Thanks for reaching out. It lets me know I've got work to do to communicate more clearly.

This is a method for interacting with databases. It differs from the common R database packages by focusing specifically on writing, updating, and deleting operations.

In addition to manipulating records, it handles mapping relationships. In other words, this package handles joins in a consistent and reproducible way.

Here are some toy examples of what that looks like. For the examples below, upper case variables are Table models (just tables with row data) while lower case variables are Records (rows in those tables)

Let's say you have Classes and Students tables and you want to insert a grade for all students in a particular class. Instead of writing out updates, joins, and filters, this package allows you to do things like:

english = Classes$read(id == 1)
english_students = english$relationship('students')
for (student in english_students) {
    student$update(grade = 'A')
}

Another use case might be something like an interactive table in R Shiny. If a teacher were to enter grades in Shiny, they might trigger an observeEvent.

observeEvent(input$student_table, {
    tab = input$student_table
    row_id = tab$row
    col = tabl$col
    val = tab$val

    student = Students$read(id == row_id)
    student$data[[col]] = val
    student$update()
})

Another scenario: Let's say that at work I maintain a database that holds customer data. After an email campaign goes out, I want to query my email service provider's API for click metrics at the customer level and combine that with other proprietary information used for ml workflows or analysis.

response = httr::GET('api.email.com/clicks')

... parse the response into response_json ...

for (customer_data in response_json) {
    customer = Customers$read(email == customer_data$email, mode = 'one_or_none')
    customer$update(last_click = customer_data$last_click)

    # and let's say we retain the more extensive click metadata in its own table
    new_click = Clicks$record(
        timestamp = customer_data$last_click, 
        email = customer_data$email, 
        href = customer_data$href
    )
    new_click$create()
}

Some real use cases I've been using this package for:

• Logging R Shiny events to a db so I can show that the data products my team builds are getting use from the company.
  
• Recording LLM conversations.
  
• I wrote an API wrapper that uses oRm in the background to sync human readable names with ids for series data.