What is a Data App?
A data app is a custom software application focused on processing, analyzing, and visualizing data to help users make decisions or gain insights. Unlike regular apps, data apps are built around specific data. They stay up to date, often allow end users to interact with filters and other controls, and have much broader use cases than just interactive reports (dashboards). They allow users to explore patterns, understand trends, and analyze outcomes within their domain.
As such, data apps are popular across all industries and can be built on any type of raw, summarized, or calculated data. For some great examples of data apps, see Example dashboards, reports, data apps, and more.
Building a data app in Displayr follows this basic workflow:
- Connecting data
- Working with variables
- Working with already calculated Data Tables
- Building outputs (tables, charts, and more)
- Creating custom items using R in Displayr
- Designing your Data App
- Adding interactivity
- Deploying your Data App
- Updating your Data App
Connecting data
Depending on the type of data app you are creating, you may need to import some data to get started. Most commonly, this is a data set containing raw data, also called unit record data, where individual records are stored as rows and variables as columns (though tables with data already calculated in them are also supported; see Working with Data Tables below).
Displayr supports a wide range of sources and import methods, including Excel/CSV, SPSS, SQL, AWS, Snowflake, R, and Google Drive, as well as other cloud storage. Since you can import data using R, you can import data from APIs using custom code as well. To add data, click Data Sources > Plus (+) and find the best method. The full list of supported files and more details are available in How to Import Data into Displayr. The GIF below is an example of how easy it is to import a file of data from your computer:
If needed, Displayr has built-in, no-code functions to set relationships between data sources, merge and stack (turn wide files into long files), and export your data.
Working with variables
Your raw data source in Displayr will be imported and ready to use in the Data Sources pane in the lower left of your screen. Displayr determines how to analyze variables based on their Variable Set Structure and Value Attributes, and uses metadata from your data source (e.g., .sav files) to automatically select the appropriate configuration. You can refer to the table in Variable Sets for reference on how these sets are interpreted by Displayr.
For other data sources without built-in metadata, such as Excel/CSV files and SQL queries, Displayr makes an educated guess about how the data should be structured. If using a data source without metadata, you should review How to Work with Excel/CSV Files in Displayr for tips on how to format the data values and configure variables with those file types.
If the data isn't organized quite right, you can manipulate, combine, and split variable sets with a couple of clicks.
You can also create custom variables to use for filtering, weighting, calculations, and other categorizations. Our Variables & Variable Sets section of our Help Center lists various automations for doing this, or you could write your own custom R or JavaScript code to do this. Variables can be used as a source for all analyses in Displayr, but sometimes your data may already be calculated and ready to be analyzed in a Data Table (next section).
Working with already calculated Data Tables
If your data is formatted as tables that are already calculated to show a summary of data, the Data Sources pane will contain lists of the tables from the Excel document. These tables can be used directly as a table, as an input to a visualization, or in a custom R Calculation on the Page. See here for more about using Data Tables that were already calculated as a data source.
There are also built-in functions to combine and reformat the table data on the Page. Of course, you can also perform these tasks within a custom R calculation, if preferred.
Building outputs (tables, charts, and more)
Once you have your data formatted to your liking, you can use it to create a wide array of outputs using built-in widgets for tables, visualizations, and other advanced analyses. Or you could build your own custom output using R (see next section). It's as easy as dragging data onto the page and selecting how to present it.
To learn more about these kinds of outputs, start here:
Creating custom items using R in Displayr
Displayr is integrated with R via R servers on the cloud, so you don't need to worry about installing R on any computer. See How R Works Differently in Displayr. In Displayr, you can use R code to create custom items individually, like Variables, Calculations (outputs in the work area), R functions, and Data Sets. Many of the items created from the menus are also R-based, and you are able to see and edit the Standard R code written by Displayr, see How to Use R in Displayr.
To build outputs on the Page using R, create a new custom Calculation by clicking Calculation > Custom Code from the toolbar. Then click on the page to position it. The R code editor will open at the bottom of the screen, and you can enter your R Code there. To use other items in your document, you can simply click on them to insert their reference into the code. More useful tips can be found in this article. The gif below shows how easy it is to make a simple, custom table using R.
To learn more about what you can do in R, see:
- How to Learn R - for a list of our most helpful resources and common examples.
- How to Reference Different Items in Your Document in R - for a quick guide on getting data into your code.
- How to Troubleshoot R Code in Displayr - is specifically tailored to troubleshooting R code within Displayr given how R is set up differently in the software.
- For more, there is an entire section devoted to R coding in our Help Center.
Designing your Data App
Part of why Displayr rocks is that, instead of writing mountains of code to create, position, and tie everything together, you can use drag and drop to create templates for a consistent feel and design the layout of your data app visually without fiddling with any code.
We've outlined other things to consider in Customizing Online Reports/Dashboards. For instance, you can create a data app across multiple pages or on a single page, and use different methods of navigating and interacting with items.
Adding interactivity
You can add inputs, known as controls in Displayr, on the page to set up interactive filtering to analyze your data for different groups. These controls can also be inputs to built-in analysis and other custom R calculations on the page, such as creating simulators. Whenever a control is changed, this new value triggers all of the outputs downstream to automatically calculate, without any buttons! See Work with Controls for Interactive Outputs for more ways to make your data app interactive.
Deploying your Data App
An app (Document) built in Displayr can be deployed (referred to as published in Displayr) with the click of a couple of buttons -- no more security measures to work through, static documents that become out-of-date, or servers to spin up. Apps are hosted on our servers, and we have automated the whole deployment process for you. Simply go to Share > Publish to Web, and you can immediately share your app with users. The gif below shows how you can create an interactive data app that's ready to share in just a few minutes with Displayr.
Need more security? You can also require your viewers to login to access. Full details on how the access rights work and how to set it up can be found in How to Publish a Document as a Web Page (Dashboard). By using Displayr's login system to grant access, you can also customize the content shown to them in the app, see How to Manage User Groups and Permissions in Displayr. No need to make multiple versions for multiple people.
Updating your Data App
It's very simple to update the data in your app within Displayr. Just click on the Data Source in the Data Sources pane, and in the object inspector , click Update. Building on that, there are some other awesome features that make updating your data app a breeze.
- There is a point and click Schedule system where you can schedule automatic updates to data sources and individual items in your app. You can even have the data app republished to the "live" version automatically after the entire update and recalculation process is completed.
- Items dependent on the updated data are automatically recalculated and are recalculated in parallel where appropriate, speeding up the time to reproduce the final product.
- When a data set is updated, a reconciliation process is triggered, which compares variables between the old and new data. Any structural data differences (i.e., missing and new variables and variable type changes) are identified, and a list is provided to you for your review so you can identify and address any data issues immediately. See How to Update a Data Set in Displayr for details.
- If any issues come up after updating, you can use our dependency graph feature. It allows you to visually see the map of how data flows in your data app, allowing you to troubleshoot errors and find bottlenecks more efficiently. Plus, because the published "live" version of the app is a snapshot, you can fix any problems in the update before they make their way to the version your users see.