[BUG] `bootstrap_df` and `shuffle_df` fail if `node_col` is a factor

[maouw]

Describe the bug
bootstrap_df and shuffle_df fail if the node_col parameter refers to a column that is of type factor. This happens because of the following lines are trying to turn a character into factor:

  for (var in original_colnames) {
    class(df_bootstrap[[var]]) <- class(df[[var]])
  }

To Reproduce

devtools::load_all()
#> ℹ Loading tractable
readr::read_csv("tests/testthat/afq_12_rows.csv", show_col_types = FALSE) |>
  dplyr::mutate(nodeID = as.factor(nodeID)) |>
  bootstrap_df(target = "dti_fa", node_col = "nodeID") |>
  pillar::glimpse(width = 1)
#> Error in class(df_bootstrap[[var]]) <- class(df[[var]]): adding class "factor" to an invalid object

devtools::load_all()
#> ℹ Loading tractable
readr::read_csv("tests/testthat/afq_12_rows.csv", show_col_types = FALSE) |>
  dplyr::mutate(nodeID = as.factor(nodeID)) |>
  shuffle_df(target = "dti_fa", node_col = "nodeID") |>
  pillar::glimpse(width = 1)
#> Error in class(df_shuffled[[var]]) <- class(df[[var]]): adding class "factor" to an invalid object

Expected behavior
The functions should succeed as below:

devtools::load_all()
#> ℹ Loading tractable
readr::read_csv("tests/testthat/afq_12_rows.csv", show_col_types = FALSE) |>
  bootstrap_df(target = "dti_fa", node_col = "nodeID") |>
  pillar::glimpse(width = 1)
#> Rows: 12
#> Columns: 6
#> $ subjectID <dbl> …
#> $ nodeID    <dbl> …
#> $ dti_fa    <dbl> …
#> $ age       <dbl> …
#> $ sex       <dbl> …
#> $ group     <dbl> …

Desktop (please complete the following information):

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[maouw]

I think what should be happening is that the class of df_bootstrap[[node_col]] result is not the same as the class of the df[[node_col]], as.character(df_bootstrap[[node_col]]) must be coerced into the original class somehow.

I haven't been able to find a really solid way to do this consistently for all objects—methods::as doesn't always work, but it's worth trying. Looking for a function paste0("as.", class(df[[node_col]])[[1]]) can also work in many cases. I believe one thing that should almost always work is a lookup table, but that can be slow.