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Besides the app, there are a couple of more ways to interact with the
LLM via chattr
:
chattr()
functionBased on where you are making the request from, chattr
will return the response appropriately in the following manner:
If the request is made from the R console, the
response will be returned as a a console message, not R code
(cat()
). This way, you can decide to copy-paste the code to
run. It will also prevent any comments from the LLM to cause an
error.
If the request is made from a script, or a code chunk in Quarto (Visual editor), the output will be inserted right after your prompt in the same document.
chattr()
functionThe fastest way to interact with the LLM is by simply calling the
chattr()
function and enter your request there. Here is an
example of a request made to OpenAI:
# Load required packages
library(tidymodels)
# Create a simple recipe for the iris dataset
iris_recipe <- recipe(Species ~ ., data = iris)
# Print the recipe
iris_recipe
In a script, chattr
will process the current line, or
the highlighted line(s), as the prompt. Because we assume that the
request is not going to be code, then chattr
will comment
out the line or lines that were highlighted. As mentioned in the
Output section, the response will be inserted in your
document.
Here is an example of a request submitted to OpenAI:
And here are the results:
# Create a function that:
# - Removes specified variables
# - Behaves like dplyr's select function
# This function removes specified variables and behaves like dplyr's select function
# It uses the tidyverse packages: dplyr and tidyr
remove_vars <- function(data, ...) {
data %>%
select(-one_of(...))
}
}
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.