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loopify step_profile() #1177

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merged 2 commits into from
Aug 1, 2023
Merged

loopify step_profile() #1177

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5abcc93
loopify step_profile()
EmilHvitfeldt Jul 31, 2023
5e55cfc
Update R/profile.R
EmilHvitfeldt Aug 1, 2023
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23 changes: 15 additions & 8 deletions R/profile.R
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Original file line number Diff line number Diff line change
Expand Up @@ -200,17 +200,24 @@ prep.step_profile <- function(x, training, info = NULL, ...) {

#' @export
bake.step_profile <- function(object, new_data, ...) {
n <- length(object$profile[[1]])
new_data <- new_data[rep(1, n), ]
keepers <- c(names(object$columns), names(object$profile))
# Keep the predictors in the same order
keepers <- names(new_data)[names(new_data) %in% keepers]
new_data <- dplyr::select(new_data, !!keepers)
col_names <- names(object$columns)
profile_names <- names(object$profile)
keepers <- c(col_names, profile_names)

for (i in names(object$columns)) {
new_data[[i]] <- rep(object$columns[[i]], n)
new_data <- list()
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I found it easier to create a empty list, fill it, order it, tibblify it. Than to modify the existing tibble, order it making sure the lengths matched


for (col_name in col_names) {
new_data[[col_name]] <- object$columns[[col_name]]
}

new_data[[names(object$profile)]] <- object$profile[[1]]

new_data <- vctrs::vec_recycle_common(!!!new_data)
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@EmilHvitfeldt EmilHvitfeldt Jul 31, 2023 •
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This function is so cool! We can avoid doing rep(value, n)

aaa <- list(1, 2, 4, 1:5)

vctrs::vec_recycle_common(!!!aaa)
#> [[1]]
#> [1] 1 1 1 1 1
#> 
#> [[2]]
#> [1] 2 2 2 2 2
#> 
#> [[3]]
#> [1] 4 4 4 4 4
#> 
#> [[4]]
#> [1] 1 2 3 4 5

new_data <- tibble::new_tibble(new_data)

# Keep the predictors in the same order
keepers <- names(new_data)[names(new_data) %in% keepers]
new_data <- new_data[keepers]
new_data
}

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