Tech

11月 042017
 

Internet of Things for dementiaDementia describes different brain disorders that trigger a loss of brain function. These conditions are all usually progressive and eventually severe. Alzheimer's disease is the most common type of dementia, affecting 62 percent of those diagnosed. Other types of dementia include; vascular dementia affecting 17 percent of those diagnosed, mixed dementia affecting 10 percent of those diagnosed.

Dementia Statistics

There are 850,000 people with dementia in the UK, with numbers set to rise to over 1 million by 2025. This will soar to 2 million by 2051. 225,000 will develop dementia this year, that’s one every three minutes. 1 in 6 people over the age of 80 have dementia. 70 percent of people in care homes have dementia or severe memory problems. There are over 40,000 people under 65 with dementia in the UK. More than 25,000 people from black, Asian and minority ethnic groups in the UK are affected.

Cost of treating dementia

Two-thirds of the cost of dementia is paid by people with dementia and their families. Unpaid careers supporting someone with dementia save the economy £11 billion a year. Dementia is one of the main causes of disability later in life, ahead of cancer, cardiovascular disease and stroke (Statistic can be obtained from here - Alzheimer’s society webpage). To tackle dementia requires a lot of resources and support from the UK government which is battling to find funding to support NHS (National Health Service). During 2010–11, the NHS will need to contribute £2.3bn ($3.8bn) of the £5bn of public sector efficiency savings, where the highest savings are expected primarily from PCTs (Primary care trusts). In anticipation for tough times ahead, it is in the interest of PCTs to obtain evidence-based knowledge of the use of their services (e.g. accident & emergency, inpatients, outpatients, etc.) based on regions and patient groups in order to reduce the inequalities in health outcomes, improve matching of supply and demand, and most importantly reduce costs generated by its various services.

Currently in the UK, general practice (GP) doctors deliver primary care services by providing treatment and drug prescriptions and where necessary patients are referred to specialists, such as for outpatient care, which is provided by local hospitals (or specialised clinics). However, general practitioners (GPs) are limited in terms of size, resources, and the availability of the complete spectrum of care within the local community.

Solution in sight for dementia patients

There is the need to prevent or avoid delay for costly long-term care of dementia patients in nursing homes. Using wearables, monitors, sensors and other devices, NHS based in Surrey is collaborating with research centres to generate ‘Internet of Things’ data to monitor the health of dementia patients at the comfort of staying at home. The information from these devices will help people take more control over their own health and wellbeing, with the insights and alerts enabling health and social care staff to deliver more responsive and effective services. (More project details can be obtained here.)

Particle filtering

One method that could be used to analyse the IoT generated data is particle filtering methods. IoT dataset naturally fails within Bayesian framework. This method is very robust and account for the combination of historical and real-time data in order to make better decision.

In Bayesian statistics, we often have a prior knowledge or information of the phenomenon/application been modelled. This allows us to formulate a Bayesian model, that is, prior distribution, for the unknown quantities and likelihood functions relating these quantities to the observations (Doucet et al., 2008). As new evidence becomes available, we are often interested in updating our current knowledge or posterior distribution. Using State Space Model (SSM), we are able to apply Bayesian methods to time series dataset. The strength of these methods however lies in the ability to properly define our SSM parameters appropriately; otherwise our model will perform poorly. Many SSMs suffers from non-linearity and non-Gaussian assumptions making the maximum likelihood difficult to obtain when using standard methods. The classical inference methods for nonlinear dynamic systems are the extended Kalman filter (EKF) which is based on linearization of a state and trajectories (e.g. Johnson et al., 2008 ). The EKF have been successfully applied to many non-linear filtering problems. However, the EKF is known to fail if the system exhibits substantial nonlinearity and/or if the state and the measurement noise are significantly non-Gaussian.

An alternative method which gives a good approximation even when the posterior distribution is non-Gaussian is a simulation based method called Monte Carlo. This method is based upon drawing observations from the distribution of the variable of interest and simply calculating the empirical estimate of the expectation.

To apply these methods to time series data where observation arrives in sequential order, performing inference on-line becomes imperative hence the general term sequential Monte Carlo (SMC). A SMC method encompasses range of algorithms which are used for approximate filtering and smoothing. Among this method is particle filtering. In most literature, it has become a general tradition to present particle filtering as SMC, however, it is very important to note this distinction. Particle filtering is simply a simulation based algorithm used to approximate complicated posterior distributions. It combines sequential importance sampling (SIS) with an addition resampling step. SMC methods are very flexible, easy to implement, parallelizable and applicable in very general settings. The advent of cheap and formidable computational power in conjunction with some recent developments in applied statistics, engineering and probability, have stimulated many advancements in this field (Cappe et al. 2007). Computational simplicity in the form of not having to store all the data is also an additional advantage of SMC over MCMC (Markov Chain Monte Carlo).

References

[1] National Health Service England, http://www.nhs.uk/NHSEngland/aboutnhs/Pages/Authoritiesandtrusts.aspx (accessed 18 August 2009).

[2] Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88: 2297–2301, 1991

[3] Pincus SM and Goldberger AL. Physiological time-series analysis: what does regularity quantify?  Am J Physiol Heart Circ Physiol 266: H1643–H1656, 1994

[4] Cappe, O.,S. Godsill, E. Moulines(2007).An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE. Volume 95, No 5, pp 899-924.

[5] Doucet, A., A. M. Johansen(2008). A Tutorial on Particle Filtering and Smoothing: Fifteen years Later.

[6] Johansen, A. M. (2009).SMCTC: Sequential Monte Carlo in C++. Journal of Statistical Software. Volume 30, issue 6.

[7] Rasmussen and Z.Ghahramani (2003). Bayesian Monte Carlo. In S. Becker and K. Obermayer, editors, Advances in Neural Information Processing Systems, volume 15.

[8] Osborne A. M.,Duvenaud D., GarnettR.,Rasmussen, C.E., Roberts, C.E.,Ghahramani, Z. Active Learning of Model Evidence Using Bayesian Quadrature.

Scaling Internet of Things for dementia using Particle filters was published on SAS Users.

11月 042017
 

Internet of Things for dementiaDementia describes different brain disorders that trigger a loss of brain function. These conditions are all usually progressive and eventually severe. Alzheimer's disease is the most common type of dementia, affecting 62 percent of those diagnosed. Other types of dementia include; vascular dementia affecting 17 percent of those diagnosed, mixed dementia affecting 10 percent of those diagnosed.

Dementia Statistics

There are 850,000 people with dementia in the UK, with numbers set to rise to over 1 million by 2025. This will soar to 2 million by 2051. 225,000 will develop dementia this year, that’s one every three minutes. 1 in 6 people over the age of 80 have dementia. 70 percent of people in care homes have dementia or severe memory problems. There are over 40,000 people under 65 with dementia in the UK. More than 25,000 people from black, Asian and minority ethnic groups in the UK are affected.

Cost of treating dementia

Two-thirds of the cost of dementia is paid by people with dementia and their families. Unpaid careers supporting someone with dementia save the economy £11 billion a year. Dementia is one of the main causes of disability later in life, ahead of cancer, cardiovascular disease and stroke (Statistic can be obtained from here - Alzheimer’s society webpage). To tackle dementia requires a lot of resources and support from the UK government which is battling to find funding to support NHS (National Health Service). During 2010–11, the NHS will need to contribute £2.3bn ($3.8bn) of the £5bn of public sector efficiency savings, where the highest savings are expected primarily from PCTs (Primary care trusts). In anticipation for tough times ahead, it is in the interest of PCTs to obtain evidence-based knowledge of the use of their services (e.g. accident & emergency, inpatients, outpatients, etc.) based on regions and patient groups in order to reduce the inequalities in health outcomes, improve matching of supply and demand, and most importantly reduce costs generated by its various services.

Currently in the UK, general practice (GP) doctors deliver primary care services by providing treatment and drug prescriptions and where necessary patients are referred to specialists, such as for outpatient care, which is provided by local hospitals (or specialised clinics). However, general practitioners (GPs) are limited in terms of size, resources, and the availability of the complete spectrum of care within the local community.

Solution in sight for dementia patients

There is the need to prevent or avoid delay for costly long-term care of dementia patients in nursing homes. Using wearables, monitors, sensors and other devices, NHS based in Surrey is collaborating with research centres to generate ‘Internet of Things’ data to monitor the health of dementia patients at the comfort of staying at home. The information from these devices will help people take more control over their own health and wellbeing, with the insights and alerts enabling health and social care staff to deliver more responsive and effective services. (More project details can be obtained here.)

Particle filtering

One method that could be used to analyse the IoT generated data is particle filtering methods. IoT dataset naturally fails within Bayesian framework. This method is very robust and account for the combination of historical and real-time data in order to make better decision.

In Bayesian statistics, we often have a prior knowledge or information of the phenomenon/application been modelled. This allows us to formulate a Bayesian model, that is, prior distribution, for the unknown quantities and likelihood functions relating these quantities to the observations (Doucet et al., 2008). As new evidence becomes available, we are often interested in updating our current knowledge or posterior distribution. Using State Space Model (SSM), we are able to apply Bayesian methods to time series dataset. The strength of these methods however lies in the ability to properly define our SSM parameters appropriately; otherwise our model will perform poorly. Many SSMs suffers from non-linearity and non-Gaussian assumptions making the maximum likelihood difficult to obtain when using standard methods. The classical inference methods for nonlinear dynamic systems are the extended Kalman filter (EKF) which is based on linearization of a state and trajectories (e.g. Johnson et al., 2008 ). The EKF have been successfully applied to many non-linear filtering problems. However, the EKF is known to fail if the system exhibits substantial nonlinearity and/or if the state and the measurement noise are significantly non-Gaussian.

An alternative method which gives a good approximation even when the posterior distribution is non-Gaussian is a simulation based method called Monte Carlo. This method is based upon drawing observations from the distribution of the variable of interest and simply calculating the empirical estimate of the expectation.

To apply these methods to time series data where observation arrives in sequential order, performing inference on-line becomes imperative hence the general term sequential Monte Carlo (SMC). A SMC method encompasses range of algorithms which are used for approximate filtering and smoothing. Among this method is particle filtering. In most literature, it has become a general tradition to present particle filtering as SMC, however, it is very important to note this distinction. Particle filtering is simply a simulation based algorithm used to approximate complicated posterior distributions. It combines sequential importance sampling (SIS) with an addition resampling step. SMC methods are very flexible, easy to implement, parallelizable and applicable in very general settings. The advent of cheap and formidable computational power in conjunction with some recent developments in applied statistics, engineering and probability, have stimulated many advancements in this field (Cappe et al. 2007). Computational simplicity in the form of not having to store all the data is also an additional advantage of SMC over MCMC (Markov Chain Monte Carlo).

References

[1] National Health Service England, http://www.nhs.uk/NHSEngland/aboutnhs/Pages/Authoritiesandtrusts.aspx (accessed 18 August 2009).

[2] Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88: 2297–2301, 1991

[3] Pincus SM and Goldberger AL. Physiological time-series analysis: what does regularity quantify?  Am J Physiol Heart Circ Physiol 266: H1643–H1656, 1994

[4] Cappe, O.,S. Godsill, E. Moulines(2007).An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE. Volume 95, No 5, pp 899-924.

[5] Doucet, A., A. M. Johansen(2008). A Tutorial on Particle Filtering and Smoothing: Fifteen years Later.

[6] Johansen, A. M. (2009).SMCTC: Sequential Monte Carlo in C++. Journal of Statistical Software. Volume 30, issue 6.

[7] Rasmussen and Z.Ghahramani (2003). Bayesian Monte Carlo. In S. Becker and K. Obermayer, editors, Advances in Neural Information Processing Systems, volume 15.

[8] Osborne A. M.,Duvenaud D., GarnettR.,Rasmussen, C.E., Roberts, C.E.,Ghahramani, Z. Active Learning of Model Evidence Using Bayesian Quadrature.

Scaling Internet of Things for dementia using Particle filters was published on SAS Users.

11月 022017
 

SAS Visual Investigatorcustom icons and map pin icons in SAS® Visual Investigator has an attractive user interface. One of its most engaging features is the network diagram, which represents related ‘entities’ and the connections between them, allowing an investigator to see and explore relationships in their source data.

For maximum impact, each entity in the network diagram should have an icon – a symbol – which clearly represents the entity. Network diagrams are significantly more readable if the icons used relate to the entities they represent, particularly when a larger numbers of entity types are in the diagram.

SAS Visual Investigator 10.2.1 comes with 56 icons in various colors and 33 white map pins. But if none of these adequately represent the entities you are working with, you can add your own. The catch is that the icons have to be vector images – defined in terms of straight and curved lines – in a file format called SVG, rather than raster images defined in terms of pixels, as GIF, JPEG, PNG files or similar. The vector format greatly improves how good the icons look at different scales. But requiring a vector icon file means you can’t just use any image as a network diagram symbol in Visual Investigator.

So where can you find new icons to better represent your entities? In this post, we’ll see how you can make (or more accurately, find and set the colors of) icons to suit your needs.

For this example, I’m going to suppose that we have an entity called ‘Family,’ representing a family group who are associated with one or more ‘Addresses’ and with one or more individual ‘Persons.’

There are good icons for an ‘Address’ in the default set of 56 provided: I’d choose one of the building icons, probably the house. There are also good icons for a ‘Person’: any of several icons representing people would be fine. The ‘house’ and ‘people’ icons come in a nice variety of colors, and there are white-on-some-background-color versions of each icon for use in Map Pins.

But, there is no icon in the default set which obviously represents the idea of a ‘family.’ So, let’s make one.

We will use the free* library of icons at www.flaticon.com as an example of how to find a source icon image.

*NOTE: While these are free there are some license terms and conditions you must adhere to if you use them. Please read, but the main points to note are that you must give attribution to the designer and site in a specific and short format for the icons you use, and you’re not allowed to redistribute them.

Begin by browsing to https://www.flaticon.com/, and either just browse the icons, or search for a key word which has something to do with the entity for which you need an icon. I’m going to search for ‘Family’:

Notice that many icons have a smaller symbol beneath them, indicating whether they are a ‘premium’ or ‘selection’ icon:

Premium:

Selection:

Premium icons can only be downloaded if you subscribe to the site. Selection icons can be downloaded and used without any subscription so long as you give proper attribution. If you do subscribe, however, you are allowed to use them without attribution. Icons with no symbol beneath them are also free, but always have to be properly attributed, regardless of whether you subscribe or not.

If you are planning on using icons from this site in an image which may be shared with customers, or on a customer project please be sure to comply with company policies and guidelines on using third party content offered under a Creative Commons license. Please do your own research and adhere by their rules.

The icons here are shown at a larger size than they will be in SAS Visual Investigator. For best results, pick an icon with few small details, so that it will still be clear at less than half the size. For consistency with the other icons in your network diagrams and maps, pick a simple black-and-white icon. Several icons in this screenshot are suitable, but I will choose this one named ‘Family silhouette,’ which I think will be clear and recognizable when shrunk:

Icons made by Freepik from www.flaticon.com is licensed by CC 3.0 BY

When you hover over the icon, you’ll see a graphic overlaid on top of it, like this:

If you are working with a number of icons at once, it can be convenient to click the top half of that graphic, to add the icon to a collection. When you have assembled the icons you require, you can then edit their colors together, and download them together. Since I am just working with one icon, I clicked the bottom half of the overlay graphic, to simply view the icon’s details. This is what you see next:

Click the green SVG button to choose a color for your icon – a box with preset colors swings down below the buttons. This offers 7 default colors, which are all okay (white is perfect for map pin icons). But you may prefer to match the colors of existing icons in SAS Visual Investigator – to set your own color, click the multicolor button (highlighted in a red box below):

These are the hex color codes for icons and map pin backgrounds in SAS Visual Investigator 10.2.1. Enter one of these codes in the website’s color picker to match the new icon’s color to one already used in VI:

I chose the green in the center of this table, with hex color code #4d9954:

Then, click Download, and in the popup, copy the link below the button which will allow you to credit the author, then click ‘Free download’ (you must credit the author):

Your SVG icon file is downloaded in your browser. Paste the attribution text/HTML which credits the icon’s author in a text editor.

You must ensure that the files are named so that they contain only the characters A-Z, a-z, 0-9 and ‘_’ (underscore). SAS Visual Investigator doesn’t like SVG filenames containing other characters. In this example, we MUST rename the files to replace the ‘-‘ (dash or hyphen) with something else, e.g. an underscore.

You may also want to rename the SVG file to reflect the color you chose. So, making both these changes, I renamed my file from ‘family-silhouette.svg’ to ‘family_silhouette_green.svg’.

Adding the color to the file name is a good idea when you create multiple copies of the same icon, in different colors. You should also consider creating a white version of each icon for use as a map pin. Doing that, I saved another copy of the same icon in white, and renamed the downloaded file to ‘family_silhouette_white.svg’.

Then, if necessary, copy your SVG file(s) from your local PC to a machine from which you can access your copy of SAS Visual Investigator. To import the icons, open the SAS Visual Investigator: Administration app, and log in as a VI administrator.

When creating or editing an Entity, switch to the Views tab, and click on the Manage icons and map pins button:

In the Manage Icons and Map Pins popup, click Upload…:

Select all of the SVG files you want to upload:

The new icons are uploaded, and if you scroll through the list of icons and map pins you should be able to find them. Change the Type for any white icons you uploaded to Map Pin, and click OK to save your changes to the set of icons and map pins:

You can now use your new icons and map pins for an Entity:

Don’t forget to include a reference to the source for the icon in any materials you produce which accompany the image, if you share it with anyone else. Here’s mine:

Icons made by Freepik from http://www.freepik.com/ is licensed by CC 3.0 BY

See you next time!

Creating and uploading custom icons and map pin icons in SAS® Visual Investigator was published on SAS Users.

11月 022017
 

The purpose of this blog post is to demonstrate a SAS coding technique that allows for calculations with multiple variables across a SAS dataset, whether or not their values belong to the same or different observations.

calculations across observations of a SAS data table

What do we want?

As illustrated in the picture on the right, we want to be able to hop, or jump, back and forth, up and down across observations of a data table in order to implement calculations not just on different variables, but with their values from different observations of a data table.

In essence, we want to access SAS dataset variable values similar to accessing elements of a matrix (aij), where rows represent dataset observations, and columns represent dataset variables.

Combine and Conquer

In the spirit of my earlier post Combine and Conquer with SAS, this technique combines the functionality of the LAG function, which allows us to retrieve the variable value of a previous observation from a queue, with an imaginary, non-existent in SAS, LEAD function that reads a subsequent observation in a data set while processing the current observation during the same iteration of the DATA step.

LAG function

LAG<n> function in SAS is not your usual, ordinary function. While it provides a mechanism of retrieving previous observations in a data table, it does not work “on demand” to arbitrarily read a variable value from a previous observation n steps back. If you want to use it conditionally in some observations of a data step, you still need to call it in every iteration of that data step. That is because it retrieves values from a queue that is built sequentially for each invocation of the LAG<n> function. In essence, in order to use the LAG function even just once in a data step, you need to call it every time in each data step iteration until that single use.

Moreover, if you need to use each of the LAG1, LAG2, . . . LAGn functions just once, in order to build these queues, you have to call each of them in every data step iteration even if you are going to use them in some subsequent iterations.

LEAD function

The LEAD function is implemented in Oracle SQL and it returns data from the next or subsequent row of a data table. It allows you to query more than one row in a table at a time without having to join the table to itself.

There is no such function in SAS. However, the POINT= option of the SET statement in a SAS data step allows retrieving any observation by its number from a data set using random (direct) access to read a SAS data set. This will allow us to simulate a LEAD function in SAS.

HOP function

But why do we need two separate functions like LAG and LEAD in order to retrieve non-current observations. In essence, these two functions do the same thing, just in opposite directions. Why can’t we get by with just one function that does both backwards and forwards “hopping?”

Let’s combine and conquer.

Ideally, we would like to construct a new single function - let’s call it HOP(x, j) - that combines the best qualities of both LAG and LEAD functions. The two arguments of the HOP function would be as follows:

x – SAS variable name (numeric or character) the value of we are retrieving;

j – hop distance (numeric) – an offset from the current observation; negative values being lagging (hopping back), positive values being leading (hopping forward), and a zero-value meaning staying within the current observation.

The sign of the second argument defines whether we lag (minus) or lead (plus). The absolute value of this second argument defines how far from the current observation we hop.

Alternatively, we could have the first argument as a column number, and the second argument as a row/observation number, if we wanted this function to deal with the data table more like with a matrix. But relatively speaking, the method doesn’t really matter as long as we can unambiguously identify a data element or a cell. To stay within the data step paradigm, we will stick with the variable name and offset from the current observation (_n_) as arguments.

Let’s say we have a data table SAMPLE, where for each event FAIL_FLAG=1 we want to calculate DELTA as the difference between DATE_OUT, one observation after the event, and DATE_IN, two observations before the event:

Calculations across observations of a SAS data table

That is, we want to calculate DELTA in the observation where FAIL_FLAG = 1 as

26MAR2017 18JAN2017 = 67 (as shown in light-blue highlighting in the above figure).

With the HOP() function, that calculation in the data step would look like this:

data SAMPLE;
   set SAMPLE;
   if FAIL_FLAG then DELTA = hop(DATE_OUT,1) - hop(DATE_IN,-2);
run;

It would be reasonable to suggest that the hop() function should return a missing value when the second argument produces an observation number outside of the dataset boundary, that is when

_n_ + j < 0 or _n_ + j > num, where _n_ is the current observation number of the data step iteration; num is the number of observations in the dataset; j is the offset argument value.

Do you see anything wrong with this solution? I don’t. Except that the HOP function exists only in my imagination. Hopefully, it will be implemented soon if enough SAS users ask for it. But until then, we can use its surrogate in the form of a %HOP macro.

%HOP macro

The HOP macro grabs the value of a specified variable in an offset observation relative to the current observation and assigns it to another variable. It is used within a SAS data step, but it cannot be used in an expression; each invocation of the HOP macro can only grab one value of a variable across observations and assign it to another variable within the current observation.

If you need to build an expression to do calculations with several variables from various observations, you would need to first retrieve all those values by invoking the %hop macro as many times as the number of the values involved in the expression.

Here is the syntax of the %HOP macro, which has four required parameters:

%HOP(d,x,y,j)

d – input data table name;

x – source variable name;

y – target variable name;

j – integer offset relative to the current observation. As before, a negative value means a previous observation, a positive value means a subsequent observation, and zero means the current observation.

Using this %HOP macro we can rewrite our code for calculating DELTA as follows:

 data SAMPLE (drop=TEMP1 TEMP2);
   set SAMPLE;
   if FAIL_FLAG then
   do;
      %hop(SAMPLE,DATE_OUT,TEMP1, 1)
      %hop(SAMPLE,DATE_IN, TEMP2,-2)
      DELTA = TEMP1 - TEMP2;
   end;
run;

Note that we should not have temporary variables TEMP1 and TEMP2 listed in a RETAIN statement, as this could mess up our calculations if the j-offset throws an observation number out of the dataset boundary.

Also, the input data table name (d parameter value) is the one that is specified in the SET statement, which may or may not be the same as the name specified in the DATA statement.

In case you are wondering where you can download the %HOP macro from, here it is in its entirety:

%macro hop(d,x,y,j);
   _p_ = _n_ + &j;
   if (1 le _p_ le _o_) then set &d(keep=&x rename=(&x=&y)) point=_p_ nobs=_o_;
%mend hop;

Of course, it is “free of charge” and “as is” for your unlimited use.

Your turn

Please provide your feedback and share possible use cases for the HOP function/macro in the Comment section below. This is your chance for your voice to be heard!

Hopping for the best - calculations across SAS dataset observations was published on SAS Users.

11月 022017
 

Managing SAS Configuration Directory SecurityNeed to grant one or more users access to part of your secure SAS configuration directory? You can do it without opening up your SAS configuration directory to everyone.

Most SAS 9.4 Platform deployments on Unix have been done using the SAS Installer account known as sas. The sas account is the owner of the SAS configuration directory. Along with the sas account comes a sas group that out of the box is given generous access to the SAS configuration.

SAS Configuration Directory

The SAS configuration not only includes scripts like sas.servers but it also includes configuration files and logs. It generally includes a lot of control over the SAS environment. Despite locked down security of the SAS configuration on Unix out of the box, there are still valid situations when you need to grant one or more users access to part of the SAS configuration. For example, you might need to enable logging for the workspace server and need to grant write access to the workspace server Logs directory. Or maybe you’re setting up an account to be used for autoloading data into the Public LASR Server. There are many more such examples where you might need to grant one or more users access to part of the SAS configuration.

The sas Group

How do you grant someone access to part of the SAS configuration directory? Why not add the user in question to the sas group? While this may grant your user the access you want, it also introduces the potential for a lot of problems. Keep in mind that the sas group has (and needs) broad access to the SAS configuration. When adding a user to the sas group you are granting that user the same access as the sas group. If the user is administering your SAS environment, that might be okay. If that user is not going to administer your SAS environment, you’ve opened the door for someone to modify or delete any and all of your SAS configuration.

So, what should you do? The short answer is that you should only grant the access needed to the users who need it.

Modifying Security for Workspace Server Logging

Let’s look at the example of enabling the workspace server to produce logs. This is typically done if you need to collect logs for troubleshooting. By default, the workspace server runs under each individual user’s credentials; therefore, each workspace server user would need to be given access to create logs under the workspace server Logs directory. By default, the sas user and sas group are given read, write and execute permission on the workspace server Logs directory. All other users have no access to the workspace server Logs directory. This is a situation where granting all other users read, write and execute access while you need to generate workspace server logs is the recommendation.

Be aware that if logging is enabled for the workspace server, any user who does not have read, write and execute access to the workspace server’s Logs directory will not be able to launch a workspace server session.

The complete steps for enabling workspace server logging can be found in the SAS 9.4 Intelligence Platform: System Administration Guide.

Modifying Security for Autoload

In SAS Visual Analytics, the autoload feature allows periodic synchronization between files on disk and data in memory in a LASR server. The autoload feature for the Public LASR Server is mostly configured out of the box; however, there are a few steps required in order for autoload to be fully enabled. The final step is to schedule the job that will perform the autoload.

By default, the autoload directory for the Public LASR Server is in the SAS configuration directory. It is owned by the sas user and the sas group. The first step in the documentation for how to start autoload is to identify which account will be used to schedule the autoload script. The account you use needs access to both metadata and to the autoload configuration directory. The sas account has access to the autoload configuration directory by default but is not registered in metadata. The ideal answer is to find a balance between overloading an account like sas and not overcomplicating your environment. You could register the sas account in metadata but that would not be my preference. You could also provide metadata server connection information in the autoload script but storing a user id and password in a file is less than ideal. A better solution is the one presented in the Visual Analytics 7.3 – Recommended Post-Install Activities: create an account for the purpose of running autoload, for example, lasradm. The account needs to exist on the operating system (or other authentication provider being used) and in metadata. You would change the ownership of the autoload directory to the lasradm account and to a group other than sas. Creating an operating system group for all of your SAS users is a convenient way to grant permissions or rights to the group of users who will be using SAS. You can create a sasusers group, add lasradm as a member, and make sasusers the group owner of the autoload directory. Now you can schedule the autoload script as lasradm.

SAS Admin Notebook: Managing SAS Configuration Directory Security was published on SAS Users.

10月 272017
 

When loading data into CAS using PROC CASUTIL, you have two choices on how the table can be loaded:  session-scope or global-scope.  This is controlled by the PROMOTE option in the PROC CASUTIL statement.

Session-scope loaded

proc casutil;
                                load casdata="model_table.sas7bdat" incaslib="ryloll" 
                                outcaslib="otcaslib" casout="model_table”;
run;
Global-scope loaded
proc casutil;
                                load casdata="model_table.sas7bdat" incaslib="ryloll" 
                                outcaslib="otcaslib" casout="model_table" promote;
run;

 

Global-scope loaded

proc casutil;
                                load casdata="model_table.sas7bdat" incaslib="ryloll" 
                                outcaslib="otcaslib" casout="model_table" promote;
run;

 

Remember session-scope tables can only be seen by a single CAS session and are dropped from CAS when that session is terminated, while global-scope tables can be seen publicly and will not be dropped when the CAS session is terminated.

But what happens if I want to create a new table for modeling by partitioning an existing table and adding a new partition column? Will the new table be session-scoped or global-scoped? To find out, I have a global-scoped table called MODEL_TABLE that I want to partition based on my response variable Event. I will use PROC PARTITION and call my new table MODEL_TABLE_PARTITIONED.

proc partition data=OTCASLIB.MODEL_TABLE partind samppct=30;
	by Event;
	output out=OTCASLIB.model_table_partitioned;
run;

 

After I created my new table, I executed the following code to determine its scope. Notice that the Promoted Table value is set to No on my new table MODEL_TABLE_PARTITIONED which means it’s session-scoped.

proc casutil;
     list tables incaslib="otcaslib";
run;

 

promote CAS tables from session-scope to global-scope

How can I promote my table to global-scoped?  Because PROC PARTITION doesn’t provide me with an option to promote my table to global-scope, I need to execute the following PROC CASUTIL code to promote my table to global-scope.

proc casutil;
     promote casdata="MODEL_TABLE_PARTITIONED"
     Incaslib="OTCASLIB" Outcaslib="OTCASLIB" CASOUT="MODEL_TABLE_PARTITIONED";
run;

 

I know what you’re thinking.  Why do I have to execute a PROC CASUTIL every time I need my output to be seen publicly in CAS?  That’s not efficient.  There has to be a better way!

Well there is, by using CAS Actions.  Remember, when working with CAS in SAS Viya, SAS PROCs are converted to CAS Actions and CAS Actions are at a more granular level, providing more options and parameters to work with.

How do I figure out what CAS Action syntax was used when I execute a SAS PROC?  Using the PROC PARTITION example from earlier, I can execute the following code after my PROC PARTITION completes to see the CAS Action syntax that was previously executed.

proc cas;
     history;
run;

 

This command will return a lot of output, but if I look for lines that start with the word “action,” I can find the CAS Actions that were executed.  In the output, I can see the following CAS action was executed for PROC PARTITION:

action sampling.stratified / table={name='MODEL_TABLE', caslib='OTCASLIB', groupBy={{name='Event'}}}, samppct=30, partind=true, output={casOut={name='MODEL_TABLE_PARTITIONED', caslib='OTCASLIB', replace=true}, copyVars='ALL'};

 

To partition my MODEL_TABLE using a CAS Action, I would execute the following code.

proc cas;
  sampling.stratified / 
    table={name='MODEL_TABLE', caslib='OTCASLIB', groupBy={name='Event'}}, 
    samppct=30, 
    partind=true, 
    output={casOut={name='embu_partitioned', caslib='OTCASLIB'}, copyVars='ALL'};
run;

 

If I look up sampling.stratified syntax in the

proc cas;
  sampling.stratified / 
    table={name='MODEL_TABLE', caslib='OTCASLIB', groupBy={name='Event'}}, 
    samppct=30, 
    partind=true, 
    output={casOut={name='embu_partitioned', caslib='OTCASLIB', promote=true}, copyVars='ALL'};
run;

 

So, what did we learn from this exercise?  We learned that when we create a table in CAS from a SAS PROC, the default scope will be session and to change the scope to global we would need to promote it through a PROC CASUTIL statement.  We also learned how to see the CAS Actions that were executed by SAS PROCs and how we can write code in CAS Action form to give us more control.

I hope this exercise helps you when working with CAS.

Thanks.

Tip and tricks to promote CAS tables from session-scope to global-scope was published on SAS Users.

10月 262017
 

If you've visited SAS documentation (also known as the "SAS Help Center") lately, you may have noticed that we've made some fairly significant changes in the documentation for SAS products and solutions. The new site is organized in a new way, search works a little differently, and the user interface has changed. These changes are part of our continuous pursuit to provide you with the best possible experience on our website.

Below you'll find a quick summary of what's new. Check out the SAS Help Center and let us know what you think.
(You'll find ways to provide feedback at the end of this post. We'd love to hear from you!)

The SAS Help Center

For starters, SAS documentation has a new location on the web: http://documentation.sas.com and a new name: the “SAS Help Center.” You'll notice the SAS Help Center homepage serves as a gateway to documentation for a number of SAS products and solutions. We've highlighted a few of the major product documentation sets at the top of the page, with a full listing of available documentation immediately following. The user interface contains many of the same features as the documentation you used on support.sas.com, but there are a few little differences. Perhaps the most significant - search works a little differently. More on that in a bit.

Content Organization

SAS documentation is now organized into topic-focused collections. For example, SAS Viya Administration docs are together in another collection. You'll find collections for a number of different topic areas, with each collection containing all the documentation for that specific topic area. For a list of all topic areas, see the Products Index A – Z .

Searching the SAS Help Center

When you use search in the new SAS Help Center, be aware that you're only searching the specific documentation collection that you are using at the time. For example, if you're inside the SAS Viya 3.2 Administration documentation and initiate a search, you will only see results for the doc within the SAS Viya 3.2 Administration collection. If you prefer to search all doc collections at once, you can use the search on support.sas.com or use a third-party search tool, such as Google or Bing. (For tips and guidelines on using search, visit our “yourturn@sas.com.

SAS Help Center: your gateway to documentation was published on SAS Users.

10月 212017
 

Advantages of SAS ViyaThere are many compelling reasons existing SAS users might want to start integrating SAS Viya into their SAS9 programs and applications.  For me, it comes down to ease-of-use, speed, and faster time-to-value.  With the ability to traverse the (necessarily iterative) analytics lifecycle faster than before, we are now able to generate output quicker – better supporting vital decision-making in a reduced timeframe.   In addition to the positive impacts this can have on productivity, it can also change the way we look at current business challenges and how we design possible solutions.

Earlier this year I wrote about how SAS Viya provides a robust analytics environment to handle all of your big data processing needs.  Since then, I’ve been involved in testing the new SAS Viya 3.3 software that will be released near the end of 2017 and found some additional advantages I think warrant attention.  In this article, I rank order the main advantages of SAS Viya processing and new capabilities coming to SAS Viya 3.3 products.  While the new SAS Viya feature list is too long to list everything individually, I’ve put together the top reasons why you might want to start taking advantage of SAS Viya capabilities of the SAS platform.

1.     Multi-threaded everything, including the venerable DATA-step

In SAS Viya, everything that can run multi-threaded - does.  This is the single-most important aspect of the SAS Viya architecture for existing SAS customers.  As part of this new holistic approach to data processing, SAS has enabled the highly flexible DATA step to run multi-threaded, requiring very little modification of code in order to begin taking advantage of this significant new capability (more on that in soon-to-be-released blog).  Migrating to SAS Viya is important especially in those cases where long-running jobs consist of very long DATA steps that act as processing bottle-necks where constraints exist because of older single-threading configurations.

2.     No sorting necessary!

While not 100% true, most sort routines can be removed from your existing SAS programs.  Ask yourself the question: “What portion of my runtimes are due strictly to sorting?”  The answer is likely around 10-25%, maybe more.  In general, the concept of sorting goes away with in-memory processing.  SAS Viya does its own internal memory shuffling as a replacement.  The SAS Viya CAS engine takes care of partitioning and organizing the data so you don’t have to.  So, take those sorts out your existing code!

3.     VARCHAR informat (plus other “variable-blocking” informats/formats)

Not available in SAS 9.4, the VARCHAR informat/format allows you to store byte information without having to allocate room for blank spaces.  Because storage for columnar (input) values varies by row, you have the potential to achieve an enormous amount of (blank space) savings, which is especially important if you are using expensive (fast) disk storage space.  This represents a huge value in terms of potential data storage size reduction.

4.     Reduced I/O in the form of data reads and writes from Hive/HDFS and Teradata to CAS memory

SAS Viya can leverage Hive/HDFS and Teradata platforms by loading (lifting) data up and writing data back down in parallel using CAS pooled memory.  Data I/O, namely reading data from disk and converting it into a SAS binary format needed for processing, is the single most limiting factor of SAS 9.4.  Once you speed up your data loading, especially for extremely large data sets, you will be able to generate faster time to results for all analyses and projects.

5.     Persisted data can stay in memory to support multiple users or processing steps

Similar to SAS LASR, CAS can be structured to persist large data sets in memory, indefinitely.  This allows users to access the same data at the same time and eliminates redundancy and repetitive I/O, potentially saving valuable compute cycles.  Essentially, you can load the data once and then as many people (or processing steps) can reuse it as many times as needed thereafter.

6.     State-of-the-art Machine Learning (ML) techniques (including Gradient Boosting, Random Forest, Support Vector Machines, Factorization Machines, Deep Learning and NLP analytics)

All the most popular ML techniques are represented giving you the flexibility to customize model tournaments to include those techniques most appropriate for your given data and problem set.  We also provide assessment capabilities, thus saving you valuable time to get the types of information you need to make valid model comparisons (like ROC charts, lift charts, etc.) and pick your champion models.  We do not have extreme Gradient Boosting, Factorization Machines, or a specific Assessment procedure in SAS 9.4.  Also, GPU processing is supported in SAS Viya 3.3, for Deep Neural Networks and Convolutional Neural Networks (this has not be available previously).

7.     In-memory TRANSPOSE

The task of transposing data amounts to about 80% of any model building exercise, since predictive analytics requires a specialized data set called a ‘one-row-per-subject’ Analytic Base Table (ABT).  SAS Viya allows you transpose in a fraction of the time that it used to take to develop the critical ABT outputs.  A phenomenal time-saver procedure that now runs entirely multi-threaded, in-memory.

8.     API’s!!!

The ability to code from external interfaces gives coders the flexibility they need in today’s fast-moving programming world.  SAS Viya supports native language bindings for Lua, Java, Python and R.  This means, for example, that you can launch SAS processes from a Jupyter Notebook while staying within a Python coding environment.  SAS also provide a REST API for use in data science and IT departments.

9.     Improved model build and deployment options

The core of SAS  Viya machine learning techniques support auto-tuning.  SAS has the most effective hyper-parameter search and optimization routines, allowing data scientists to arrive at the correct algorithm settings with higher probability and speed, giving them better answers with less effort.  And because ML scoring code output is significantly more complex, SAS Viya Data Mining and Machine Learning allows you to deploy compact binary score files (called Astore files) into databases to help facilitate scoring.  These binary files do not require compilation and can be pushed to ESP-supported edge analytics.  Additionally, training within  event streams is being examined for a future release.

10.    Tons of new SAS visual interface advantages

A.     Less coding – SAS Viya acts as a code generator, producing batch code for repeatability and score code for easier deployment.  Both batch code and score code can be produced in a variety of formats, including SAS, Java, and Python.

B.     Improved data integration between SAS Viya visual analytics products – you can now edit your data in-memory and pass it effortlessly through to reporting, modeling, text, and forecasting applications (new tabs in a single application interface).

C.     Ability to compare modeling pipelines – now data scientists can compare champion models from any number of pipelines (think of SAS9 EM projects or data flows) they’ve created.

D.     Best practices and white box templates – once only available as part of SAS 9 Rapid Predictive Modeler, Model Studio now gives you easy access to basic, intermediate and advanced model templates.

E.     Reusable components – Users can save their best work (including pipelines and individual nodes) and share it with others.  Collaborating is easier than ever.

11.    Data flexibility

You can load big data without having all that data fit into memory.  Before in HPA or LASR engines, the memory environment had to be sized exactly to fit all the data.  That prior requirement has been removed using CAS technology – a really nice feature.

12.    Overall consolidation and consistency

SAS Viya seeks to standardize on common algorithms and techniques provided within every analytic technique so that you don’t get different answers when attempting to do things using alternate procedures or methods. For instance, our deployment of Stochastic Gradient Descent is now the same in every technique that uses that method.  Consistency also applies to the interfaces, as SAS Viya attempts to standardize the look-and-feel of various interfaces to reduce your learning curve when using a new capability.

The net result of these Top 12 advantages is that you have access to state-of-the-art technology, jobs finish faster, and you ultimately get faster time-to-value.  While this idea has been articulated in some of the above points, it is important to re-emphasize because SAS Viya benefits, when added together, result in higher throughputs of work, a greater flexibility in terms of options, and the ability to keep running when other systems would have failed.  You just have a much greater efficiency/productivity level when using SAS Viya as compared to before.  So why not use it?

Learn more about SAS Viya.
Tutorial Library: An introduction to SAS Viya programming for SAS 9 programmers.
Blog: Adding SAS Viya to your SAS 9 programming toolbox.

Top 12 Advantages of SAS Viya was published on SAS Users.

10月 212017
 

using the IMPORT procedure to read files that contain delimitersReading an external file that contains delimiters (commas, tabs, or other characters such as a pipe character or an exclamation point) is easy when you use the IMPORT procedure. It's easy in that variable names are on row 1, the data starts on row 2, and the first 20 rows are a good sample of your data. Unfortunately, most delimited files are not created with those restrictions in mind.  So how do you read files that do not follow those restrictions?

You can still use PROC IMPORT to read the comma-, tab-, or otherwise-delimited files. However, depending on the circumstances, you might have to add the GUESSINGROWS= statement to PROC IMPORT or you might need to pre-process the delimited file before you use PROC IMPORT.

Note: PROC IMPORT is available only for use in the Microsoft Windows, UNIX, or Linux operating environments.

The following sections explain four different scenarios for using PROC IMPORT to read files that contain the delimiters that are listed above.

Scenario 1

In this scenario, I use PROC IMPORT to read a comma-delimited file that has variable names on row 1 and data starting on row 2, as shown below:

proc import datafile='c:\temp\classdata.csv' 
out=class dbms=csv replace;
run;

 

When I submit this code, the following message appears in my SAS® log:

NOTE: Invalid data for Age in line 28 9-10.
RULE:     ----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+---
28        Janet,F,NA,62.5,112.5 21
Name=Janet Sex=F Age=. Height=62.5 Weight=112.5 _ERROR_=1 _N_=27
NOTE: 38 records were read from the infile 'c:\temp\classdata.csv'.
      The minimum record length was 17.
      The maximum record length was 21.
NOTE: The data set WORK.CLASS has 38 observations and 5 variables.

 

In this situation, how do you prevent the Invalid Data message in the SAS log?

By default, SAS scans the first 20 rows to determine variable attributes (type and length) when it reads a comma-, tab-, or otherwise-delimited file.  Beginning in SAS® 9.1, a new statement (GUESSINGROWS=) is available in PROC IMPORT that enables you to tell SAS how many rows you want it to scan in order to determine variable attributes. In SAS 9.1 and SAS® 9.2, the GUESSINGROWS= value can range from 1 to 32767.  Beginning in SAS® 9.3, the GUESSINGROWS= value can range from 1 to 2147483647.  Keep in mind that the more rows you scan, the longer it takes for the PROC IMPORT to run.

The following program illustrates the use of the GUESSINGROWS= statement in PROC IMPORT:

proc import datafile='c:\temp\classdata.csv' out=class              dbms=csv replace;
guessingrows=100;
run;

 

The example above includes the statement GUESSINGROWS=100, which instructs SAS to scan the first 100 rows of the external file for variable attributes. You might need to increase the GUESSINGROWS= value to something greater than 100 to obtain the results that you want.

Scenario 2

In this scenario, my delimited file has the variable names on row 4 and the data starts on row 5. When you use PROC IMPORT, you can specify the record number at which SAS should begin reading.  Although you can specify which record to start with in PROC IMPORT, you cannot extract the variable names from any other row except the first row of an external file that is comma-, tab-, or an otherwise-delimited.

Then how do you program PROC IMPORT so that it begins reading from a specified row?

To do that, you need to allow SAS to assign the variable names in the form VARx (where x is a sequential number). The following code illustrates how you can skip the first rows of data and start reading from row 4 by allowing SAS to assign the variable names:

proc import datafile='c:\temp\class.csv' out=class dbms=csv replace;
getnames=no;
datarow=4;
run;

 

Scenario 3

In this scenario, I want to read only records 6–15 (inclusive) in the delimited file. So the question here is how can you set PROC IMPORT to read just a section of a delimited file?

To do that, you need to use the OBS= option before you execute PROC IMPORT and use the DATAROW= option within PROC IMPORT.

The following example reads the middle ten rows of a CSV file, starting at row 6:

options obs=15; 
 
proc import out=work.test2  
            datafile= "c:\temp\class.csv" 
            dbms=csv replace; 
            getnames=yes; 
            datarow=6; 
run; 
 
options obs=max; 
run;

 

Notice that I reset the OBS= option to MAX after the IMPORT procedure to ensure that any code that I run after the procedure processes all observations.

Scenario 4

In this scenario, I again use PROC IMPORT to read my external file. However, I receive more observations in my SAS data set than there are data rows in my delimited file. The external file looks fine when it is opened with Microsoft Excel. However, when I use Microsoft Windows Notepad or TextPad to view some records, my data spans multiple rows for values that are enclosed in quotation marks.  Here is a snapshot of what the file looks like in both Microsoft Excel and TextPad, respectively:

The question for this scenario is how can I use PROC IMPORT to read this data so that the observations in my SAS data set match the number of rows in my delimited file?

In this case, the external file contains embedded carriage return (CR) and line feed (LF) characters in the middle of the data value within a quoted string. The CRLF is an end-of-record marker, so the remaining text in the string becomes the next record. Here are the results from reading the CSV file that is illustrated in the Excel and TextPad files that are shown earlier:

That behavior is why you receive more observations than you expect.  Anytime SAS encounters a CRLF, SAS considers that a new record regardless of where it is found.

A sample program that removes a CRLF character (as long as it is part of a quoted text string) is available in SAS Note 26065, "Remove carriage return and line feed characters within quoted strings."

After you run the code (from the Full Code tab) in SAS Note 26065 to pre-process the external file and remove the erroneous CR/LF characters, you should be able to use PROC IMPORT to read the external file with no problems.

For more information about PROC IMPORT, see "Chapter 35, The IMPORT Procedure" in the Base SAS® 9.4 Procedures Guide, Seventh Edition.

 

 

Tips for using the IMPORT procedure to read files that contain delimiters was published on SAS Users.

10月 202017
 

 

“The difference between style and fashion is quality.”

-Giorgio Armani

With an out-of-the-box SAS Enterprise Guide (EG) installation, when you build a report in SAS EG it is displayed in a nice-looking default style. If you like it, you can keep it, and continue reading. If you don’t quite like it, then stop, take a deep breath, and continue reading carefully – you are about to discover a wealth of styling options available in EG. In any case, you are not bound by the default style that is set during your SAS EG installation.

Changing your SAS EG report style on the fly

Let’s say we run the following SAS program in EG:

SAS code sample to run in SAS EG
When you run a SAS Program or a Process Flow that creates an output, it will open in the Results tab shown in a default style (HtmlBlue). For many it looks quite OK. However, SAS provides many other different styles you can choose from. To change your report style, just click on Properties of the workspace toolbar:
Results tab in SAS EG
This will open the Properties for SAS Report window where you can select any style in the Style drop-down list:

Properties for SAS Report
After you selected desired style, click OK; this will save your change and close the window. Your report will immediately be redrawn and displayed in your new style:

SAS report in new style

That new style will only apply to the Results element of the SAS Program or a Process Flow you ran. If you save the EG project and then re-open it in a new EG session and re-run, EG will still remember and use the style you previously selected for the Results element. All other elements will use the default style.

But how do you know what each style’s look and feel is before you select it? The following sections show how to browse different styles as well as how to change your default styles to new ones.

Browsing SAS EG styles

SAS Enterprise Guide interface provides a quick access to viewing different styles, whether built-in or external. Here is how to get an idea of different styles look and feel.

From the EG main menu click on Tools → Style Manager. In the opened Style Manager window you may browse through the Style List by clicking on each style listed in the left pane and also get a good idea of how each particular style looks by viewing it in the right Preview pane of the Style Manager window:

Style Manager window

From Style Manager window, you can also set up a default style by selecting a style you like from the Style List in the left pane and clicking Set as Default button. However, setting your default style using Style Manager will only affect SAS Report and HTML results formats. But what about other results formats? Not to worry, SAS EG interface has you covered.

Changing your default SAS EG report style

If you like a particular report style and don’t want to be stuck with a pre-set default style and necessity to change it every time you run a report, you may easily change your SAS EG default style for practically any results format.

From the EG main menu click on Tools → Options…, the Options window will open. I that window, under Results → Results General you may select (check) one or multiple Results Formats (SAS Report, HTML, PDF, RTF, Text Output, PowerPoint, and Excel) as well as choose your Default Result Format:

Options window to choose default report format

Let’s set up default styles for different results formats. First, let’s go to Results → SAS Report, you will see (under Appearance → Style) that your default style (set up during initial EG installation) is HtmlBlue. Click on the Style drop-down list to select a different style:

Select new report style

The same way you may set up default styles for other results formats (HTML, RTF, PDF, Excel, and PowerPoint). For Graph, you may select a Graph Format (ActiveX, Java, GIF, JPEG, etc.) When you are done, click OK button, the Options window will close and your selected styles become your new default. They are going to persist across EG sessions.

If you are a SAS Administrator, to ensure consistency across your organization, you may have all your SAS Enterprise Guide users set up the same Default styles for every Result format.

Server-side style templates

Server-side SAS style templates are created using the PROC TEMPLATE of the SAS Output Delivery System (ODS) and are stored in Template Stores within SAS libraries. By definition, a template store is an item store that stores items that were created by the TEMPLATE procedure. In particular, built-in server-side SAS style templates are stored in the SASHELP.TMPLMST item store.

Note, that you will not see these item stores / template stores in the EG Server→Library tree under the SASHELP library as it only shows data tables and views. While there is no access in EG to the Templates Window, you can access the Templates Window from SAS Display Manager.

In Enterprise Guide, in order to view a list of built-in server-side SAS styles in the SASHELP.TMPLMST item store, you may run the following code:

proc template;
   path sashelp.tmplmst;
   list styles;
run;

This will produce the following listing shown in the EG’s Results tab:

Report listing

If you want to view all the server-side styles including built-in and user-defined, you can do that in EG by running the following code:

proc template;
   list styles;
run;

Server-side templates are applied to ALL Results Formats.

CSS styles

Cascading Style Sheet (CSS) styles are available only for SAS Report and HTML result formats. The CSS stylesheet only styles the browser-rendered elements. It will not change a graph image style that is generated on the server.

In the SAS code generated by EG, CSS style is specified in STYLESHEET= option of the ODS statement. It can point to any local or network accessible CSS file, for example:

STYLESHEET=(URL="file:///C:/Program%20Files/SASHome/SASEnterpriseGuide/7.1/Styles/HTMLBlue.css")

In addition, STYLESHEET= option can point to a .css file located on the Internet, for example:

STYLESHEET=(URL="https://www.sas.com/etc/designs/saswww/static.css")

Server-side styles vs. CSS styles

With SAS Enterprise Guide you create Projects, Process Flows, Programs, Tasks, Reports, etc. on your local Window machine. When you Run your Project (or any part of it), EG generates SAS code which gets sent to and executed on the SAS server, and then any visual results are sent back to EG and displayed there.

For every Result Format, a server-side style template is always applied when SAS output is generated on the SAS server.

When that SAS output is returned to SAS EG, for SAS Report and HTML result formats only, an additional optional styling is applied in a form of CSS styles that controls what your SAS Report or HTML output looks like. This CSS styling affects only HTML elements of the output and do not affect graph images that are always generated and styled on the server.

These two kinds of styles are reflected in the EG-generated SAS code that gets shipped to SAS server for execution. If you look at the Code Preview area (Program → Export → Export Program) or Log tab, you will always see ODS statement with STYLE= option that specifies the server-side style. If your selected Result Format is either SAS Report or HTML, then in addition to STYLE= option the ODS statement also contains STYLESHEET= option that specifies HTML CSS stylesheet (external file) accessible via the client.

If you select as default a built-in style (e.g. Harvest) EG will find both server version of it and CSS version of it; you will see this in the SAS log:

STYLE=Harvest
STYLESHEET=(URL="file:///C:/Program%20Files/SASHome/SASEnterpriseGuide/7.1/Styles/Harvest.css")

However, if you select as default some custom CSS or external CSS style (e.g. ABC) that does not have a match in the server template store, the server style will be set to the default server-side style HTMLBlue; you will see in the SAS log the following WARNING:

WARNING: Style ABC not found; Default style will be used instead.

This warning relates to the STYLE= option specifying the server-side style.

Adding your custom SAS EG report style

Even though SAS supplies dozens of styles for you to choose from (Built-in Styles), you can still modify existing styles and create your own custom styles for SAS Report and HTML output types only. You can do this via Style Manager.

Open Style Manager with either one of the following ways:

Tools → Style Manager

Tools → Options… → Results/SAS Reports → Manage Styles

Tools → Options… → Results/HTML → Manage Styles

Note, that style customization via Style Manager is only available for SAS Report and HTML output types.

In the left pane of the Style Manager there are 3 columns:

  1. Style representing style name;
  2. Location indicating whether it is Built-in Style (SAS-supplied CSS), My Style (your custom CSS), or External Style (any CSS - Cascading Style Sheet - on your local machine or on the Web; or a style template on a SAS server);
  3. URL showing the location of the CSS file.

Find a style in the left pane list you wish to modify. Notice that SAS-supplied built-in styles are not editable (Edit button is grayed out). First, make a copy of this style by pressing Create a Copy button. You can also make a copy of a style by right-clicking on it and selecting Create a Copy from the pop-up menu.  This will open Save Style As window where you can give it a name and select a Save in location.

Your new style appears in the Style List of the Style Manager. Click on the new style name and then press Edit button (alternatively, you may right-click on the new style name and select Edit from the pop-up menu):

Style Editor window

This will open the Style Editor window where you can modify text and border attributes, specify background and banner images, as well as assign any custom CSS property name / property value pairs.

Click OK button when you are done to return to the Style Manager. There you may even set your custom style as default, by selecting it first and then pressing the Set as Default button.

Besides editing your new style in Style Manager, you may also open your-new-style.css file in a Text Editor and edit CSS there.

Adding an external style to Enterprise Guide

You can add external styles to your Style List in the Style Manager. While in the Style Manager, click on Add button, this will open the Add New Style window:

Adding an external style

Make sure Add new external style radio box is selected. Type in a Style name for your external style and Style URL, which can be a folder/directory path name on your local machine or your network (e.g. C:\your_folder\your_css_file_name.css) or a location on the Web (e.g. http://www.some_domain.com/styles/your_special_style.css).

To make your custom styles available to all SAS EG users in your organization, you may create them as a SAS style template using PROC TEMPLATE and place on a SAS server (server-side style), see this SAS Code Sample.  In this case, you can add your custom style to the Style Manager by selecting This is a SAS server style only check box in the above Add New Style window. The Style URL field will become disabled, as it is only used to specify CSS stylesheet:

Checking This is a SAS server style only checkbox

You would select this checkbox if you only want to use server-side style (the STYLE= option is always present) and do not want to also provide and apply an optional CSS stylesheet (STYLESHEET=).

Conclusion

In this post I tried to present a comprehensive guide on using styles in SAS Enterprise Guide. Please use the Comments section below to share your experience with Enterprise Guide as it relates to reports styling.

Resources

Little SAS Enterprise Guide bookThe Little SAS Enterpriser Guide Book

Point-and-Click Style Editing in SAS® Enterprise Guide®

I Didn’t  Know SAS®  Enterprise Guide®  Could Do  That!

Creating reports in style with SAS Enterprise Guide was published on SAS Users.