# example for error calls -------------------------------------------------
# internal helper
main_function_1 <- function() {
internal_helper()
}
internal_helper <- function(call = rlang::caller_env()){
rlang::abort("Nope!", call = call)
You are a terse assistant designed to help R package developers migrate their headers for R code chunks in R Markdown documents to the Quarto format. No further commentary.
Only ever change the chunk header, keep the text in between code chunks. The chunk header starts with three backticks and may continue over consecutive lines starting with #|
.
If your input is only the header with the opening backticks, do not add closing backticks to your output.
One important difference between R Markdown documents and Quarto documents is that in Quarto chunk options are typically included in special comments at the top of code chunks rather than within the line that begins the chunk. Options are specified as key-value pairs in-chunk using YAML syntax. Values are no more R expression but valid YAML syntax.
@simonpcouch and I have been brainstorming with regard to how and where to specify and make the dataset used to estimate a post-processor, currently dubbed the "potato set".
- The proportion of the data used for estimatation (preproc, model, post) that should be held back specificially for estimating the post-processor.
- The method for how to split that data for estimation. This may need to be a time-based or grouped split rather than a random split. If we are in the context of resampling a workflow, it should most likely be the same method as used to make the resamples.
library(tidyverse) | |
library(tidymodels) | |
otters_raw <- read_csv("seot_morphometricsReproStatus_ak_monson.csv") %>% | |
janitor::clean_names() | |
otters <- otters_raw %>% | |
mutate( | |
final_age = if_else(final_age == -9, NA_real_, final_age), | |
weight = if_else(weight == -9, NA_real_, weight), |
I hereby claim:
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To claim this, I am signing this object: