeness, Uniqueness, Validity, Accuracy, Consistency.

Under the tab Issues Types, we can register issue categories. This enables us to categorize the different remediation issues. For example, we can point to the affected part of record like Name, Address, Phone Number.

At Task Templates/Select Templates we can set a workflow to be used for each issue type. You can design your own workflow using SAS Workflow Studio or you can use a prepared workflow that comes with Data Remediation. You need to make sure that the desired workflow is loaded on to Workflow Server to link it to the Data Remediation Service. Workflows are not mandatory in SAS Data Remediation but will improve efficiency of the remediation process.

Saving the remediation service will make it available to be called.

Sending issues to Data Remediation.

When you process data, and have identified issues that you want to send to Data Remediation, you can either call Data Remediation from the job immediately where you process the data or you store the issue records in a table first and then, in a second step, create remediation records via a Data Management job.

To send records to Data Remediation you can call remediation REST API form the HTTP Request node in a Data Management job.

Remediation REST API

The REST API expects a JSON structure supplying all required information:

{
	"application": "mandatory",
	"subjectArea": "mandatory",
	"name": "mandatory",
	"description": "",
	"userDefinedFieldLabels": {
		"1": "",
		"2": "",
		"3": ""
	},
	"topics": [{
		"url": "",
		"name": "",
		"userDefinedFields": {
			"1": "",
			"2": "",
			"3": ""
		},
		"key": "",
		"issues": [{
			"name": "mandatory",
			"importance": "",
			"note": "",
			"assignee": {
				"name": ""
			},
			"workflowName": "",
			"dueDate": "",
			"status": ""
		}]
	}]
}

 

JSON structure description:

In a Data Management job, you can create the JSON structure in an Expression node and use field substitution to pass in the necessary values from the issue records. The expression code could look like this:

REM_APPLICATION= "Customer Record"
REM_SUBJECT_AREA= "Completeness"
REM_PACKAGE_NAME= "Data Correction"
REM_PACKAGE_DESCRIPTION= "Mon-Result: " &formatdate(today(),"DD MM YY") 
REM_URL= "http://myserver/Sourcesys/#ID=" &record_id
REM_ITEM_NAME= "Mobile phone number missing"
REM_FIELDLABEL_1= "Source System"
REM_FIELD_1= "CRM"
REM_FIELDLABEL_2= "Redord ID"
REM_FIELD_2= record_id
REM_FIELDLABEL_3= "-"
REM_FIELD_3= ""
REM_KEY= record_id
REM_ISSUE_NAME= "Phone Number"
REM_IMPORTANCE= "high"
REM_ISSUE_NOTE= "Violated data quality rule phone: 4711"
REM_ASSIGNEE= "Ben"
REM_WORKFLOW= "Customer Tag"
REM_DUE-DATE= "2018-11-01"
REM_STATUS= "open"
 
JSON_REQUEST= '
{
  "application":"' &REM_APPLICATION &'",
  "subjectArea":"' &REM_SUBJECT_AREA &'",
  "name":"' &REM_PACKAGE_NAME &'",
  "description":"' &REM_PACKAGE_DESCRIPTION &'",
  "userDefinedFieldLabels": {
    "1":"' &REM_FIELDLABEL_1 &'",
    "2":"' &REM_FIELDLABEL_2 &'",
    "3":"' &REM_FIELDLABEL_3 &'"
  },
  "topics": [{
    "url":"' &REM_URL &'",
    "name":"' &REM_ITEM_NAME &'",
    "userDefinedFields": {
      "1":"' &REM_FIELD_1 &'",
      "2":"' &REM_FIELD_2 &'",
      "3":"' &REM_FIELD_3 &'"
    },
    "key":"' &REM_KEY &'",
    "issues": [{
      "name":"' &REM_ISSUE_NAME &'",
      "importance":"' &REM_IMPORTANCE &'",
      "note":"' &REM_ISSUE_NOTE &'",
      "assignee": {
        "name":"' &REM_ASSIGNEE &'"
      },
      "workflowName":"' &REM_WORKFLOW &'",
      "dueDate":"' &REM_DUE_DATE &'",
      "status":"' &REM_STATUS &'"
    }]
  }]
}'

 

Tip: You could also write a global function to generate the JSON structure.

After creating the JSON structure, you can invoke the web service to create remediation records. In the HTTP Request node, you call the web service as follows:

Address:  http://[server]:[port]/SASDataRemediation/rest/groups
Method: post
Input Filed: The variable containing the JSON structure. I.e. JSON_REQUEST
Output Filed: A field to take the output from the web service. You can use the New button create a filed and set the size to 1000
Under Security… you can set a defined user and password to access Data Remediation.
In the HTTP Request node’s advanced settings set the WSCP_HTTP_CONTENT_TYPE options to application/json

 

 

 

You can now execute the Data Management job to create the remediation records in SAS Data Remediation.

Improving data quality through SAS Data Remediation was published on SAS Users.

9月 282017
 

SAS Viya: What’s in it for me?If you’re in the field of analytics, you’ve undoubtedly heard about SAS Viya, our new, open analytic platform. Designed for all analytic professionals, regardless of skills or experience, SAS Viya seamlessly handles big, complex, diverse data and can bridge SAS 9.4. It also provides a tool that supports any programming language, allowing analysts to choose the tool that makes them most productive.

Recently a colleague of mine, Leo Sadovy, wrote the blog post SAS Viya: What’s in it for me? The business? This post describes the benefits of SAS Viya for the line of business owner. Spoiler alert: When it comes to analytics, SAS Viya provides the best of all worlds.

But what does SAS Viya mean to me … if I’m a current SAS user? As the communication manager for our existing SAS user base, Leo’s post inspired me to ask a similar question on behalf of our SAS users.

So, I hit the road, found a few smart colleagues (who know a lot more than I do about SAS Viya!) and recorded the Facebook Live video you’ll find attached below.

You’ll learn what SAS Viya is and what motivated us to create it, what it means to you as a SAS user (a new or longtime one), and what learning tools and other resources are available to you to learn even more.

Enjoy!

SAS Viya: What's in it for me? The user

Learn more about SAS Viya

And, if you have any other questions about SAS Viya, feel free to leave them in the comments field. I’ll get back to if I have the answer… or find someone else who can help, if I don't!

SAS Viya: What’s in it for me? The user. was published on SAS Users.

9月 252017
 

With the fifth maintenance release of SAS 9.4 (SAS 9.4M5) now available, it seems like a good time to get a refresher on some of the ways you can upgrade your existing SAS deployments to the latest release. Among several benefits, SAS 9.4M5 provides closer integration with the CAS in-memory runtime engine in SAS Viya so it’s something you might want to consider.

There are three ways you can upgrade a SAS Deployment, they are illustrated in the diagram below. Let’s take a look at each one:

Upgrading a SAS 9 deployment

Automated Migration

Automated migration consists of creating a new SAS deployment from an existing SAS Deployment using the automated migration tools the SAS Migration Utility and SAS Deployment Wizard. The automated migration tools and process are designed to create a target deployment that preserves the content and configuration information from the source. These tools require an all-at-once approach, and provide limited options for making changes to the deployment during the upgrade. The automated migration tools support a like-for-like transition—the operating system family and the distribution of SAS components must be the same in the source and target environments.

Deploy new and Promote

Promotion is a related concept to migration; promotion is the process of copying selected metadata and associated content within or between SAS Deployments. To upgrade using promotion, create a new out-of-the-box SAS deployment and use the export/import functionality to move the content from the old deployment. The promotion framework is designed to allow you to selectively move content from one deployment to another. Depending on the release of your source deployment there are limits to the content that can be exported using promotion; however, this option offers the most flexibility in changing the deployed topology, operating system and transitioning in stages.

Update-in-Place

Update-in-Place is the process of upgrading an existing SAS Deployment to apply maintenance or add and update SAS products. The update modifies the existing deployment rather than creating a new deployment. Update-in-place is only supported within a major SAS release. For example, you can use update-in-place to update a SAS 9.4 deployment to maintenance 4 or maintenance 5, however you cannot use update-in-place to transition from SAS 9.3 to SAS 9.4.

Considerations when Deciding on an Approach

Automated Migration

  • Automated migration moves a whole system, configuration and content, you cannot move only part of a system.
  • The tools support migration from (n-2), so the earliest release you can migrate from is currently SAS 9.2.
  • Individual SAS Products and solutions may have additional baselines for migration.
  • During a migration you cannot change your:
    • Operating system family.
    • Software Topology (the distribution of the SAS components in the environment).

Deploy new and Promote

  • Deploy New and promote creates a new deployment and moves content into it.
  • The target system can be a different topology and operating system from the source.
  • The tools support promotion from (n-2), so the earliest release you can promote from is currently SAS 9.2.
  • Content can be transitioned in stages.
  • Not all content is supported by the import and export wizards and batch tools (e.g., dashboard content cannot be promoted between releases and portal content requires execution of a command line tool).

Update-in-Place

  • Update-in-Place can only be done within the same major SAS release.
  • Updates operate on a whole deployment, you cannot selectively update products.
  • Maintenance updates do not preserve the source environment because maintenance releases:
    • are applied to the existing environment.
    • are cumulative (including hotfixes).
    • cannot be uninstalled, e.g. you cannot uninstall M2 to get back to M1.
  • Adding products to a Deployment will require a second pass through the SAS Deployment Wizard (update and then add).

Summary

I would encourage you to consider your target SAS 9.4 deployment as a new house.

Using automated migration you would move the house. Take the whole house off its current foundation. Move the house, contents and all, to a new foundation.

  • The Layout of the house remains the same.
  • Everything in the house moves at one time.
  • Anything outside the house does not move.

Using deploy new and promote you can selectively move the content of your house to a whole new house. You can leave your junk behind. Your new house:

  • Must be built before you move in.
  • Can be constructed any way you want.

No matter how hard you try you cannot move all content.

With update-in-place you have a renovation project of your existing house. However, if your house is old, you cannot or may not want to renovate it. Plus, if you mess up the renovation then your existing house is broken.

You can find the documentation on upgrading in a variety of places.

A review of the options available for upgrading a SAS 9 deployment was published on SAS Users.

9月 152017
 

Whether you are a SAS code creator, a blogger, a technical writer, an editor-in-chief, an executive, a secretary, a developer or programmer in any programming language or simply someone who uses computer or hand-held device for writing, you need to read this blog post – your life is about to change forever!

Did you know that you can use a web browser as a SAS code editor? I’m not talking about browser-based SAS programming interfaces like SAS University Edition or SAS Studio; these are full-blown applications. I’m talking about converting a regular web browser into a “notepad” where you can type, display, and save your SAS code. Or non-SAS code. Or practically anything. And you don’t even have to be connected to the Internet to use this browser functionality.

Converting a web browser into a notepad

This trick works with most modern browsers:

  • Chrome
  • Firefox
  • Opera
  • Safari

It will not work on Internet Explorer 11.

Try this: open your web browser (I am using Firefox in the examples below) or a new tab in your browser and type the following in the URL field (case insensitive):

data:text/html,<html contentEditable>

Hit Enter. Then click anywhere in the browser body.

Your browser has just turned into a Notepad. You can now type anything in it, including SAS code:

Using web browser as SAS code editor

In order to save your SAS code in Firefox, click on File ⇒ Save Page As… and save it as type Text Files (*.txt;*.text):

Saving SAS code in a browser as text file

This functionality is possible thanks to HTML5’s contentEditable attribute and the browsers’ ability to handle data URL.

I don’t know about you, but I find this browser-notepad feature very cool and handy. Not only does it allow you to type SAS code in your browser, but it also gives you the capability to take notes and copy & paste excerpts or code snippets from other web pages on non-web applications. If you use WebEx or Skype or Lync to present one of the SAS web browser-based products such as SAS Visual Analytics, SAS Visual Statistics, etc., you can share your browser to your audience and make one of the tabs a typeable area. Then during your presentation you may switch between browser tabs depending on whether you are presenting SAS VA/VS or your own on-the-fly typing.

Bookmark notepad in a browser

If you like this Notepad browser feature, you can easily bookmark it by placing it on the Bookmarks toolbar. In this case, I suggest typing the following line in the URL filed:

data:text/html,<html contentEditable><title>Notepad</title>

and then dragging the image in front of this URL string and dropping it to the Bookmarks toolbar to create a button. Then, every time you need a Notepad it is at your fingertips; you just need to click the button:

Bookmark SAS code editor in a browser

Styling your new SAS editor in a browser

By default, your browser editor does not look pretty. However, you can apply CSS styles to it to make it look better. You can control font (style, size, color), margins, paddings, background and other CSS style attributes. For example, try the following URL:

data:text/html, <textarea style="width:100%; height:100%; padding:20px; font-size:2em; font-family: SAS Monospace; color:darkblue; border:none; border-left: 10px solid lightblue; margin-left: 30px;" autofocus/>

Your web browser editor becomes much more presentable:

Customize SAS code editor in a browser

The autofocus attribute places cursor immediately in the typing area of the browser notepad, without having to click on the browser body first.

I want to hear from you!

Do you like this editable browser feature? Would you use it to enhance your presentations? Do you envision yourself writing SAS code in a browser? An article, a blog post? What other usages can you envision using such a web browser transformation? Do you have any ideas to expand this notepad browser functionality beyond presenting, typing, taking notes, copying/pasting, and saving your SAS code? Can you apply SAS color syntax highlighting in a browser? Or a background image? How about submitting your SAS code from a browser?

Using a web browser as a SAS code editor was published on SAS Users.

9月 152017
 

ATTRS The SGPLOT procedure (as well as other ODS Graphics procedures) does a great job of creating nice- looking output with very little coding. However, there are times when you want to make adjustments to the output's appearance. For those occasions, we have an ATTRS for that!

The statements in PROC SGPLOT include many options that enable you to change the attributes for parts of the plot. Each of these options ends in ATTRS, which makes them easy to find in code.

Before you can change the attributes, you need to know which part of the plot you want to change.  For example, do you want to change the color of the line, the marker symbol, the size of the label font, and so on? Once you know the part of the graph that you want to change, you can search the PROC SGPLOT documentation for an ATTRS option.

In the following PROC SGPLOT code, we have added some ATTRS options to demonstrate the types of changes you can make to a graph.

proc sgplot data=sashelp.class;
vbar age / stat=freq datalabel datalabelattrs=(size=12pt color=blue)
fillattrs=(color=cx66A5A0) transparency=0.3 
dataskin=matte name='bar' 
legendlabel='Frequency of age';
vline age / stat=percent markers 
markerattrs=(symbol=circlefilled color= cx01665E size=12px) 
lineattrs=(color=cxD05B5B thickness=3px) 
curvelabel='Percent Line' 
curvelabelattrs=(size=11pt style=italic)
curvelabelloc=inside curvelabelpos=min 
name='vline' legendlabel='Percent of age' y2axis;
refline 4 / axis=y lineattrs=(pattern=2 thickness=2px) label='Refline' 
labelattrs=(size=12pt) labelpos=min labelloc=inside;
xaxis valueattrs=(size=10pt color=navy);
yaxis labelattrs=(size=12pt weight=bold) offsetmin=0;
keylegend 'bar' 'vline' / title='My legend' 
titleattrs=(color=blue size=14pt)
valueattrs=(size=12pt) noborder;
run;

 

The figure below shows the graph that is produced by this PROC SGPLOT code. In the figure, some labels are added to help you identify the part of the graph that is modified using an ATTRS option. Note that this graph depicts only some of the ATTRS options that are available. For other ATTRS options, see the SAS® 9.4 ODS Graphics: Procedures Guide, Sixth Edition for the specific plot statement that you want to use.

In this figure:

  • The LABELATTRS= option enables you to change the color, font family, font weight, font style, and size for the axis or reference line labels.
  • The LINEATTRS= option enables you to change the color, pattern, and thickness for the plot line.
  • The CURVELABELATTRS= option enables you to change the color, font family, font weight, font style, and size for the text that is added by the CURVELABEL= option.
  • The DATALABELATTRS= option enables you to change the color, font family, font weight, font style, and size for the text that is added by the DATALABEL= option.
  • The MARKERATTRS= option enables you to change the color, size, and symbol for the plot markers.
  • The FILLATTRS= option enables you to change the color and transparency of the bar colors.
  • The VALUEATTRS= option enables you to change the color, font family, font weight, font style, and size for the axis tick-value labels or legend value labels.
  • The TITLEATTRS= option enables you to change the color, font family, font weight, font style, and size for the legend title.

For more information about attribute options, see the Commonly Used Attribute Options section of the SAS® 9.4 ODS Graphics: Procedures Guide, Sixth Edition.

The ATTRS options affect all of the output that is produced by that statement. This means that if you include the GROUP= option, all of the groups use the attributes that are specified in the ATTRS options. This behavior is great if you want all of the lines to use the same line pattern, but it can be a problem if you want to specify colors for each of your lines.

Beginning with SAS 9.4, the STYLEATTRS (notice the ATTRS ending) statement is part of the SPLOT (and SGPANEL) procedure to enable you to define attributes for grouped data.

For example, the following code uses the DATACONTRASTCOLORS= option to specify the colors for the marker symbols and the DATASYMBOLS= option to specify the symbols that are to be used.

ods graphics / attrpriority=none;
 
proc sgplot data=sashelp.class;
styleattrs datacontrastcolors=(pink blue)
datasymbols=(circlefilled squarefilled);
scatter x=age y=height / group=sex markerattrs=(size=10px);
xaxis valueattrs=(size=12pt) labelattrs=(size=14pt);
yaxis valueattrs=(size=12pt) labelattrs=(size=14pt);
keylegend / valueattrs=(size=12pt) titleattrs=(size=14pt);
run;

 

You also might need to add the ATTRPRIORITY=NONE option in your ODS GRAPHICS statement to cycle the colors and symbols as expected. For more information about how the attributes are applied to the grouped values, see the How the Attributes Are Cycled section of the SAS® 9.4 ODS Graphics: Procedures Guide, Sixth Edition.

The attributes that are listed in the STYLEATTRS statement are associated with the group values in the order in which they appear in the data set. This behavior can cause the same value to be associated with a different color when you use the same code with another set of data.

To associate an attribute with a specific data value, you can define an attribute map. The attribute map is a data set, referenced in the DATTRMAP= option in the PROC SGPLOT statement, which includes variables that indicate to the SGPLOT procedure how to assign attributes to the group variable values.

Within the attribute map, the ID variable identifies the variables that are specific to a particular set of group values. The VALUE variable identifies the data value for the group variable that you want to associate with attributes. Note that if the variable for the GROUP= option has an associated format, the VALUE variable in the attribute map needs to contain the formatted value.

The other variables in the attribute map data set define attributes such as color, symbol, line thickness, and so on.

For example, the following code defines an attribute map to assign the color pink and the filled-circle  symbol to group value F and the color blue and the filled-square symbol to the group value M:

data myattrmap;
id='scattersymbols';
length markersymbol $12;
input value $ markercolor $ markersymbol $;
datalines;
F pink circlefilled
M blue squarefilled
;
 
proc sgplot data=sashelp.class dattrmap=myattrmap;
scatter x=age y=height / group=sex markerattrs=(size=10px) attrid=scattersymbols;
xaxis valueattrs=(size=12pt) labelattrs=(size=14pt);
yaxis valueattrs=(size=12pt) labelattrs=(size=14pt);
keylegend / valueattrs=(size=12pt) titleattrs=(size=14pt);
run;

 

Your attribute-map data set can contain multiple attribute maps, using a different value for the ID variable to distinguish each of the attribute maps. For more information about attribute maps, see the Using Attribute Maps to Control Visual Attributes section of the SAS® 9.4 ODS Graphics: Procedures Guide, Sixth Edition.

As you can see, there are many ways to assign attributes to plot elements. So, the next time you want to make a change to the visual appearance of your graph, remember that we have an ATTRS for that!

If you would like to see how to make attribute changes using a style template, read Dan Heath’s 2017 SAS Global Forum paper, Diving Deep into SAS® ODS Graphics Styles.

PROC SGPLOT: There’s an ATTRS for that was published on SAS Users.

9月 082017
 

In SAS Visual Analytics 8.1, report creators have the ability to include drive-distance and drive-time in their geographical maps, but only if their site has an Esri ArcGIS Online account and they have valid credentials for the account.

In the user Settings for SAS Visual Analytics Geographic Mapping 8.1 release, there are three choices for selection of a geographic map provider.  The map provider creates the background map for geo maps and for network diagrams that display a map.

The map provider options are:

  • OpenStreet Map service, hosted at SAS.
  • Esri ArGIS Online Services, which only requirFinale acceptance of the terms and conditions.
  • Esri premium services, which requires a credential validation.

If Esri premium services is selected, there is an additional prompt for valid credentials, and you must still accept the Esri ArcGIS Online Services terms in order to select the premium services checkbox.

It’s also worth pointing out here, that even if you have Esri premium credentials, in order for these credentials to be validated in SAS Visual Analytics, you must also be a member of the ESRI Users custom group.  Users can be added to this group in SAS Environment Manager, as shown below.

Note that without the Esri ‘premium’ service and validated credentials, when you right-click and Create geographic selection in your report map, you are only able to select the Distance selection, which displays the radial distance for the selection point.

With premium services in effect, you can also select drive-time or drive-distance.  An example of a drive-time selection is shown here.  Drive-time creates an irregular selection based on the distance that can be driven in the specified amount of time.

A drive-distance example is shown below.  Drive-distance creates an irregular selection based on the driving distance using roads.

When selecting drive-time or drive distance, you can also add breaks to show, as in the example below, the 5-mile distance, the 10-mile distance, and the 15-mile distance on the maps.

It’s also worth pointing out, that if a viewer of the report has not had Esri premium credentials validated, the viewer will be unable to view the drive-distances and drive-time features.  The settings for users of the report viewer are also stored in the Report Viewer Geographic Mapping user settings.

If a user is adding a connection to the server in SAS Mobile BI 8.15 and their account is a member of the Esri Users group, they will be prompted for their Esri premium credentials when adding the server connection:

I hope you’ve found this post helpful.

How do I access the Premium Esri Map Service for my SAS Visual Analytics reports? was published on SAS Users.

8月 212017
 

The stored compiled macro facility enables you to compile and save your macro definition in a permanent catalog in a library that you specify. The macro is compiled only once. When you call the macro in the current and subsequent SAS® sessions, SAS executes the compiled code from the macro catalog that you created when you compiled the macro.

The stored compiled facility has two main purposes. The first is that it enables your code to run faster because the macro code does not need to be compiled each time it is executed. The second purpose is to help you protect your code. Sometimes you need to share code that you’ve written with other users, but you do not want them to be able to see the code that is being executed. The stored compiled macro facility enables you to share the program without revealing the code. Compiling the macro with the SECURE option prevents the output of the SYMBOLGEN, MPRINT, and MLOGIC macro debugging options from being written to the log when the macro executes. This means that no code is written to the log when the code executes. After the macro has been compiled, there is no way to decompile it to retrieve the source code that created the catalog entry. This behavior prevents the user from being able to retrieve the code. However, it also prevents you from being able to recover the code.

It is very important to remember that there is no way to get back the code from a stored compiled macro. Because of this behavior, you should ALWAYS save your code when creating a stored compiled macro catalog. In order to update a stored compiled macro, you must recompile the macro. The only way to do this is to submit the macro definition again. Another important fact is that a stored compiled macro catalog can be used only on the same operating system and release of SAS that it was created on. So, in order to use a stored compiled macro on another operating system or release of SAS, that macro must be compiled in the new environment. Again, the only way to compile the macro is to resubmit the macro definition.

Save the Macro Source Code

To make it easier for you to save your code, the %MACRO statement contains the SOURCE option. When you create a stored compiled macro, the SOURCE option stores the macro definition as part of a catalog entry in the SASMACR catalog in the permanent SAS library listed on the SASMSTORE= system option.

Here is the syntax needed to create a stored compiled macro with the SOURCE option set:

libname mymacs 'c:\my macro library';   ❶                                                                                                
options mstored sasmstore=mymacs;       ❷                                                                                              
 
%macro test / store source;             ❸                                                                                                          
 
  libname mylib1 'path-to-my-first-library';                                                                                            
  libname mylib2 'path-to-my-second-library';                                                                                           
 
%mend;

 

❶ The LIBNAME statement points to the SAS library that will contain my stored compiled macro catalog.

❷ The MSTORED system option enables the stored compiled facility. The SASMSTORE= option points to the libref that points to the macro library.

❸ The STORE option instructs the macro processor to store the compiled version of TEST in the SASMACR catalog in the library listed in the SASMSTORE= system option. The SOURCE option stores the TEST macro definition in the same SASMACR catalog.

Note that the contents of the SASMACR catalog do not contain an entry for the macro source. The source has been combined with the macro entry that contains the compiled macro. To verify that the source has been saved, add the DES= option to the %MACRO statement. The DES= option enables you specify a description for the macro entry in the SASMACR catalog. So for example, you could add the following description when compiling the macro to indicate that the source code has been saved:

%macro test / store source des=’Source code saved with entry’;

 

You can look at the contents of the macro catalog using the CATALOG procedure:

proc catalog cat=a.sasmacr;                                                                                                            
contents;                                                                                                                               
run;                                                                                                                                    
quit;

 

You see the description indicating that the source code was saved with the macro entry in the output from PROC CATALOG:

Retrieve the Macro Source Code

When you need to update the macro or re-create the catalog on another machine, you can retrieve the macro source code using the %COPY statement. The %COPY statement enables you to retrieve the macro source code and write the code to a file. Here is the syntax:

%copy test / source outfile='c:\my macro library\test.sas';

 

This %COPY statement writes the source code for the TEST macro to the TEST.SAS file. Using TEST.SAS, you are now able to update the macro or compile the macro on another machine.

Remember, you should always save your source code when creating a stored compiled macro. Without the source code, you will not be able to update the macro or move the macro to a new environment.

Here are the relevant links for this article:

Always save your code when creating a stored compiled macro was published on SAS Users.