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.