R/path-andrews.r

In tourr: Tour Methods for Multivariate Data Visualisation

#' Draw the path that the geodesics took.
#'
#' This computes the projected values of each observation at each step, and
#' allows you to recreate static views of the animated plots.
#'
#' @param history list of bases produced by \code{\link{save_history}}
#'   (or otherwise)
#' @param data dataset to be projected on to bases
#' @export
#' @examples
#' path1d <- save_history(flea[, 1:6], grand_tour(1), 3)
#' path2d <- save_history(flea[, 1:6], grand_tour(2), 3)
#'
#' if (require("ggplot2")) {
#'   plot(path_curves(path1d))
#'   plot(path_curves(interpolate(path1d)))
#'
#'   plot(path_curves(path2d))
#'   plot(path_curves(interpolate(path2d)))
#'
#'   # Instead of relying on the built in plot method, you might want to
#'   # generate your own.  Here are few examples of alternative displays:
#'
#'   df <- path_curves(path2d)
#'   ggplot(data = df, aes(x = step, y = value, group = obs:var, colour = var)) +
#'     geom_line() +
#'     facet_wrap(~obs)
#'
#'   library(tidyr)
#'   ggplot(
#'     data = pivot_wider(df,
#'       id_cols = c(obs, step),
#'       names_from = var, names_prefix = "Var",
#'       values_from = value
#'     ),
#'     aes(x = Var1, y = Var2)
#'   ) +
#'     geom_point() +
#'     facet_wrap(~step) +
#'     coord_equal()
#' }
path_curves <- function(history, data = attr(history, "data")) {
  history <- as.list(history)
  n <- length(history)

  project <- function(basis) {
    proj <- data %*% basis
    data.frame(
      obs = factor(row(proj)),
      var = factor(col(proj)),
      value = as.vector(proj)
    )
  }
  projections <- do.call("rbind", lapply(history, project))
  projections$step <- rep(seq_len(n), each = nrow(data) * ncol(history[[1]]))
  class(projections) <- c("path_curve", class(projections))

  projections
}

#' Plot history curves.
#'
#' The default plot method is a line plot with step on the x axis and
#' value on the y axis.  Each observation is drawn with a different line
#' line and the plot is facetted by variable.  This is rather similar in
#' spirit to a parallel coordinates plot or Andrews curves.
#'
#' For alternative ways of plotting this data, see
#'  \code{\link{path_curves}}
#' @keywords internal
#' @export
plot.path_curve <- function(x, ...) {
  step <- NULL # quiet R CMD check warning

  ggplot2::ggplot(data = x, ggplot2::aes(x = step, y = value, group = obs)) +
    ggplot2::geom_line() +
    ggplot2::facet_grid(var ~ .)
}
# globalVariables(c("value", "obs"))