About FalkoSchulz

FalkoSchulz

Are you able to share some details about your input table? When you say your client requires a list of all identified relationships - this would be just a plain distinct list of your source/target columns - similar to your example output table? A similar table structure is shown when you maximize your network analysis object (top right corner -> maximize view). Are you after something like that? You could also export this data if required. Anything more targeted towards real relationship analysis and especially the calculation of network metrics, is unfortunately not something you can easily replicate in any other visual. The heavy lifting to calculate the graph layout and structure is done on the server using SAS Viya's NETWORK actions (https://go.documentation.sas.com/doc/da/pgmsascdc/v_055/casactml/casactml_network_details01.htm). If you are comfortable with SAS Studio - you can certainly achieve something similar by executing the actions manually and adjust related output format as required. The doc link also has a number of examples you could use to get started. Hope this helps! Falko

FalkoSchulz

From record-breaking winds to massive storm surges, tropical cyclones have shaped Australia's history in extreme ways. Using data from the Australian Bureau of Meteorology, I’ve created an infographic highlighting the most destructive storms, common cyclone tracks, and fascinating records. This SAS Visual Analytics report uses a Geo Line object to render the tracks of each storm (top right corner) as well as selected storms in the bottom right geo visualization. User can select years of interest using the left hand bar chart to filter if required. A box plot in the bottom center is used for the top 10 storms given their estimated wind speed. Cyclone Fury: Unraveling Australia's Most Devastating Storms Enjoy!

FalkoSchulz · ‎01-20-2025

Data provided by OECD Affordable Housing Database shows some interesting insights into real house price trends and housing affordability across OECD countries. The infographic below was created using SAS Visual Analytics and visualizes the data using a geographical map (color coded given price index) as well as compares price index trends and price-to-income ratio to measure affordability. Interesting insights noticeable in the data: A decline in affordability leading up to the Global Financial Crisis. Temporary improvement post-crisis, followed by a steady decline from the late 2010s. Impact of rising interest rates on borrowing costs, further affecting affordability. Significant price increases in countries such as Canada, Chile, Israel, and Sweden Some minor price declines in countries such as Greece, Italy, and Spain. Infographic: The Surge in Housing Costs: A Three-Decade Analysis Across OECD Countries You can also view the ultra-high resolution version of the graphic (resolution: 3400x5000, size: 9MB, format: PNG). Enjoy!

FalkoSchulz · ‎01-16-2025

I used a flow container with a text object (with header text) and the following image to the right of it: Not sure how well this is visible here .. but the inner circle is transparent in this image, so it will take on the background color of the flow container (e.g. blue). The outside is colored white and matches the actual report background. Easy!👌

FalkoSchulz · ‎01-05-2025

Only OSM map tile servers are supported at this stage. If you don't want to configure your own tile server - you could also try the preloaded (free) ISO or VM images such as https://live.osgeo.org which come with everything pre-configured. There is some minor configuration required and you will also need to download the actual tiles of the geographical area you interested but this should get you started quickly. Hope this helps. Falko

FalkoSchulz · ‎01-05-2025

Either the underlying data structure is not correct or some of your nodes don't have valid latitude/longitude coordinates. There is more information about the expected data structure in the doc at https://go.documentation.sas.com/doc/en/vacdc/v_029/vaobj/n0kcvb9vm0kd1sn1b199uu8hgq1w.htm#p1omdndy76sxuwn1gg2rts7za5bi - Note, that all leaf nodes also need to be represented in the source (from) column. This is required so you can specify any potential node attributes such as color or size. Hope this helps. Falko

FalkoSchulz · ‎11-18-2024

With the 2024 SAS Hackathon now being complete - I thought about visualizing some of the related statistics using a SAS Visual Analytics report. I used a portrait precision layout configuration for this infographic with individual containers focusing on different statistics. Along with some text analytics to understand general topics of team submissions, it also shows the geographical distribution and number of participants by team and industry track. SAS Hackathon 2024 - Innovating Together Enjoy!

FalkoSchulz · ‎10-17-2024

Great job Team Hore - very well polished videos and great story 👍

FalkoSchulz · ‎10-04-2024

Great work Kalle! Very valuable tutorials for any VA designer 👍

FalkoSchulz · ‎10-02-2024

There are tens of thousands of electric vehicle (EV) charging stations across the United States, strategically installed in key locations for public and workplace use, complementing residential charging. While most EV owners primarily charge their vehicles at home, infrastructure expansion has accelerated in recent years. This SAS Visual Analytics report visualizes the number of EV charging stations per 100,000 people by state as of September 2024. The data is sourced from the U.S. Department of Energy, with population data from the U.S. Census Bureau. Powering the EV Future - The Electrification of America’s Roads With approximately 76,000 stations nationwide, I've aggregated station data at the postal code level, represented by yellow data points. Larger points indicate areas with a higher concentration of chargers, while state boundaries are color-coded based on the number of stations per 100,000 people to illustrate station accessibility. Surprisingly, while California has nearly 50 stations per 100,000 residents, Vermont and Washington, D.C. lead with over 60 stations per 100,000. Nevertheless, California tops the U.S. in total EV chargers with 18,561 stations, making up 24.34% of the national total and serving its population of 38,965,193. New York follows with 4,847 stations (6.35%), while Florida and Texas have 4,001 (5.25%) and 3,964 (5.20%) stations, respectively. Together, these four states play a pivotal role in supporting the growing demand for EV infrastructure. You can also view the ultra-high resolution version of the graphic (resolution: 1347x1797, size: 1.3MB, format: PNG). Enjoy!

FalkoSchulz · ‎09-12-2024

Can you share how your geo data provider structure looks like? If you have more than one polyline - each of these lines would get a unique identifier assigned. Given it's a line - you will need more than two rows representing the lines (minimum a start and an end point). You would repeat this for any line - each with it's own identifier. In your business data (the table used in the report) - would just carry the actual line identifiers and you would use related column when mapping the geo data provider. Assuming - those match successfully - you should be able to see multiple polylines in your geo map. If you can't get it to work - maybe share the first couple of rows representing your lines as well as an extract of your business data. If you see any warnings via the small 'i' icon in the bottom right corner of the geo map - see whether it helps to narrow down the issue. Falko

FalkoSchulz · ‎09-07-2024

Have you ever wondered how infographic segments such as the following were created using SAS Visual Analytics: This visual is used in examples such as The Global Findex Database in SAS Visual Analytics and is a great tool to visualize percentage without the use of key values or textural information. The following illustrates the steps required to create such visualization. Step 1 - Data preparation While you could integrate required data into your business data, for reusability I decided to create a separate data set. The following data step can be executed in SAS Studio and creates a number of different grid sizes, related coordinates and percentages. We will later choose a desired grid size given our business data: cas; caslib _all_ assign; data work.infographic; do grid_counter=3 to 12 by 1; cell_grid=catx(' ', catx('x', put(grid_counter, 8.), put(grid_counter, 8.)), 'Grid'); cell_num=0; do cell_y=grid_counter to 1 by -1; do cell_x=1 to grid_counter by 1; cell_num=cell_num + 1; cell_char='C' || put(cell_num, z2.); cell_pct=cell_num / ceil(grid_counter ** 2); output; end; end; end; format cell_pct percent12.; drop grid_counter; run; proc casutil; droptable casdata="INFOGRAPHIC" incaslib="Public" quiet; load data=work.infographic outcaslib="Public" casout="INFOGRAPHIC" promote; run; Step 2 - Parameter In order for us to drive the current value to be used as percentage, I'm going to use a parameter. For illustration purposes, I will drive the value of this parameter using a slider component. We are now going to use this parameter in a calculated item which determines later whether or not a given icon is highlighted or shown: This will make creating of display rules in the next step easier. Step 3 - Visualization and display rules To render this infographic segment, I'm going to use a Geo Coordinate object. While you could use a standard Scatter Plot as well, it doesn't have the same ability to fine tune display rules. Especially the icon support is very useful in this example. In order to use a geo map, I'm creating a new geo item using the grid x/y coordinates: You can now assign this item to the geographical map. Make sure to turn off the geographical map background as we are just using some artificial geographical coordinates for the grid cells. Given we created multiple grid sizes in the data step, you will now either have to add a filter to select a single grid (column cell_grid) or add a button bar with filter interactions against the geo map. This will make sure, that you only have a single grid rendered at a time. The final step is now to create a display rule to define which icons and colors to use for each data point in the geographical map: Geographical maps allow the assignment of icons as well as allow you to upload your own icon images: Final results Once display rules are in place, you can use the button bar and slider to try out different combination of grid sizes and values: Hope you enjoyed this short tutorial and able to utilize techniques like these in your next infographic! Enjoy!

FalkoSchulz · ‎08-29-2024

I don't think that is possible. Exporting a report package always happens at run-time given a certain user session. It's that session which determines what data is included in the resulting package. If you have row-level security configured for example - the package will only contain data accessible by this user. As per SDK FAQ's - it's "..it is the responsibility of the hosting web server to provide adequate authentication to the contents". Instead of generating offline packages, you may also consider leaving the security aspect to SAS Viya and connect from your Angular website directly to a live SAS Viya system. You can use API methods such as like connectToServer and objects like SASReportElement in your web page directly. This method also gives you full flexibility which visual element you want to include in your web page. Hope this helps. Falko

FalkoSchulz · ‎08-26-2024

Has been a little while since I last worked on an Angular but here are some tips. Install the report components modules if not done already in your project. Use the --save option to update your package.json: npm install @sassoftware/va-report-components --save Make sure to disable the cache for your project - I remember having difficulties serving .js files from the SDK module otherwise. You can switch off the cache in angular.json via: "cli": { "cache": { "enabled": false } }, Best to serve static files via a local assets folder instead of importing modules. This makes referencing the modules in index.html easier. I believe this is related to the lack of configuration options of the underlying vite server. I simply copied all files from node_modules/@sassoftware/va-report-components/dist to the assets folder, e.g. assets/sdk-assets. Extract your report package content also into the assets folder. For instance create a sub folder reports and have all reports hosted there. The same applies here - we want the report package to be hosted statically. Register the assets folder as such static source folder in your angular.json file (projects/architect/build/options): "assets": [ "src/assets" ], Add related references to those scripts in your index.html and don't forget to add the <DIV/> container to the body for the SAS report: <!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8" /> <title>MyAngularProject</title> <base href="/" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <link rel="icon" type="image/x-icon" href="favicon.ico" /> <script src="assets/sdk-assets/dist/umd/va-report-components.js"></script> <link rel="stylesheet" href="assets/sdk-assets/dist/umd/style.css" /> </head> <body> <app-root></app-root>  <div id="sas-report-container"></div> </body> </html> Define the SDK element via CUSTOM_ELEMENTS_SCHEMA since won't compile otherwise (assuming you are using TypeScript?). An example of defining the SASReportPageElement would be the following: import { Component, OnInit, CUSTOM_ELEMENTS_SCHEMA } from '@angular/core'; import { SASReportPageElement } from '@sassoftware/va-report-components'; @Component({ selector: 'app-home', standalone: true, imports: [], schemas: [CUSTOM_ELEMENTS_SCHEMA], templateUrl: './home.component.html', styleUrl: './home.component.css', }) export class HomeComponent implements OnInit { ngOnInit() { // Ensure the custom element is defined if (!customElements.get('sas-report-page')) { customElements.define('sas-report-page', SASReportPageElement); } } } Once done - you can reference the element in your template .html as normal. Adjust the packageUri to point to your report package in the assets folder: <p>home works!</p> <sas-report-page packageUri="../../assets/reports/MyReportPackage" pageName="vi92" style="width: 600px; height: 300px" ></sas-report-page> I'm also seeing some translation error I18N messages in the console in my quick test program here - but this doesn't seem to cause any harm. Assuming some of the non-EN resource packages aren't available. This may need more tweaks for the underlying webpack config. If you see a console message " ../reportPackage.xml 404 not found" - verify the packageUri attribute and make sure that is referencing the correct location of your report package. Hope this helps. Falko

FalkoSchulz · ‎08-18-2024

Unfortunately, this particular graph type is not available as native object in SAS Visual Analytics. You could however look into the generic Data-Driven visualization object which allows you to embed any graph type really. For this, you may want to look into the D3 based version and follow the SAS documentation on how to write such custom graph. There are also tons of existing data-driven content examples if you just want to try out a few before writing your own. Hope this helps! Falko

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