% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/harmonic.R
\name{step_harmonic}
\alias{step_harmonic}
\title{Add sin and cos terms for harmonic analysis}
\usage{
step_harmonic(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  frequency = NA_real_,
  cycle_size = NA_real_,
  starting_val = NA_real_,
  keep_original_cols = FALSE,
  columns = NULL,
  skip = FALSE,
  id = rand_id("harmonic")
)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}

\item{...}{One or more selector functions to choose variables
for this step. See \code{\link[=selections]{selections()}} for more details. This will
typically be a single variable.}

\item{role}{For model terms created by this step, what analysis role should
they be assigned? By default, the new columns created by this step from
the original variables will be used as \emph{predictors} in a model.}

\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}

\item{frequency}{A numeric vector with at least one value.
The value(s) must be greater than zero and finite.}

\item{cycle_size}{A numeric vector with at least one value that indicates
the size of a single cycle. \code{cycle_size} should have the same units as the
input variable(s).}

\item{starting_val}{either \code{NA}, numeric, Date or POSIXt value(s) that indicates
the reference point for the sin and cos curves for each input variable.
If the value is a \code{Date} or \code{POISXt} the value is converted to numeric
using \code{as.numeric}. This parameter may be specified to increase control
over the signal phase.  If \code{starting_val} is not specified the default
is 0.}

\item{keep_original_cols}{A logical to keep the original variables in the
output. Defaults to \code{FALSE}.}

\item{columns}{A character string of the selected variable names. This field
is a placeholder and will be populated once \code{\link[=prep]{prep()}} is used.}

\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake]{bake()}}? While all operations are baked
when \code{\link[=prep]{prep()}} is run, some operations may not be able to be
conducted on new data (e.g. processing the outcome variable(s)).
Care should be taken when using \code{skip = TRUE} as it may affect
the computations for subsequent operations.}

\item{id}{A character string that is unique to this step to identify it.}
}
\value{
An updated version of \code{recipe} with the new step added to the
sequence of any existing operations.
}
\description{
\code{step_harmonic()} creates a \emph{specification} of a recipe step that will add
\code{sin()} and \code{cos()} terms for harmonic analysis.
}
\details{
This step seeks to describe periodic components of observational
data using a combination of sin and cos waves. To do this, each wave of a
specified frequency is modeled using one sin and one cos term. The two
terms for each frequency can then be used to estimate the amplitude and
phase shift of a periodic signal in observational data. The equation
relating cos waves of known frequency but unknown phase and amplitude to a
sum of sin and cos terms is below:

\deqn{A_j cos(\sigma_j t_i - \Phi_j) = C_j cos(\sigma_j t_i) + S_j sin(\sigma_j t_i)}

Solving the equation yields \eqn{C_j} and \eqn{S_j}. the
amplitude can then be obtained with:

\deqn{A_j = \sqrt{C^2_j + S^2_j}}

And the phase can be obtained with:
\deqn{\Phi_j = \arctan{(S_j / C_j)}}

where:
\itemize{
\item \eqn{\sigma_j = 2 \pi (frequency / cycle\_size))}
\item \eqn{A_j} is the amplitude of the \eqn{j^{th}} frequency
\item \eqn{\Phi_j} is the phase of the \eqn{j^{th}} frequency
\item \eqn{C_j} is the coefficient of the cos term for the \eqn{j^{th}} frequency
\item \eqn{S_j} is the coefficient of the sin term for the \eqn{j^{th}} frequency
}

The periodic component is specified by \code{frequency} and \code{cycle_size}
parameters. The cycle size relates the specified frequency to the
input column(s) units. There are multiple ways to specify a wave of given
frequency, for example, a \code{POSIXct} input column given a \code{frequency} of
24 and a \code{cycle_size} equal to 86400 is equivalent to a \code{frequency} of
1.0 with \code{cycle_size} equal to 3600.
}
\section{Tuning Parameters}{
This step has 1 tuning parameters:
\itemize{
\item \code{frequency}: Harmonic Frequency (type: double, default: NA)
}
}

\section{Tidying}{
When you \code{\link[=tidy.recipe]{tidy()}} this step, a tibble is returned with
columns \code{terms}, \code{starting_val}, \code{cycle_size}, \code{frequency}, \code{key} , and \code{id}:

\describe{
\item{terms}{character, the selectors or variables selected}
\item{starting_val}{numeric, the starting value}
\item{cycle_size}{numeric, the cycle size}
\item{frequency}{numeric, the frequency}
\item{key}{character, key describing the calculation}
\item{id}{character, id of this step}
}
}

\section{Case weights}{


The underlying operation does not allow for case weights.
}

\examples{
\dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
library(ggplot2, quietly = TRUE)
library(dplyr)

data(sunspot.year)
sunspots <-
  tibble(
    year = 1700:1988,
    n_sunspot = sunspot.year,
    type = "measured"
  ) \%>\%
  slice(1:75)

# sunspots period is around 11 years, sample spacing is one year
dat <- recipe(n_sunspot ~ year, data = sunspots) \%>\%
  step_harmonic(year, frequency = 1 / 11, cycle_size = 1) \%>\%
  prep() \%>\%
  bake(new_data = NULL)

fit <- lm(n_sunspot ~ year_sin_1 + year_cos_1, data = dat)

preds <- tibble(
  year = sunspots$year,
  n_sunspot = fit$fitted.values,
  type = "predicted"
)

bind_rows(sunspots, preds) \%>\%
  ggplot(aes(x = year, y = n_sunspot, color = type)) +
  geom_line()


# ------------------------------------------------------------------------------
# POSIXct example

date_time <-
  as.POSIXct(
    paste0(rep(1959:1997, each = 12), "-", rep(1:12, length(1959:1997)), "-01"),
    tz = "UTC"
  )

carbon_dioxide <- tibble(
  date_time = date_time,
  co2 = as.numeric(co2),
  type = "measured"
)

# yearly co2 fluctuations
dat <-
  recipe(co2 ~ date_time,
    data = carbon_dioxide
  ) \%>\%
  step_mutate(date_time_num = as.numeric(date_time)) \%>\%
  step_ns(date_time_num, deg_free = 3) \%>\%
  step_harmonic(date_time, frequency = 1, cycle_size = 86400 * 365.24) \%>\%
  prep() \%>\%
  bake(new_data = NULL)

fit <- lm(co2 ~ date_time_num_ns_1 + date_time_num_ns_2 +
  date_time_num_ns_3 + date_time_sin_1 +
  date_time_cos_1, data = dat)

preds <- tibble(
  date_time = date_time,
  co2 = fit$fitted.values,
  type = "predicted"
)

bind_rows(carbon_dioxide, preds) \%>\%
  ggplot(aes(x = date_time, y = co2, color = type)) +
  geom_line()
\dontshow{\}) # examplesIf}
}
\references{
Doran, H. E., & Quilkey, J. J. (1972).
Harmonic analysis of seasonal data: some important properties.
American Journal of Agricultural Economics, 54, volume 4, part 1, 646-651.

Foreman, M. G. G., & Henry, R. F. (1989).
The harmonic analysis of tidal model time series.
Advances in water resources, 12(3), 109-120.
}
\seealso{
Other individual transformation steps: 
\code{\link{step_BoxCox}()},
\code{\link{step_YeoJohnson}()},
\code{\link{step_bs}()},
\code{\link{step_hyperbolic}()},
\code{\link{step_inverse}()},
\code{\link{step_invlogit}()},
\code{\link{step_logit}()},
\code{\link{step_log}()},
\code{\link{step_mutate}()},
\code{\link{step_ns}()},
\code{\link{step_percentile}()},
\code{\link{step_poly}()},
\code{\link{step_relu}()},
\code{\link{step_sqrt}()}
}
\concept{individual transformation steps}
