Can't imagine doing it any other way

And that's on top of the vastly simpler syntax compared to what's being shown here

2. I check my ontology for the answer, probably in a dashboard, and it takes 5 seconds. I have a link I can freely share around my enterprise and I haven't spent token costs.

Whats more, when I have sent my agent out to some tasks (go find out what revenue we're leaving on the table by not selling spot contracts to our biggest customers) my ontology gives me a few bits of data to validate the agents work against. For humans and AI to work together, they need the same context layer

1. While AI is capable of driving massive value, chatbots are very rarely the solution

2. You need much more than this sort of text data to represent an enterprise. Timeseries, SAP (and other ERPs), and general relational data is part of building a knowledge graph, ontology, etc

3. Storing it the way this article presents makes it usable for agents, but not humans. Whereas the point of knowledge graph, ontology, etc is to create the same layer for both humans and AI to interact with

I can write code like: penguin_sizes <- select(penguins, weight, height)

Here, weight and height are columns inside the dataframe. But I can refer to them as if they were objects in the environment (I., e without quotes) because the select function looks for them inside the penguins dataframe (it's first argument)

This is a very simple example but it's used extensively in some R paradigms

> RMCP has been tested with real-world scenarios achieving 100% success rate:

1. It's easier to get up and running as RStudio is much more 'batteries included' than other popular IDEs, it's harder to get into the case of multiple different python versions, and you install packages through the R interpreter rather than via pip at the command line

2. I would say R data analysis packages are easier to learn than the python equivalents. Because the dataframe is a native structure in R there has been a lot more packages that have tried alternative syntax approaches to try and find the 'optimal' one. Python has really only had pandas, polars, and pyspark (all of which have implemented their own data structures and therefore have focused more on performance than syntax)

3. This doesn't hold if you're studying a language to be a general purpose programmer. Then python is much better. Anything to avoid the hell of the R standard lib. But if you need to do a bit of coding to analyse data and you've never done any before, my vote would be for R.

However, these are thoughts from my own personal anecdotes rather than any pedagogical theory

I have yet to see any software that rivals dplyr, data.table, and ggplot2 in the balance of power and ease of use. It also has all the auxiliary packages you need to fetch your data (DBI, httr, rvest), model it if necessary (parsnip, caret) and visualise it (ggplot2, plotly, shiny)

I know python is more popular here but I would choose R in a heartbeat 19 times out of 20

Been playing on and off for 20 years and have only managed a single ascension in that time!

My assumption would be that if you haven't seen anything, even from the creator, then you just have different expectations to the people who do get value from React

NIWA is also just a very cool place in general. Have really liked everyone I've met from there.