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#' Secondary axis helper
#'
#' The purpose of this function is to construct a secondary axis with a
#' projection function.
#'
#' @param data A `data.frame` object.
#' @param primary,secondary An expression that is evaluated in the context of
#' the `data` argument. These can be symbols for column names or plain
#' expressions.
#' @param method One of the following: \describe{
#' \item{`"range"`}{Causes the ranges of `primary` and `secondary` data to
#' overlap completely.}
#' \item{`"max"`}{Causes the maxima of `primary` and `secondary` data to
#' coincide.}
#' \item{`"fit"`}{Uses the coefficients of `lm(primary ~ secondary)` to make
#' the axes fit.}
#' \item{`"ccf"`}{Uses the lag at which maximum cross-correlation occurs to
#' then align the data by truncation. The aligned data is then passed to the
#' `"fit"` method.}
#' \item{`"sortfit"`}{Sorts the both `primary` and `secondary` independently
#' before passing these on to the `"fit"` method.}
#' }
#' @inheritDotParams ggplot2::sec_axis -trans
#'
#' @details The intent is to run this function before starting a plot. The
#' output of the function is a secondary axis wherein the `trans` argument of
#' `sec_axis()` is populated by an appropriate transformation. In addition,
#' the output also contains a `output$proj()` function that helps transform the
#' secondary data.
#'
#' @return An `AxisSecondary` ggproto object with a `proj` method for projecting
#' secondary data.
#' @export
#' @md
#'
#' @examples
#' # Run the secondary axis helper
#' sec <- help_secondary(economics, primary = unemploy, secondary = psavert)
#'
#' # Making primary plot
#' p <- ggplot(economics, aes(date)) +
#' geom_line(aes(y = unemploy), colour = "blue")
#'
#' # For the secondary data, later we use the `proj` function from the helper
#' p <- p + geom_line(aes(y = sec$proj(psavert)), colour = "red")
#'
#' # We feed the scale the secondary axis
#' p + scale_y_continuous(sec.axis = sec)
#'
#' # Setup cross-correlated data
#' set.seed(42)
#' n <- 100
#' lag <- 20
#' dat <- cumsum(rnorm(n + lag))
#' df <- data.frame(
#' x = seq_len(n),
#' y1 = head(dat, n),
#' y2 = 10 + tail(dat, n) * 5 # offset and scale y2
#' )
#' # Choosing the cross-correlation function method.
#' sec <- help_secondary(df, y1, y2, method = "ccf")
#'
#' ggplot(df, aes(x)) +
#' geom_line(aes(y = y1), colour = "blue") +
#' geom_line(aes(y = sec$proj(y2)), colour = "red") +
#' scale_y_continuous(sec.axis = sec)
help_secondary <- function(
data = NULL,
primary = c(0, 1),
secondary = c(0, 1),
method = c("range", "max", "fit", "ccf", "sortfit"),
...
) {
primary <- enquo(primary)
secondary <- enquo(secondary)
method <- match.arg(method, c("range", "max", "fit", "ccf", "sortfit"))
name <- as_label(secondary)
primary <- eval_tidy(primary, data)
secondary <- eval_tidy(secondary, data)
help <- switch(
method,
"range" = help_sec_range(primary, secondary),
"max" = help_sec_max(primary, secondary),
"fit" = help_sec_fit(primary, secondary),
"ccf" = help_sec_ccf(primary, secondary),
"sortfit" = help_sec_sortfit(primary, secondary)
)
out <- ggproto(
NULL,
new_sec_axis(trans = help$reverse, ...),
proj = help$forward
)
if (inherits(out$name, "waiver")) {
out$name <- name
}
return(out)
}
# This is a workaround to bridge the gap between ggplot 3.4.4 and 3.5.0
new_sec_axis <- function(trans = NULL, ...) {
args <- list2(trans = trans, ...)
if ("transform" %in% fn_fmls_names(ggplot2::sec_axis)) {
names(args)[1] <- "transform"
}
inject(sec_axis(!!!args))
}
# Methods -----------------------------------------------------------------
help_sec_range <- function(from, to) {
from <- range(from)
to <- range(to)
forward <- function(x) {
rescale(x, from = to, to = from)
}
reverse <- function(x) {
rescale(x, from = from, to = to)
}
list(forward = forward, reverse = reverse)
}
help_sec_max <- function(from, to) {
from <- range(from)
to <- range(to)
forward <- function(x) {
rescale_max(x, from = to, to = from)
}
reverse <- function(x) {
rescale_max(x, from = from, to = to)
}
list(forward = forward, reverse = reverse)
}
help_sec_fit <- function(from, to) {
if (length(from) != length(to)) {
cli::cli_abort(
"The primary and secondary values must have the same length."
)
}
fit <- coef(lm(from ~ to))
forward <- function(x) {
fit[1] + x * fit[2]
}
reverse <- function(x) {
(x - fit[1]) / fit[2]
}
list(forward = forward, reverse = reverse)
}
help_sec_ccf <- function(from, to) {
if ({len <- length(from)} != length(to)) {
cli::cli_abort(
"The primary and secondary values must have the same length."
)
}
lag <- ccf(from, to, lag.max = len - 1, plot = FALSE)
lag <- lag$lag[which.max(lag$acf)]
# No block for 0-lag because data is optimal as-is
if (sign(lag) == 1) {
from <- tail(from, -lag)
to <- head(to, -lag)
} else if (sign(lag) == -1) {
from <- head(from, lag)
to <- tail(to, lag)
}
help_sec_fit(from = from, to = to)
}
help_sec_sortfit <- function(from, to) {
help_sec_fit(from = sort(from), to = sort(to))
}
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