Adding additional dimensions to the Scatter Map widget tooltip
Adding additional dimensions to the Scatter Map widget tooltip Additional dimensions can be added to the hover tooltip of the Scatter Map widget type, beyond the default limit of three, to include more information from other dimensions about a location in a Scatter Map widget. This can be accomplished by using a combination of the widget's before query event to add additional dimension data for the hover tooltips and the before datapoint tooltip event to incorporate these dimensions into the tooltip. This method of modifying the query using the "beforequery" event can also be applied to all other widget types // Add extra parameters to be used in tooltips by modifying query widget.on('beforequery', function (se, ev) { // Initial number of widget metadata panels excluding filter panel widget.initialPanelSizeExcludingFilterPanel = ev.query.metadata.filter((panel) => { return panel.panel !== "scope" }).length; // Extra dimensions to show in tooltip, should return a single result, include as many as needed, just add to array // Jaql Objects can be copied from other widgets from the prism.activeWidget.metadata.panels via the browser console // Modify JAQL as needed, title of JAQL is used in tooltip and can be modified to any string widget.extraDimensionJAQL = [ { "jaql": { "table": "Category", "column": "Category ID", "dim": "[Category.Category ID]", "datatype": "numeric", "merged": true, "agg": "count", "title": "Unique Category ID" } }, { "jaql": { "table": "Country", "column": "Country ID", "dim": "[Country.Country ID]", "datatype": "numeric", "merged": true, "agg": "count", "title": "Unique Country ID" } }, ] // Add to default widget query the extra dimensions to be used in tooltips ev.query.metadata = ev.query.metadata.concat(widget.extraDimensionJAQL) }); // Add extra dimensions added with beforequery object to ScatterMap tooltip widget.on("beforedatapointtooltip", (event, params) => { // Convert query results to include only the additional dimensions, and formatted for tooltip template var onlyAdditionalDimensions = widget.queryResult.$$rows.map((withoutDefaultDimensionOnlyAdditional) => { // Remove the default dimensions, first part of row result array var withoutDefaultDimensionOnlyAdditional = withoutDefaultDimensionOnlyAdditional.slice(widget.initialPanelSizeExcludingFilterPanel) // Format for tooltip template, include title from JAQL var extraDimensionObj = withoutDefaultDimensionOnlyAdditional.map((extraDimensionValue, index) => { // Use extraDimensionJAQL for label in tooltip return { "text": extraDimensionValue.text, "title": widget.extraDimensionJAQL[index].jaql.title } }) return extraDimensionObj }); // Object to store extra dimensions params.context.marker.extraDimension = {}; // Use matching queryIndex for tooltip of additional dimensions params.context.marker.extraDimension.arr = onlyAdditionalDimensions[params.context.marker.queryIndex]; // Template for tooltip, modify as needed params.template = ` <div class='geo-text'>{{ model.marker.name }}</div> <div class='measure-holder' data-ng-if='model.measuresMetadata.sizeTitle'> <div class='measure-title slf-text-secondary'>{{ model.measuresMetadata.sizeTitle }}:</div> <div class='measure-value'>{{ model.marker.sizeObj.text }}</div> </div> <div class='measure-holder' data-ng-if='model.measuresMetadata.colorTitle'> <div class='measure-title slf-text-secondary'>{{ model.measuresMetadata.colorTitle }}:</div> <div class='measure-value'>{{ model.marker.colorObj.text }}</div> </div> <div class='measure-holder details-measure-holder' data-ng-if='model.measuresMetadata.detailsTitle'> <div class='measure-title slf-text-secondary'>{{ model.measuresMetadata.detailsTitle }}:</div> <div class='measure-value' data-ng-if="!model.marker.detailsObj.arr">{{ model.marker.detailsObj.text }}</div> <div class="details-wait" data-ng-if="model.marker.detailsObj.pendingDetails"></div> <div data-ng-if="model.marker.detailsObj.arr"> <div class="details-value" data-ng-repeat="a in model.marker.detailsObj.arr">{{a.text}}</div> <div class="details-counter" data-ng-if="model.marker.detailsObj.hasMore"> <div class="details-counter">...</div> <div class="details-counter"> {{'smap.ttip.firstres'|translate:(args={count:model.marker.detailsObj.arr.length})}}</div> </div> </div> </div> <div data-ng-if="model.marker.extraDimension.arr"> <div data-ng-if='model.measuresMetadata.colorTitle'> <div class='measure-holder' data-ng-repeat="a in model.marker.extraDimension.arr"> <div class='measure-title slf-text-secondary'>{{a.title}}:</div> <div class="measure-value extra-dimension-value">{{a.text}}</div> </div> </div> </div>`; }); The JAQL can be changed to any valid JAQL object, and the tooltip template can also be further modified.How to create a list of users with a current role
How to create a list of users with a current role To get a list of the users with their roles you can use table usage. Get the next fields from it - email and userRole. In this table, you can find all the roles that these users had previously [ALT Text: A table displaying two columns: "email" and "userRole." The "email" column contains redacted email addresses, while the "userRole" column lists different roles such as "Viewer," "Data Designer," "datadesigner," and "Designer." Some users appear multiple times with different roles.] To avoid this, lets go to the elasticube, open table usage. Create a Custom Column [ALT Text: A screenshot of the Sisense Elasticube interface showing the usage table with multiple fields, including trackingId, timeStamp, userId, userRole, and more. An arrow points to the three-dot menu next to the usage table, where a dropdown menu is open. The "Add Custom Column" option is highlighted in red, indicating the selection for creating a new custom column.] Name it - rank_date_ids We will use the function RankCompetitionDesc (https://docs.sisense.com/main/SisenseLinux/mathematical-functions.htm) Our query will look like this: RankCompetitionDesc( email, [timeStamp] ) Press the Save button and Build the Elasticube [ALT Text: A screenshot of the Sisense Elasticube interface showing the creation of a custom column named rank_date_ids in the usage table. The formula RankCompetitionDesc(email, [timeStamp]) is entered in the editor. An arrow points to the "Save" button, indicating the action to save the custom column. The status message at the bottom confirms that the formula has been parsed successfully.] Go to the dashboard and edit our widget. Add a filter for a column rank_date_ids = 1 [ALT Text: Dropdown menu interface with multiple selectable options. Check the box next to each option. "Add Filter" and "Usage Analytics Model" are visible. One item is selected and highlighted.] As the result, we will get the current user roles for every user. [ALT Text: Table showing an email column with text obscured in red and a user-role column listing "Viewer" and "Data Designer".]Converting an Existing Sisense Widget to a Dynamic ComposeSDK Widget Component
Using either the widget Sisense API's, or the Sisense widget JS API object, it is possible to determine all components and parameters of a widget required to create an equivalent dynamic ComposeSDK widget component. This approach allows the extraction of all components and parameters of a widget, enabling the creation of an equivalent dynamic ComposeSDK widget component without directly referencing or relying on a specific widget or dashboard ID. The metadata of an existing widget contains all the information needed to create a dynamic version of an existing Sisense widget. It is also possible to use an existing widget ID and dashboard ID to render an existing widget in ComposeSDK, but this does not take full advantage of the capabilities of ComposeSDK to generate new widgets directly.
LAG / LEAD Functions
Question LAG & LEAD Functions are functions that give access to multiple rows within a table, without the need for a self-join. The LAG function is used to access data from a previous row, while the LEAD function is used to return data from rows further down the result set. A perfect example of these functions in use is with the “Sales Order” scenario. A department store is trying to monitor the purchase trends of its customers; more specifically, per customer, what’s the average time it took to repeat an order? For us to do so, we must take a record in the table (Order Date) and compare it to another record in the same table. What are some ways we can do that? In our example, we will analyze & compare the efficiency between the multiple solutions. We will start by addressing the problem in the most common/traditional of solving a problem like this. Answer Steps: 1. Create A New ElastiCube Add Data > Microsoft SQL Server > Connect To Server Pull in Sales Order Header Table "How are we going to manipulate that table to get it into a format that we would want to work with?" Uncheck fields that you don’t need; looking for high-level/important data BUILD! 2. Let's Create A Custom SQL Expression What we are getting here a single customer’s activity. It also orders the Order Date in ascending order (from first date to last) "For this specific customer, I want to be able to see __ amount of days between each order. How can we compute / compare those dates? In order to do so, we want to take the order table and join it to itself, where the customer is the same and the order date is offset to the previous or future date." So, let’s start by leveraging the rank function to assign an order number to every date… The Rank function is important here because within the Customer ID, we want to rank based off the order date, group it per customer, and order by the order date. This basically creates create a system that makes it so that it is comparing itself to itself minus 1. This is what sets up our ranking per order. In order for us to join the table to itself, let’s create a sub query and inside of it, put the entire Customer’s table: (Type Of Join:) What it’s getting joined on: Result: 3. Now That We Have The Order Date & The Total Due, We Can Start Subtracting Them Off Each Other & Start Doing Differences Now you would want to go back to the top; the first “Select” of the sub query and change the claims to: Preview: Now that we saw that this function is working, we can now delete our customer filter, order by filter. Now we get all the differences across all of the customers. Issue With This Solution The main issue with this solution is that it requires a lot of processing due to the two tables. Although we are really using one table, we still have two and the same data is repeated between them. This extra storage isn’t necessary, creates the long processing, and can also create confusion for the customer. Solution 2: Doing A Lookup Function On Itself (Own Data Set). To improve upon the last solution, is there a way to pull in one table and do the manipulation in Sisense, without creating a second table? Answer: Yes! What we can do is take the custom table and add it as a view to the source; that way we only import one table Steps: 1. Duplicate "SalesOrderHeader 1", Rename "SalesOrderHeader 2" BUILD! For us to do a look up on this table, we need to have need to have one key to join. The key is a combination of the customer ID + ranking, that’s what makes it unique. 2. So First, Create A New Field, Record_Rank (Int), Input: BUILD Add Data > Custom SQL Table 3. Head Back Into SalesOrderHeader 2, And Set Up The Different Fields To Create The Key + New Field > CurrCustomerRankKey (Text) Do Entire Build 4. Let's Create A Test Function + Custom SQL Expression > Test Function 5. Go Back To SalesOrderHeaders 2 Table, Do Lookup In There + New Field > PrevOrderDate (Date-Time) + New Field > PrevTotalDue (Float) + New Field > DaysBetweenOrders(Int) + New Field > DiffOrderAmount Schema Build: Solution 3: LAG & LEAD Functions This solution does not include a self-join Steps: 1. Open Up SQL Server (TC SQL) Go to same database (Adventure Works 2012) > create new query Let’s start off the new query as: Now we want to dynamically compare one against the other. Instead of doing a self-join, it tells itself to look at the look at the record/number that is above or below and just pull that value in. So it’s almost like a lookup , but it doesn’t require a join , it just requires you to tell it how to sort the data and just dynamically go up and go down and pull the field that you want in to the current record. This is where the LAG & LEAD functions step in To go back a record, we call the LAG function. Within the LAG function, we must define the field that we want to pull in (order date) and how many records back do you want to go (1). Next we want to group by (partition by) and Order By a type of variety and then order it either ASC / DES. To make our “look up” a bit more distinct we can Result: If we want to compare up against future dates, we would use: And its result would be: What we can also pull in the Total Due and calculate the previous Total Due: Which would require a sub query: To get of the first null, we would enter: Pull in everything from t2.* and now we can calculate our differences (Date Diff, etc): As for getting this entire query into Sisense, we want to create a View
