This is the second part of a six part series that describes tips and tricks for building impactful reports in SAS Visual Analytics.   Draft a Plan Choose the Best Chart (this article!) Bonus: Chart Best Practices Focus on What’s Important Consider the Layout Test, Test, and Test Again   SAS Visual Analytics enables you to create compelling, interactive reports that can be viewed by anyone, anywhere. To create impactful reports that resonate with your audience you need to (1) draft a plan for the report, (2) choose the best chart type to display your data, (3) create your reports so viewers can focus on what’s important to them, (4) pick a layout that will best display your data and tell your data story, and (5) test the report to ensure it operates and looks the way you want. In this post, we will focus on the second step in the process: Choose the Best Chart.   When choosing the chart to best display your data and advance your data story, you need to think about the audience and the type of data you want to display (category, measure, date, datetime, time). You can ask yourself the following questions to determine the best chart:    Am I highlighting one important fact? Am I comparing two or more things? Am I showing survey or questionnaire results? Am I describing how parts relate to the whole? Am I showing the relationship between data items? Is a graph even required?   Highlighting One Important Fact   Choosing the best chart type can be determined by the type of data you want to display (category, measure, date, datetime, time) and what you are trying to showcase.   If you want to highlight one important fact, you can use a key value object or dynamic text to highlight a single number.     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 this example, both objects highlight a single number without any unnecessary information detracting from the value.   You can use a donut graph or a faded bar chart to draw the viewer’s attention to a specific value.     In this example, both objects use display rules to highlight important values (Internet Sale in the donut chart and Golf in the bar chart). These objects also show other values in muted colors for comparison.   Pro Tip! Use display rules to highlight important values, but take care to choose colors that are easily distinguished which can be helpful for color blind users. For more information about display rules, see the SAS Visual Analytics Display Rules: Report-Level post.   Comparing Two or More Things   For general comparisons, bar charts can be used to compare multiple pieces of data, dot plots can be used to compare groups with values that are close together, butterfly charts can be used to compare two groups across different categories, and dual axis charts can be used to compare two measures with different ranges. However, be aware that some audience members might not be familiar with dual axis charts and might misinterpret them.     In this example, the stacked bar chart uses the group role to compare quantity ordered for loyalty and non-loyalty members. For the dot plot, a zero baseline is not assumed, so it is useful for comparing values that are close together (like the number of orders by product line). The butterfly chart is useful for comparing two opposing groups (in this case, customer satisfaction for toy and novelty products). The dual axis chart makes it easy to compare two measures (like customer satisfaction and product sale) that have very different ranges. Viewing both these measures on the same axis will flatten the customer satisfaction data and make it difficult to see the relationship between the measures.   Pro Tip! Fixed axis ranges can be useful for accessibility because they enable users of sonification to understand the scale of data displayed in the object. This is especially useful when the values shown in the object can change due to filtering or drilling down in a hierarchy. However, if your data updates frequently or is likely to fall outside of the range when filtering or drilling, a fixed axis range is not recommended as values can unknowingly fall outside the fixed range.   For comparisons over time, use a line chart or a time series plot. Line charts are useful for comparing ordinal groups (like age ranges or loyalty levels), while time series plot are useful for displaying data over time (like year, month, or day). Time series plots can also be useful for spotting seasonal trends in the data.   Pro Tip! Time series plots and line charts include an option to add an overview axis. This is a scrolling axis that enables you to specify how much of the axis is displayed and what segment is displayed. While these are useful for focusing on smaller segments when there are a lot of data values, they can add clutter to the report. In addition, screen readers cannot interact with an overview axis. If an overview axis needs to be included, it’s a best practice to add another accessible object (like a list table or crosstab) that presents the same information in an accessible way. Alternatively, you can direct users to maximize the chart to view the details table.     In this example, the line chart shows quantity ordered by age group (an ordinal value) and the time series plot shows total sales by day.   Pro Tip! Animation can also be used to make comparisons over time. Be aware that the animation toolbar is not fully accessible to users of screen readers. The details table displayed when maximizing the object, however, is fully accessible. If using animation in your charts, consider including a note that instructs users to maximize the object to view a table of the same information in the animated graph.   For comparing against a benchmark, you can add reference lines or display rules to most charts.     In this example, a reference line is added to the chart on the left to compare customer satisfaction values to a target (50%) for each continent and a display rule is added to the chart on the right to highlight continents where customer satisfaction falls below 50% in pink.   Alternatively, a needle plot can be used to compare values to a baseline. A bullet gauge or a targeted bar chart can be used to compare actual values to a target.     In this example, a needle plot is used to compare profits to a set baseline ($50,000). The bullet gauge compares total sales (13M) to the goal (23M) and the targeted bar chart compares total sales for each unit to the target. For the targeted bar chart, a display rule is added to highlight the units where actual sales do not exceed target sales in pink.   Pro Tip! When working with gradients, three-color gradients (like the one used for the bullet gauge) are not accessible for color deficient users. Instead use two-color gradients and choose starting and ending colors with sufficient contrast or difference in luminosity.   Showing Survey or Questionnaire Results   If you want to display the results of a survey or questionnaire, you can use a stacked bar chart to compare Likert responses, a vertical bar chart (or column bar chart) for all that apply questions, or a table or crosstab if precision is important. A Likert is a global scale that is used to assess opinions, attitudes, and views; a 5-point example of a Likert scale would be the following choices: strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree. Box plots can also be used to show the average response, the minimum and maximum values, and outliers. Many people might be unfamiliar with box plots, so it might be helpful to provide an explanation or display the same information in an alternative way.     In this example, the stacked bar chart displays the response percentage for a Likert scale (choose one), the vertical bar chart shows the number of responses for a question where users can select all that apply, and the crosstab shows the average response. The box plot displays the average response (diamond), the median response (line), and the minimum and maximum responses (the whiskers).   Describing How Parts Relate to the Whole   When describing how parts relate to the whole, a donut chart or pie chart can be useful for comparing less than 5 data points and treemaps can display many more data points and two measures (one to size the tile and one to color the tile).     In this example, the donut chart and pie chart show the percent quantity ordered for each age group (5 distinct values) and the treemap shows the number of orders and customer satisfaction by product make.   Pro Tip! Use pie charts sparingly. It is very difficult to compare the relative sizes of slices in the pie chart. To make the comparison easier, add data labels to each slice.   In addition, stacked bar charts can be normalized to show percent of total, histograms can be used to show ranges and intervals, and geo maps can be used when location is an important part of the analysis. Consider supplementing geo maps (which may be difficult to interpret) with more easily understood objects (like tables or crosstabs).     In this example, the bar chart options are modified to set the Grouping style to Stacked (so one bar is displayed for each continent) and the Group scale to Normalize groups to 100% (so the bar shows the relative contribution for each order type). The histogram shows the range of values for vendor satisfaction, which can be useful for modeling, and shows the number of values that fall within each range.     In this example, the geo map shows customer locations on a map background which makes it easier to see areas with a high concentration of customers.   Showing the Relationship Between Data Items   To show the relationship between two measures, use a scatter plot or heat map. Scatter plots are great for low cardinality measures or small data sources and heat maps are better at displaying high cardinality measures or large data sources.     In this example, the scatter plot displays the relationship between two measures (total profit and annual salary) and uses color to show differences between job titles. The heat map displays the relationship between two measures (product sale and customer satisfaction) and uses color to show the number of observations within each range.   Use a bubble plot to show the relationship between three measures or a correlation matrix to show the linear relationship between many measures.     In this example, the bubble plot shows the relationship between three measures and uses color to show differences between age groups: two measures (profit and days to delivery) determine bubble placement and one measure (quantity ordered) determines bubble size. The correlation matrix shows the linear relationship between many measures and uses color to shows the strength of the relationship (where lighter cells indicate a weak relationship or correlation value near 0 and darker cells indicate a strong relationship or correlation values near 1 or -1).   Pro Tip! It’s a best practice to limit the number of digits displayed after decimal points in your charts unless they are required for precision.   In addition, you can use a network diagram to show many relationships (like relationships between users of social media or parties of interest in a terrorist network), a path analysis object to show process flows (like steps in a process), or a text object to add a detailed description when no visual is possible.     In this example, the network diagram shows the relationship between employees in an organization and uses color to indicate the department of each employee. The text object shows a bulleted list of steps to complete when moving, which don’t need to be completed in a specific order.     In this example, the path analysis object shows the steps in the drug approval process, which do need to be completed in a specific order and uses color to show where drugs dropped off or did not move on to the next phase.   Pro Tip! It’s especially important to ensure that legends are displayed on all screen sizes. If legends are not displayed in the chart, they can only be viewed when the user activates a collapsed button. This button does not have an accessible name or role, so it’s not useful for users of screen reader technology.   Is a Graph Even Required?   In some cases, a graph might not need to be used to display your data. Word clouds, images, or illustrations created with the Data-driven content object might better communicate your point. In addition, a Text object can be used to give context to certain data values or state the conclusions that can be inferred from the data.     In this example, the word cloud is used to show the top recipe searches on a website. Recall that word clouds should be avoided if analytical accuracy is desired because it is difficult to compare the relative sizes of different words due to the number of letters in each word and the size of the letters.   In this example, the Image object is used to show the components of a model car and to display an illustration, and the text object summarizes the data presented in other objects and draws conclusions based on those values.   Pro Tip! When using text to provide instructions, avoid using sensory characteristics (like the size of the object, the shape of an item, or the position of the object on the screen) as the sole means of conveying information or identifying a portion of the report. This makes the report more accessible for users of screen reader technology.   Summary   After you have drafted a plan for your report, you need to choose the best chart for displaying your data. Choosing an appropriate chart is more of an art than a science, but several rules are available to aid you in showcasing your data and recommendations are made for charts that most closely aligns with the data you want to present and the details you want to showcase. There is no correct choice when choosing a chart type, but there are many incorrect choices. If you don’t believe me, listen to two experts on data visualization:   “… the only thing worse than a pie chart is several of them.” – Edward Tufte   “Save the pies for dessert.” – Stephen Few   If you want to apply the skills learned in this post to a report created in SAS Visual Analytics, take the report design challenge.   In the next part of this series, we discuss some chart best practices.   References   Beautiful Reports   Envisioning Information by Edward Tufte   The Visual Display of Quantitative Information by Edward Tufte   Visual Explanations by Edward Tufte   Documentation: Keyboard Shortcuts for SAS Visual Analytics   Documentation: Viewing Objects with SAS Graphics Accelerator   Documentation: Creating Accessible Reports Using SAS Visual Analytics   Documentation: Accessibility Features for SAS Visual Analytics       Find more articles from SAS Global Enablement and Learning here. ... View more

This is the first part of a six part series that describes tips and tricks for building impactful reports in SAS Visual Analytics.   Draft a Plan (this article!) Choose the Best Chart Bonus: Chart Best Practices Focus on What’s Important Consider the Layout Test, Test, and Test Again   SAS Visual Analytics enables you to create compelling, interactive reports that can be viewed by anyone, anywhere. To create impactful reports that resonate with your audience you need to (1) draft a plan for the report, (2) choose the best chart type to display your data, (3) create your reports so viewers can focus on what’s important to them, (4) pick a layout that will best display your data and tell your data story, and (5) test the report to ensure it operates and looks the way you want. In this blog, we will focus on the first step in the process: Draft a Plan.   Draft a Plan   When drafting a plan, you need to select the data you want to use for the report, think about your audience, craft your data story, and sketch the design of the report.   Select your Data   Before you start creating your report in SAS Visual Analytics, you need to select your data. This data can be stored in any format (SAS datasets, Microsoft Excel files, database tables, or text files) and can even exist in multiple tables. You can combine the tables before using them in SAS Visual Analytics or you can create data joins and aggregated tables within your report. You can even use multiple data sources.   Pro Tip! As a best practice, you should prepare your data before using it in SAS Visual Analytics. Data joins and aggregated tables created in Visual Analytics use temporary CAS tables that are created for each user who views the report. This can impact the performance of your report. In addition, using SAS code to prepare your data will provide you with more advanced functionality (like functions that can improve data quality, multi-table joins, non-equijoins, and more).   You can also create a variety of data items in SAS Visual Analytics using one-click calculations (like the difference from the previous period or the distinct count), hierarchies (which enable you to view different levels of your data and create a drill-down functionality for your charts), geography data items (that can be used for mapping), parameters (which give report viewers more control over the report), and statistical data items (like interaction effects, spline effects, and partitions).   Know your Audience   Next, you need to determine the audience of the report. The needs and interests of your audience will determine what you present, how you present it, and the level of detail you’ll include in the report.   For example, if you are creating a report for statisticians, you might use models (like the Statistics and Machine Learning objects) and advanced analytic objects (like automated explanation, automated prediction, forecasting, network analysis, path analysis, and text topics). For this audience, these models will be self-explanatory, and you might not have to include a lot of detail about how the model works, the purpose of the model, and why it’s useful for the report. On the other hand, if you are building a report for a general audience (like the public or your non-statistician customers), you might want to use simpler graphs that are easier to interpret and understand. In addition, you might include additional instructions and explanatory text in the report to explain the purpose and usefulness of objects and the report itself.   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 this example, a Cluster object (located in the Statistics group) is used in a report for statisticians, who understand the concepts of clustering and can easily interpret the various components. For a general audience, however, simpler objects (like key value objects and bar charts) are used instead, and explanatory text (using the Text object) is added to explain the layout of the report and state the conclusions.   Pro Tip! Regardless of your audience, it’s a best practice to provide instructions for using the report (including tips on navigating the interface, if necessary) and to give context to your graph content. This will enable your viewers to identify the key takeaways of the report and can incite them to act on those findings.   Your audience will also give you a good idea about the level of modifications you want to allow for the report. You can control the level of modification for each report by setting the Customization level for the report. The degree of customization can be adjusted from simple edits (that will enable viewers to modify simple options for the report, such as adding labels and hiding or displaying legends) to comprehensive edits (that will enable viewers to change chart types), to data edits (that will enable viewers to modify the data used in report objects, filters, and ranks).       This example shows the options available for a bar chart when the Viewer customization level is set to simple edits, comprehensive edits, and data edits.   Craft your Story   Your reports should focus on telling a single data story. When you begin to craft your story, consider these questions:   Are you trying to persuade your audience? Are you presenting facts? Do you want the audience to act?   The answers to these questions will help you determine how to present your findings in the report and will help you start constructing your narrative. Storytelling helps your audience feel more connected to your report by eliciting emotion and triggering memory. Telling a good story that resonates with your audience will make it more likely that they will remember what the report has to say and will want to act on those findings.   Sketch Design   Finally, you should roughly sketch out the design of the report. This can be anything from a bulleted outline of the report, a rough table of contents, or a hand drawn draft of each page that shows the anticipated layout, the types of objects that will be used on each page, and an explanation of how each page contributes to the overall data story. You should always keep the story in mind when building the report, from the very first sketch to the fully completed, and tested report.   Also in this stage, you should start planning for accessibility. Think about the audience and what functionality they may need to better interact with your report. For example, you can plan to add a table representation for each graph to more easily enable viewers to see specific values, you can add simpler graphs as an alternative to more complex graphs, or you can add additional information about keyboard shortcuts in SAS Visual Analytics or more details for users of screen-reader technology.   Summary   Before you begin to create your reports in SAS Visual Analytics, you need to draft a plan. In this initial step, you need to select the data you want to use for the report, think about your audience, craft your data story, and sketch the design of the report.   In the next part of this series, we discuss best practices for choosing the best chart for displaying your data.     References   Documentation: Managing Data Documentation: Keyboard Shortcuts for SAS Visual Analytics Documentation: Viewing Objects with SAS Graphics Accelerator Documentation: Creating Accessible Reports Using SAS Visual Analytics Documentation: Accessibility Features for SAS Visual Analytics Telling Your Data Story by Atrin Assa SAS Visual Analytics Stories are Data With a Soul by Ted Stolarczyk     ... View more

This is the fourth part of a four part series that discusses how to display product photos in SAS Visual Analytics.   Basics Validating Data Debugging Viewing Multiple Photos (this article!)   Scenario   In previous posts, we created a visualization to display an image of a selected product in a Visual Analytics report or the company logo if no product was selected. In this post, we will see how to display multiple photos if more than one product (or no product) is selected in the report. To do this, we will need to restructure the data passed from Visual Analytics (in JSON format) to an array of objects so it can be more easily used in the JavaScript code and then update the code to display an image gallery instead of a single image.   Note: More detail about the requirements and steps for using the data-driven content object in Visual Analytics can be found in the first post in this series.   Note: The entire code is available at the end of this post.   Updating Sample Data   First, we need to update the sample data message to contain multiple rows instead of a single row.      Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Notice that the rowCount, availableRowCout, and data values are updated to show multiple rows. For more information about how data is passed from Visual Analytics to the third-party visualization, see the third post in this series.   Restructuring the Data   Now that we are using more than one row of data in our JavaScript code, we need to restructure the data from a two-dimensional array to an array of objects. This is done if the data passed from Visual Analytics is valid (which is determined by referencing the validateRoles function in the contentUtil.js utility).     Now, the data values can be referenced using data[i].item, where data[0].item references the first data value (the value Figure from the sample data), data[1].item references the second data value (the value Game from the sample data), and so on.   Gallery of Images   Finally, the JavaScript code needs to be updated to display one image (at full width) if one product line is selected, and a gallery of images (two per row) if more than one product line is selected.     For this example, a div (with a class of row) is created to hold all images (rowDiv) and appended to the web page inside the div (with an id of photo).   Then, a new div is created for each image using the for loop (colDiv).   If one row is passed, the div (and the image) spans the entire width (using the Bootstrap class of col-12). If more than one row is passed, the div (and each image) spaces half the width (using Bootstrap class of col-6).   Then, a Bootstrap class of py-2 is added to insert padding on the top and bottom of each image and colDiv is added to the DOM (using the appendChild method).   Lastly, a new img element is created for each row of data passed from Visual Analytics (using the createElement property), attributes of the element are modified (src and alt), and the element is added to the DOM (using the appendChild method).   The src attribute points to the image for the selected product line in the images folder. Note that the toLowerCase method is needed convert the product line passed from Visual Analytics (which is proper case) to the case used for the image file (which is in lower case). The alt attribute is the value of the selected product line. A Bootstrap class (img-fluid) is also added to the image to ensure the image takes up 100% of the width of the div (colDiv).   Testing   When the code is complete, you can test it in Visual Analytics. Notice that page prompts have been relocated to the left of the canvas and the page prompt has been changed to a list control (so users can select multiple product lines). In addition, a data-driven content object (that references the code) is included on the canvas.     Notice when no product line is selected, images for all product lines are displayed in the data-driven content object.   Pro Tip: Recall that to use list controls in the page prompt or report prompt areas, the location of the prompts need to be on the left or the right or a prompt container needs to be used. In this example, I changed the location of the page prompt container to the left side.   When one product line is selected, an image for that product line is displayed and it takes up the full width of the object.     When more than one product line is selected, images for those product lines are displayed and take up half the width of the object and subsequent images are added below the others.     Summary   You can use similar code to restructure data passed from Visual Analytics from a two-dimensional array to an array of objects and to display multiple images in your report.   References   Documentation: Working with Data-Driven Content   Programming Considerations for Data-Driven Visualizations   SAS GitHub repository for third-party visualizations    Book: Interactive Reports in SAS Visual Analytics      Find more articles from SAS Global Enablement and Learning here.   Code   Below is the code in its entirety.   <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Document</title> <!-- Bootstrap --> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-EVSTQN3/azprG1Anm3QDgpJLIm9Nao0Yz1ztcQTwFspd3yD65VohhpuuCOmLASjC" crossorigin="anonymous"> <!-- Utilities for DDC --> <script type="text/javascript" src="util/messagingUtil.js"></script> <script type="text/javascript" src="util/contentUtil.js"></script> </head> <body> <!-- Container for Photos --> <div class="container" id="photo"> <!-- <div class="row"> <div class="col py-2"> <img src="images/figure.jpg" alt="Figure" class="img-fluid"> </div> <div class="col py-2"> <img src="images/game.jpg" alt="Game" class="img-fluid"> </div> <div class="col py-2"> <img src="images/plush.jpg" alt="Plush" class="img-fluid"> </div> <div class="col py-2"> <img src="images/bead.jpg" alt="Bead" class="img-fluid"> </div> </div> --> </div> <!-- Custom JavaScript --> <script> // Listening for HTML document completely loaded and parsed, best practice document.addEventListener("DOMContentLoaded", function() { /******************************* Declare variables ***************************************/ // Sample data message to render outside of VA for debugging const sampleData = { "version": "1", "resultName": "dd55", "rowCount": 5, "availableRowCount": 5, "data": [ [ "Figure" ], [ "Game" ], [ "Plush" ], [ "Beach" ], [ "Bead" ] ], "columns": [ { "name": "bi79", "label": "Product Line", "type": "string" } ] }; // Dynamic data variables let vaMessage; // Data message from VA let vaResult; // Name of DDC object, required to send messages back to VA let rowCount; // Number of rows passed from VA let data; // Data to be parsed from VA data message // Selection variables let photoDiv; // Div for photo let rowDiv; // Div for row let colDiv; // Div for col let itemPhoto; // Element for photo /******************************* Setup Callback Functions ***************************************/ // Attach event for data message from VA va.messagingUtil.setOnDataReceivedCallback(onDataReceived); // If not being rendered in iFrame (outside of VA), render with sample data if (!inIframe()) { onDataReceived(sampleData); } // Take action on received data function onDataReceived(messageFromVA) { // Initialize data variables vaMessage = messageFromVA; vaResult = messageFromVA.resultName; rowCount = messageFromVA.rowCount; // Validate data roles if ( !va.contentUtil.validateRoles( messageFromVA, ["string"], // data from DDC object ["number"] // additional field for brush column (not needed in this example) ) ) { // If roles are invalid or no data is being passed, display a message in VA about required roles (requires https) va.messagingUtil.postInstructionalMessage( vaResult, "Display Photo expects columns to be assigned in this order:\n" + " 1. Item (string)" ); } else { // Restructure data from 2d array to array of objects data = []; for (let i = 0; i < vaMessage.data.length; i++) { data.push({ item: vaMessage.data[i][0], index: i }); } } // Add photo to DOM const photoDiv = document.getElementById("photo"); photoDiv.innerText = ""; const rowDiv = document.createElement("div"); rowDiv.classList.add("row"); photoDiv.appendChild(rowDiv); for (let i = 0; i < data.length; i++) { const colDiv = document.createElement("div"); if (rowCount == 1) { colDiv.classList.add("col-12"); } else { colDiv.classList.add("col-6"); } colDiv.classList.add("py-2"); rowDiv.appendChild(colDiv); itemPhoto = document.createElement("img"); itemPhoto.src="images/"+data[i].item.toLowerCase()+".jpg"; itemPhoto.alt = data[i].item; itemPhoto.classList.add("img-fluid"); colDiv.appendChild(itemPhoto); } }; /******************************** Helper functions *****************************************/ function inIframe() { try { return window.self !== window.top; } catch (e) { return true; } }; }); </script> </body> </html>       ... 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This is the third part of a four-part series that discusses how to display product photos in SAS Visual Analytics.   • Basics • Validating Data • Debugging (this article!) • Viewing Multiple Photos   Scenario   Recall from previous posts that you would like to display an image of a selected product in a report. In this post, we will see how to add sample data to your code so the visualization can be displayed outside of SAS Visual Analytics. This is helpful for debugging the code before you use it in Visual Analytics. Note: More detail about the requirements and steps for using the data-driven content object in Visual Analytics can be found in the first post in this series. Note: The entire code is available at the end of this post.   Data Messages   Recall that data messages are sent from Visual Analytics to the data-driven content object in JSON format. We will create a sample data message (also in JSON format) so we can render the visualization outside of Visual Analytics. Note: Communication between Visual Analytics and the third-party visualization is discussed in more detail in the second post in this series.   To do this, we need to see the Product Line data for this example in JSON format. The SAS GitHub repository for third-party visualizations includes a visualization (jsonDataViewer.html) that can be used to display your data as a JSON object.   In Visual Analytics, add a data-driven content object to the canvas that references the jsonDataViewer sample from the SAS GitHub repository. This is the value I entered in the URL field on the Options pane: https://sassoftware.github.io/sas-visualanalytics-thirdpartyvisualizations/samples/jsonDataViewer.html .   Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   Note: Details on how to reference samples can be found in the samples folder on the SAS GitHub repository for third-party visualizations.   Then, on the Roles pane, assign Product Line to the Variables role.     The data-driven content object displays the Product Line data in JSON format, which is how data is passed from Visual Analytics to the third-party visualization. You can filter or edit the result to return a smaller number of rows. For this example, we only want to return one row of data so we can select a Product Line value from the page prompt (Beach).     Declaring a Sample Data Message   We can use the data from the jsonDataViewer sample to add a sample data message to the code.     Recall that in the code for this visualization, if one row is passed from Visual Analytics that means a single product line is selected and an image of that product line appears. If more than one row is passed from Visual Analytics that means no product line is selected and the company logo appears. For this example, we want to test the code when one row is returned, so only one row is included in the sample data message. Displaying Sample Data   Next, code needs to be added to display the sample data (rather than the data passed from Visual Analytics) when the visualization is viewed outside of Visual Analytics.     The if statement checks to see if the function returns not true (! means not). If it does, then the sample data (sampleData) is passed to the onDataReceived function. Recall that the onDataReceived function initializes data variables, validates the data, and adds an image to the web page.   The inIframe function checks to see if the visualization is being rendered in an iFrame.     If the visualization is used inside Visual Analytics (in a data-driven content object), it’s rendered inside an iFrame (the window of the object is not the same as the topmost window in the window hierarchy, window.self !== window.top). If this is the case, the data passed from Visual Analytics is used. If the visualization is viewed outside of Visual Analytics (to test the validity of the code), it’s not rendered inside an iFrame (the window of the object is the same as the topmost window in the hierarchy). If this is the case, the sample data is used.   Testing   When the code is complete, you can test it by viewing the HTML file in a browser (outside of Visual Analytics).     Because the visualization is not viewed in an iFrame (outside of Visual Analytics), sample data is used. Recall that sample data has a rowCount of 1 and data for the Beach product line, so the Beach image is displayed. Note: For testing purposes, I used the FiveServer extension in Visual Studio Code to use my machine as a web server.   To test all scenarios, change the rowCount for the sample data message to 2.     When you view the updated HTML file in the browser, the company logo is displayed.       Notice, for this example, you don’t have to add additional data items to the sample data message. The value of rowCount determines whether a product line image is displayed or the company logo is displayed.   Summary   You can use this code to view any third-party visualization outside of Visual Analytics for debugging purposes. Remember to use the jsonDataViewer sample from the SAS GitHub repository for third-party visualizations to create the sample data in JSON format.   In the next part of this series, we will discuss how to view multiple photos if more than one product line is selected in the Visual Analytics report.   References   Documentation: Working with Data-Driven Content   Documentation: Programming Considerations for Data-Driven Visualizations   Resource: SAS GitHub repository for third-party visualizations   Book: Interactive Reports in SAS Visual Analytics    Code   Below is the code in its entirety.   !DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Document</title> <!-- Bootstrap --> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-EVSTQN3/azprG1Anm3QDgpJLIm9Nao0Yz1ztcQTwFspd3yD65VohhpuuCOmLASjC" crossorigin="anonymous"> <!-- Utilities for DDC --> <script type="text/javascript" src="util/messagingUtil.js"></script> <script type="text/javascript" src="util/contentUtil.js"></script> </head> <body> <!-- Container for Photo --> <div class="container" id="photo"> </div> <!-- Custom JavaScript --> <script> // Listening for HTML document completely loaded and parsed, best practice document.addEventListener("DOMContentLoaded", function() { /******************************* Declare variables ***************************************/ // Sample data message to render outside of VA for debugging const sampleData = { "version": "1", "resultName": "dd60", "rowCount": 2, "availableRowCount": 1, "data": [ [ "Beach" ] ], "columns": [ { "name": "bi69", "label": "Product Line", "type": "string" } ] }; // Dynamic data variables let vaMessage; // Data message from VA let vaResult; // Name of DDC object, required to send messages back to VA let rowCount; // Number of rows passed from VA let data; // Data to be parsed from VA data message // Selection variables let photoDiv; // Div for photo let itemPhoto; // Element for photo /******************************* Setup Callback Functions ***************************************/ // Attach event for data message from VA va.messagingUtil.setOnDataReceivedCallback(onDataReceived); // If not being rendered in iFrame (outside of VA), render with sample data if (!inIframe()) { onDataReceived(sampleData); } // Take action on received data function onDataReceived(messageFromVA) { // Initialize data variables vaMessage = messageFromVA; vaResult = messageFromVA.resultName; rowCount = messageFromVA.rowCount; // Validate data roles if ( !va.contentUtil.validateRoles( messageFromVA, ["string"], // data from DDC object ["number"] // additional field for brush column (not needed in this example) ) ) { // If roles are invalid or no data is being passed, display a message in VA about required roles (requires https) va.messagingUtil.postInstructionalMessage( vaResult, "Display Photo expects columns to be assigned in this order:\n" + " 1. Item (string)" ); } // Add photo to DOM const photoDiv = document.getElementById("photo"); photoDiv.innerText = ""; // Display photo for selected item if ( rowCount == 1 ) { itemPhoto = document.createElement("img"); itemPhoto.src="images/"+vaMessage.data[0][0].toLowerCase()+".jpg"; itemPhoto.alt = vaMessage.data[0][0]; itemPhoto.classList.add("img-fluid"); photoDiv.appendChild(itemPhoto); } else { itemPhoto = document.createElement("img"); itemPhoto.src="images/insight_toys_logo.png"; itemPhoto.alt = "Insight Toys Logo"; itemPhoto.classList.add("img-fluid"); photoDiv.appendChild(itemPhoto); }; }; /******************************** Helper functions *****************************************/ function inIframe() { try { return window.self !== window.top; } catch (e) { return true; } }; }); </script> </body> </html>     ... View more

This is the second part of a four-part series that discusses how to display product photos in SAS Visual Analytics.   Basics Validating Data (this article!) Debugging Viewing Multiple Photos   Scenario Recall from the previous post that you would like to display an image of a selected product in a report. In this post, we will see how to add code to validate the data received from Visual Analytics and send a message back to Visual Analytics about required roles if the data is not valid (or no data is assigned to the data-driven content object).   Note: More detail about the requirements and steps for using the data-driven content object in Visual Analytics can be found in the previous post. Note: The entire code is available at the end of this post.   Communication   Before we discuss validating the data received from Visual Analytics, it’s important to understand how Visual Analytics and the third-party visualization communicate. Three types of messages are sent between Visual Analytics and the third-party visualization: data messages, instructional messages, and selection messages.   Select any image to see a larger version. Mobile users: If you do not see this image, scroll to the bottom of the page and select the "Full" version of this post.   Data messages are sent from Visual Analytics to the data-driven content object. This message is sent in JSON format and includes the data assigned to the data-driven content object (in the Roles pane) and metadata information about that data. The data are stored in an array type structure and at the end of the array, Visual Analytics sends an additional column (named brush) that indicates whether a row of data is selected when the data-driven content object is the target of a linked selection action. The SAS GitHub repository for third-party visualizations includes a visualization (jsonDataViewer.html) that can be used to display your data as a JSON object.   Note: In this example, because the data-driven content object is not the target of a linked selection action, we are not using the brush column in our JavaScript code.     Instructional messages are sent from the third-party visualization to Visual Analytics. These messages typically contain details about the type, number, and order of data items required for the visualization.   Recall, to send messages back to SAS Visual Analytics, SAS Viya must be accessed using the HTTPS protocol, a secure version of HTTP that is encrypted to increase the security of the data transferred between the web client and the server.     Selection messages are sent from the third-party visualization to Visual Analytics. These messages include details about any selection made in the third-party visualization. This enables the third-party visualization to be used as the source of an action.   Note: In this example, selection messages are not used.   Declaring and Initializing an Additional Variable   It’s a best practice to validate the data being passed from Visual Analytics before executing any JavaScript code. If the data is not valid (or no data is assigned), a message will be sent back to Visual Analytics with details about what roles are required. To pass messages back to the data-driven content object in Visual Analytics, we need to create a dynamic data variable that holds the internal name of the object.     In the callback function created to get the data from Visual Analytics (onDataReceived), that data from Visual Analytics (messageFromVA) is used to initialize the vaResult variable with the internal name of the data-driven content object.       Validate Roles   Next, the data passed from Visual Analytics can be validated using the validateRoles function from the contentUtil.js utility.     The if statement checks if the roles are not valid. Notice the exclamation point in front of the !va.contentUtil.validateRoles() function, the ! means not.   The validateRoles function uses the data received from Visual Analytics (messageFromVA), checks if the required column is a string, and checks if the optional column (the brush column) is a number. The brush column is automatically added to the data received from Visual Analytics to indicate if a row is selected when the data-driven content object is the target of a linked selection action. For this example, this column is not needed.   Note: Utilities can be found on the SAS GitHub repository for third-party visualizations. For the utilities to be available in the visualization, a link to the utilities must be included in the head element of the web page.   Adding Instructions   If the data passed from Visual Analytics is not valid (or no data is assigned to the object), then a message describing the required roles can be sent back to Visual Analytics using the postInstructionalMessage function from the messagingUtil.js utility.     The postInstructionalMessage function uses the internal name of the data-driven content object (vaResult) to send a message back to Visual Analytics about the required roles for the object (Item), their assignment order, and the types of data items required (string).   Recall that HTTPS needs to be configured to send messages back to Visual Analytics.   Note: Utilities can be found on the SAS GitHub repository for third-party visualizations. For the utilities to be available in the visualization, a link to the utilities must be included in the head element of the web page.   Testing   When the code is complete, you can test it in Visual Analytics. If invalid data (or no data) is assigned to the data-driven content object, a message appears stating what data is required, the assignment order, and the types of data items. It’s a best practice to send an instructional information with this information so the report designer knows how to use the visualization.       Summary   You can use this code to validate data for any third-party visualization and to send messages about required roles back to Visual Analytics.   In the next part of this series, we will discuss how to debug your code using sample data.     References Documentation: Working with Data-Driven Content Documentation: Programming Considerations for Data-Driven Visualizations Resource: SAS GitHub repository for third-party visualizations Book: Interactive Reports in SAS Visual Analytics   Code   Below is the code in its entirety.    <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Document</title> <!-- Bootstrap --> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-EVSTQN3/azprG1Anm3QDgpJLIm9Nao0Yz1ztcQTwFspd3yD65VohhpuuCOmLASjC" crossorigin="anonymous"> <!-- Utilities for DDC --> <script type="text/javascript" src="util/messagingUtil.js"></script> <script type="text/javascript" src="util/contentUtil.js"></script> </head> <body> <!-- Container for Photo --> <div class="container" id="photo"> </div> <!-- Custom JavaScript --> <script> // Listening for HTML document completely loaded and parsed, best practice document.addEventListener("DOMContentLoaded", function() { /******************************* Declare variables ***************************************/ // Dynamic data variables let vaMessage; // Data message from VA let vaResult; // Name of DDC object, required to send messages back to VA let rowCount; // Number of rows passed from VA // Selection variables let photoDiv; // Div for photo let itemPhoto; // Element for photo /******************************* Setup Callback Functions ***************************************/ // Attach event for data message from VA va.messagingUtil.setOnDataReceivedCallback(onDataReceived); // Take action on received data function onDataReceived(messageFromVA) { // Initialize data variables vaMessage = messageFromVA; vaResult = messageFromVA.resultName; rowCount = messageFromVA.rowCount; // Validate data roles if ( !va.contentUtil.validateRoles( messageFromVA, ["string"], // data from DDC object ["number"] // additional field for brush column (not needed in this example) ) ) { // If roles are invalid or no data is being passed, display a message in VA about required roles (requires https) va.messagingUtil.postInstructionalMessage( vaResult, "Display Photo expects columns to be assigned in this order:\n" + " 1. Item (string)" ); } // Add photo to DOM const photoDiv = document.getElementById("photo"); photoDiv.innerText = ""; // Display photo for selected item if ( rowCount == 1 ) { itemPhoto = document.createElement("img"); itemPhoto.src="images/"+vaMessage.data[0][0].toLowerCase()+".jpg"; itemPhoto.alt = vaMessage.data[0][0]; itemPhoto.classList.add("img-fluid"); photoDiv.appendChild(itemPhoto); } else { itemPhoto = document.createElement("img"); itemPhoto.src="images/insight_toys_logo.png"; itemPhoto.alt = "Insight Toys Logo"; itemPhoto.classList.add("img-fluid"); photoDiv.appendChild(itemPhoto); }; }; }); </script> </body> </html>   Find more articles from SAS Global Enablement and Learning here. ... View more

This is the first part of a four part series that discusses how to display product photos in SAS Visual Analytics.   Basics (this article!) Validating Data Debugging Viewing Multiple Photos The data-driven content object in SAS Visual Analytics enables you to display your data in a custom third-party visualization within your Visual Analytics report. These third-party visualizations can be developed in any JavaScript charting framework, like D3.js, Google Charts, or CanvasJS. The visualization receives its data from Visual Analytics and can interact with filters, ranks, and actions in the same manner as other objects in your report. The data-driven content is not limited to unique or complex visualizations, however. You can use the data-driven content to present any content that can be displayed in an iFrame.   Scenario   Suppose you would like to display an image of a selected product in a report. Because the image is updated based on viewer selections, a data-driven content object is needed for this task. You can use this code to display images for any scenario: to show a picture of an employee, an office location, or even a state flag.   Requirements   To use the data-driven content object, you will need some knowledge of HTML, CSS, and JavaScript to create the file that will display the image. The file will also need to be hosted on a web server. In addition, if you want to send messages from the HTML file to Visual Analytics, SAS Viya must be accessed using the HTTPS protocol.   Note: For testing purposes I used the Five Server extension in Visual Studio Code to use my machine as a web server.   Steps   To create the visualization, follow these steps:   Create a simple HTML page Add an event listener to execute JavaScript code when the HTML page is loaded and parsed Add sample data for debugging outside of Visual Analytics (optional) Declare variables Create a callback function to get the data from Visual Analytics Validate the data received from Visual Analytics (optional) Use the data received from Visual Analytics to add a photo to the web page   Note: In this post, I will discuss on the required pieces of code. Future posts will cover optional pieces. Note: The entire code is available (via a link) at the end of this post.   Create a Simple HTML Page   I’ll start with a simple HTML page that contains a div (for the photo).     Select any image to see a larger version. Mobile users: To view the images, select the "Full" version at the bottom of the page.   A link is included to Bootstrap so special classes can be used to define the layout of the page and modify the content included in the page. These classes make it easier develop a responsive, mobile-first web page. In this example, Bootstrap will be used to display the image at 100% width, regardless of the screen size.   Script tags that reference the utilities from the SAS GitHub repository for third-party visualizations are included to help when creating the visualization. These utilities will be used in the JavaScript code to receive messages from Visual Analytics (using the setOnDataReceivedCallback function), send messages to Visual Analytics (using the postInstructionalMessage function), and validate the data received from Visual Analytics (using the validateRoles function).   A div is created with an id of photo to hold the image.   Note: For this example, I copied the utilities from the SAS GitHub repository for third party visualizations and stored them in a util folder where the web page is hosted. This is a best practice. Note: Only the setOnDataReceivedCallback function is used in this example. The other functions will be explored in future posts.   Add an Event Listener   Inside the script tag (in the body), an event listener is added to execute a function when the HTML document is loaded and parsed. This is a best practice when working with JavaScript.     Declare Variables   Inside the event listener function, several dynamic variables are created: vaMessage to hold the data passed from Visual Analytics to the third-party visualization and rowCount to hold the number of rows of data passed from Visual Analytics.   The rowCount variable will be used to determine if the viewer selects a product line. If they do, only one row will be passed from Visual Analytics and a picture of that product line will be displayed. If they do not, multiple rows will be passed from Visual Analytics and the company logo will be displayed.     This code also creates some selection variables that will be used to add the photo to the web page: photoDiv to hold the div for the photo and itemPhoto to hold the img element that will contain either the product image or the company logo.   Create a Callback Function   Next, create a callback function to get the data from Visual Analytics.     The setOnDataReceivedCallback function (located in the messagingUtil.js file) creates a callback function that can be used to handle messages received from Visual Analytics. You must define a function (in this example, onDataReceived) that specifies what actions will be performed on the data received from Visual Analytics.   The onDataReceived function takes the data from Visual Analytics (messageFromVA) and initializes the dynamic variables: vaMessage contains the message in its entirety and rowCount contains the number of rows of data passed from Visual Analytics.   Add the Product Photo   After the data variables are initialized, code is added to include the product photo on the web page.     The getElementById method is used to create an object that references the div in the body that has an id of photo. Then, the div is cleared (using the innerText property). Next, an if statement checks to see if the data passed from Visual Analytics contains one row (rowCount == 1). If it does, then a single product line is selected in the report. When this occurs, a new img element is created (using the createElement property), attributes of the element are modified (src and alt), and the element is added to the DOM (using the appendChild method).   The src attribute points to the image for the selected product line in the images folder. Note that the toLowerCase method is needed convert the product line passed from Visual Analytics (which is proper case) to the case used for the image file (which is in lower case). The alt attribute is the value of the selected product line. A Bootstrap class (img-fluid) is also added to the image to ensure the image takes up 100% of the width of the screen.   Testing   When the code is complete, you can test it in Visual Analytics. I added a page prompt to the report to select a product line and added a data-driven content object (that references the code) to the canvas.     Notice that Promo is selected in the drop-down list control.   Adding Product Line to the Variables role will send the selected product line to the third-party visualization. If one row is passed, an image for the selected product line will be displayed.     If multiple rows are passed (nothing is selected in the drop-down list control), the company logo will be displayed.     Considerations   This example is relatively simple because the data from Visual Analytics is only used when one row is passed to the visualization. If you are working on an example that requires the use of multiple rows, you will need to restructure the data from Visual Analytics into a two-dimensional array of objects so it can easily be referenced in your JavaScript code.   In addition, images don’t require any axes to display the data and Bootstrap is used to handle the responsiveness of the image for different screen sizes. In many cases, you will need to extend your code to create axes based on the scale of data assigned to the object and to redraw elements when the screen (or object) is resized. For examples on how to do this, see the samples in the SAS GitHub repository for third-party visualizations.   Summary You can use this code to display images for any selected value in your report: a product, an employee, an office location, or even a state flag. In this example, we used a page prompt to filter the values, but it will even work with actions between objects. Suppose I have a report that contains a bar chart that filters the data-driven content object. When I select a product line (Plush), the image of that product line is displayed.     In the next part of this series, we will discuss how Visual Analytics communicates with the visualization and see how to validate the data passed from Visual Analytics   References Documentation: Working with Data-Driven Content Documentation: Programming Considerations for Data-Driven Visualizations SAS GitHub repository for third-party visualizations Book: Interactive Reports in SAS Visual Analytics   Code   Below is the code in its entirety.    <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Document</title> <!-- Bootstrap --> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-EVSTQN3/azprG1Anm3QDgpJLIm9Nao0Yz1ztcQTwFspd3yD65VohhpuuCOmLASjC" crossorigin="anonymous"> <!-- Utilities for DDC --> <script type="text/javascript" src="util/messagingUtil.js"></script> <script type="text/javascript" src="util/contentUtil.js"></script> </head> <body> <!-- Container for Photo --> <div class="container" id="photo"> </div> <!-- Custom JavaScript --> <script> // Listening for HTML document completely loaded and parsed, best practice document.addEventListener("DOMContentLoaded", function() { /******************************* Declare variables ***************************************/ // Dynamic data variables let vaMessage; // Data message from VA let rowCount; // Number of rows passed from VA // Selection variables let photoDiv; // Div for photo let itemPhoto; // Element for photo /******************************* Setup Callback Functions ***************************************/ // Attach event for data message from VA va.messagingUtil.setOnDataReceivedCallback(onDataReceived); // Take action on received data function onDataReceived(messageFromVA) { // Initialize data variables vaMessage = messageFromVA; rowCount = messageFromVA.rowCount; // Add photo to DOM const photoDiv = document.getElementById("photo"); photoDiv.innerText = ""; // Display photo for selected item if ( rowCount == 1 ) { itemPhoto = document.createElement("img"); itemPhoto.src="images/"+vaMessage.data[0][0].toLowerCase()+".jpg"; // check this! itemPhoto.alt = vaMessage.data[0][0]; itemPhoto.classList.add("img-fluid"); photoDiv.appendChild(itemPhoto); } else { itemPhoto = document.createElement("img"); itemPhoto.src="images/insight_toys_logo.png"; itemPhoto.alt = "Insight Toys Logo"; itemPhoto.classList.add("img-fluid"); photoDiv.appendChild(itemPhoto); }; }; }); </script> </body> </html>   Find more articles from SAS Global Enablement and Learning here. ... 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