How to create nested training and testing sets?

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I'm working with the ChickWeight data set in R. I'm looking to create multiple models, each trained for an individual chick. As such, I am nesting the data so that a dataframe is created for each individual chick and stored within the list column.

Here is the start:

library(tidyverse)
library(datasets)
data("ChickWeight")

ChickWeightNest <- ChickWeight %>% 
  group_by(Chick) %>% 
  nest()

From here, training a linear regression model on all dataframes simultaneously is very easy by simply building the model as a function then mutating a new column and mapping. However, building a more sophisticated model (e.g. xgboost) requires first splitting the data into testing and training sets. How can I split my all nested data frames at once to create training and testing sets so that I can train multiple models simultaneously?

As a side note, info on training/tuning multiple models seems to be relatively sparse in my research, any related resources or past stack questions would be very appreciated.



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