No Clocks

Compared to event-based programming, reactivity allows Shiny to do the minimum amount of work when input(s) change, and allows humans to more easily reason about complex MVC logic.

An attractive default look based on Bootstrap which can also be easily customized with the bslib package or avoided entirely with more direct R bindings to HTML/CSS/JavaScript.

Tools for improving and monitoring performance, including native support for async programming, caching, load testing, and more.

observeEvent() is used to perform an action in response to an event eventReactive() is used to create a calculated value that only updates in response to an event

observe() and reactive() functions automatically trigger on whatever they access observeEvent() and eventReactive() functions need to be explicitly told what triggers them

And where does isolate fit in all this? isolate() is used to stop a reaction observeEvent() is used to perform an action in response to an event eventReactive() is used to create a calculated value that only updates in response to an event

You have two fact tables that differ only in terms of granularity. Your Fact_Leases table, for example, is a fact table at the granularity of a lease. I can assume this quite safely because it appears the Lease ID column is a primary key. Each row of that table represents a lease.

On the other hand, your ?_Valuations table is a fact table at the granularity of quarter-time-building. That is, each row not only represents a building but also a quarter time period. And one way you can sort of know that this is a fact table is by understanding that if you had a date-dimension table, you could relate the two on their Quarter columns (although it would be a many-to-many relationship). Therefore, your date-DIMENSION table would be explaining the facts of your valuations. (I'd recommend, however, replacing your Quarter column with actual dates, and allow the date-dimension table to inform the quarters. That's an aside, though.)

Now, the problem of repeating valuation metrics occurs because you are trying to combine two fact tables at different levels of granularity. When you try to apply the valuations to the Fact_Leases table, which is at the granularity of lease, Power BI (or any BI tool, for that matter) can't understand how to apportion the valuation at the BUILDING level down to the LEASE level of granularity. So it just repeats. And it's important to keep this in mind when developing your reporting. No visualizations built at the context level of lease will be able to include a valuation metric because valuations exist only at a higher level of granularity.

Create .Renviron file Within the get_data.R script of my repository, I extract my EIA API key from my R environment so that I can connect to the EIA API and pull the data needed for my project. In order for this to occur during my workflow, I need to create an .Renviron file within my virtual environment and store the key within that environment. - name: Create and populate .Renviron file run: | echo EIA_API_KEY="$EIA_API_KEY" >> ~/.Renviron shell: bash

Using Crosstalk to Add User-Interactivity

The goal is to link the reactable table I created to a plotly chart and provide additional filter options that control both the table and the chart.

An important note: in order to use crosstalk, you must create a shared dataset and call that dataset within both plotly and reactable. Otherwise, your dataset will not communicate and filter with eachother. The code to do this is SharedData$new(dataset).

If you expand the code below, you’ll see that the code to build a table in reactable is quite extensive. I will not go into the details in this post, but do recommend a couple great tutorials that I used to create the interactive table such as this tutorial from Greg Lin, and this from Tom Mock which really helped me understand how to use CSS and Google fonts to enhance the visual appeal of the table (see the “Additional CSS Used for Table” section below for more info).

If you have ever built something in Shiny before, you’ll notice that the crosstalk filters are very similar. You can add a filter to any existing column in the dataset. As you can see in the code below, I used a mixture of filter_checkbox and filter_select depending on how many unique options were available in the column you’re filtering. My rule of thumb is if there are more than five options to choose from it’s probably better to put them into a list in filter_select like I did with the Division filtering as to not take up too much space on the page.

For the layout of the data visualization, I used bscols to place the crosstalk filters side-by-side with the interactive plotly chart. I then placed the reactable table underneath and added a legend to the table using tags from the htmltools package. The final result is shown below. Feel free to click around and the filters and you will notice that both the plot and the table will filter accordingly. Another option is to drag and click on the plot and you will see the table underneath mimic the teams shown.

The OpenAPI Specification Explained The OpenAPI Specification is the ultimate source of knowledge regarding this API description format. However, its length is daunting to newcomers and makes it hard for experienced users to find specific bits of information. This chapter provides a soft landing for readers not yet familiar with OpenAPI and is organized by topic, simplifying browsing. The following pages introduce the syntax and structure of an OpenAPI Description (OAD), its main building blocks and a minimal API description. Afterwards, the different blocks are detailed, starting from the most common and progressing towards advanced ones. Structure of an OpenAPI Description: JSON, YAML, openapi and info API Endpoints: paths and responses. Content of Message Bodies: content and schema. Parameters and Payload of an Operation: parameters and requestBody. Reusing Descriptions: components and $ref. Providing Documentation and Examples: description and example/examples. API Servers: servers.

Introduction to OpenAPI Overlay Specification The Overlay Specification defines a document format for information that transforms an existing OpenAPI description yet remains separate from the OpenAPI description’s source document(s). The Overlay Specification defines a mechanism for providing consistent, deterministic updates to a given OpenAPI description, as an aid to automation throughout the API lifecycle. An Overlay can be applied to an OpenAPI description, resulting in an updated OpenAPI description. OpenAPI + Overlays = (better) OpenAPI One Overlay might be specific to one OpenAPI description, or general enough to be used with multiple OpenAPI descriptions. Equally, one OpenAPI description pipeline might apply different Overlays during the workflow.

Use cases for Overlays Overlays support a range of scenarios, including: Translating documentation into another language Providing configuration information for different deployment environments Allowing separation of concerns for metadata such as gateway configuration or SLA information Supporting a traits-like capability for applying a set of configuration data, such as multiple parameters or multiple headers, for targeted objects Providing default responses or parameters where they were not explicitly provided Applying configuration data globally or based on filter conditions

Resources for working with Overlays The GitHub repository for Overlays is the main hub of activity on the Overlays project. Check the issues and pull requests for what is currently in progress, and the discussions for details of future ideas and our regular meetings. The project maintains a list of tools for working with Overlays.

Example: Add multiple parameters to selected operations One of the most requested features for OpenAPI is the ability to group parameters and easily apply all of them together, to either some or all operations in an OpenAPI description. Especially for common parameters that always come as a set (pagination or filter parameters are a great example), it can be more maintainable to use them as a “trait” and apply the set as part of the API lifecycle rather than trying to maintain a source of truth with a lot of repetition. This approach leads to good API governance, since if the collection of fields changes then the update is consistently applied through automation. In the following example, any operations with the extension x-supports-filters set to true will have two inline parameters added to their parameter collection, and an x-filters-added tag for decoration/debugging.

Example: add a license Every API needs a license so people know they can use it, but what if your OpenAPI descriptions don’t have a license? This example shows an Overlay that adds a license to an OpenAPI description. Here’s the Overlay file, with just one action to add or change the info.license fields: overlay: 1.0.0 info: title: Add MIT license version: 1.0.0 actions: - target: '$.info' update: license: name: MIT url: https://opensource.org/licenses/MIT You can use this Overlay with different OpenAPI files to make the same change to a batch of files.

Example: tag DELETE operations To add the same tag to all operations in an OpenAPI description that use DELETE methods, use an Overlay like the example below. This example adds an x-restricted tag to all delete operations: overlay: 1.0.0 info: title: Tag delete operations as restricted version: 1.0.0 actions: - target: $.paths.*.delete update: tags: - x-restricted This overlay adds x-restricted to the tags array for each delete operation. If the tags array doesn’t exist, it’ll be created; if it does, the new tag is added to the existing array. You can use an approach like this to make other changes to all matching operations.

Resolve References A reference is said to be resolved within a tool if: Its target has been identified Any modifications to the target required by the OAS (e.g. because of fields adjacent to "$ref") have been performed on a copy of the target The resulting target value has been associated with the reference source in some way that the tool can easily use when needed A reference is said to be removed if it has been replaced by its (possibly modified) target. Reference resolution usually preserves the referencing information such as the URI used to identify the target, while reference removal usually discards it. In many cases this is not significant, except that not knowing how the parsed OAD relates to the references in your JSON or YAML document may make debugging more difficult. While plain JSON documents form a tree structure, an OpenAPI Description with resolved references is not necessarily a tree, but a graph. Tools that resolve references in-memory and work with the graph structure can process all OADs. Tools that rely on reference removal, either as part of the tool or by a separate pre-processing tool, can only support OADs that form trees.

What are references? A reference is a keyword and value that identifies a reference target with a URI. In some cases, this URI can be treated as a URL and de-referenced directly. In other cases, as we will see in the (forthcoming) guide to resolving references, it is helpful to separate the target’s identity from its location. External references are how multiple documents are linked into a single OpenAPI Description (OAD). This means that referencing impacts how other linkages, such as those that use Components Object names, or values such as operationId in the Path Item Object, work. These other linkages can only work if the document (or with many tools, the specific JSON object) containing the name or other identifier has been referenced.

A taxonomy of references References exist in several variations in the OpenAPI Specification (OAS) versions 3.0 and 3.1, as shown in the following table. Note that an adjacent keyword is a keyword in the same JSON Object (whether it is written in JSON or YAML) as the reference keyword.