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#' Logarithmic Transformation
#'
#' `step_log` creates a *specification* of a recipe step
#' that will log transform data.
#'
#' @inheritParams step_center
#' @param base A numeric value for the base.
#' @param offset An optional value to add to the data prior to
#' logging (to avoid `log(0)`).
#' @param columns A character string of variable names that will
#' be populated (eventually) by the `terms` argument.
#' @param signed A logical indicating whether to take the signed log.
#' This is sign(x) * abs(log(x)) when abs(x) => 1 or 0 if abs(x) < 1.
#' If `TRUE` the `offset` argument will be ignored.
#' @template step-return
#' @family individual transformation steps
#' @export
#' @details
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns
#' `terms` (the columns that will be affected) and `base`.
#'
#' @template case-weights-not-supported
#'
#' @examples
#' set.seed(313)
#' examples <- matrix(exp(rnorm(40)), ncol = 2)
#' examples <- as.data.frame(examples)
#'
#' rec <- recipe(~ V1 + V2, data = examples)
#'
#' log_trans <- rec %>%
#' step_log(all_numeric_predictors())
#'
#' log_obj <- prep(log_trans, training = examples)
#'
#' transformed_te <- bake(log_obj, examples)
#' plot(examples$V1, transformed_te$V1)
#'
#' tidy(log_trans, number = 1)
#' tidy(log_obj, number = 1)
#'
#' # using the signed argument with negative values
#'
#' examples2 <- matrix(rnorm(40, sd = 5), ncol = 2)
#' examples2 <- as.data.frame(examples2)
#'
#' recipe(~ V1 + V2, data = examples2) %>%
#' step_log(all_numeric_predictors()) %>%
#' prep(training = examples2) %>%
#' bake(examples2)
#'
#' recipe(~ V1 + V2, data = examples2) %>%
#' step_log(all_numeric_predictors(), signed = TRUE) %>%
#' prep(training = examples2) %>%
#' bake(examples2)
step_log <-
function(recipe,
...,
role = NA,
trained = FALSE,
base = exp(1),
offset = 0,
columns = NULL,
skip = FALSE,
signed = FALSE,
id = rand_id("log")) {
add_step(
recipe,
step_log_new(
terms = enquos(...),
role = role,
trained = trained,
base = base,
offset = offset,
columns = columns,
skip = skip,
signed = signed,
id = id
)
)
}
step_log_new <-
function(terms, role, trained, base, offset, columns, skip, signed, id) {
step(
subclass = "log",
terms = terms,
role = role,
trained = trained,
base = base,
offset = offset,
columns = columns,
skip = skip,
signed = signed,
id = id
)
}
#' @export
prep.step_log <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
step_log_new(
terms = x$terms,
role = x$role,
trained = TRUE,
base = x$base,
offset = x$offset,
columns = col_names,
skip = x$skip,
signed = x$signed,
id = x$id
)
}
#' @export
bake.step_log <- function(object, new_data, ...) {
check_new_data(names(object$columns), object, new_data)
col_names <- object$columns
# for backward compat
if (all(names(object) != "offset")) {
object$offset <- 0
}
if (!object$signed) {
for (i in seq_along(col_names)) {
new_data[, col_names[i]] <-
log(new_data[[col_names[i]]] + object$offset, base = object$base)
}
} else {
if (object$offset != 0) {
rlang::warn("When signed is TRUE, offset will be ignored")
}
for (i in seq_along(col_names)) {
new_data[, col_names[i]] <-
ifelse(abs(new_data[[col_names[i]]]) < 1,
0,
sign(new_data[[col_names[i]]]) *
log(abs(new_data[[col_names[i]]]), base = object$base)
)
}
}
new_data
}
print.step_log <-
function(x, width = max(20, options()$width - 31), ...) {
msg <- ifelse(x$signed, "Signed log", "Log")
title <- glue::glue("{msg} transformation on ")
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_log <- function(x, ...) {
out <- simple_terms(x, ...)
out$base <- x$base
out$id <- x$id
out
}
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