R/data.R

In ferrn: Facilitate Exploration of touRR optimisatioN

#' Simulated data
#'
#' The \code{boa} data is simulated using different Gaussian mixtures with varied centres and weights  (see section format for the simulation code).
#' Theses data are simulated for demonstrating the usage of four diagnostic plots in the package,
#' users can create their own guided tour data objects and diagnose with the visualisation designed in this package.
#'
#' The prefix \code{holes_*} indicates the use of holes index in the guided tour. The suffix \code{*_better/geo} indicates the optimiser used: \code{search_better} and \code{search_geodesic}.
#'
#' The name \code{boa} comes from the fact that the density plot of each variable in the data looks like boa constrictors swallowing multiple French baguettes, rather than elephants, as in the novella the little prince.
#'
#'
#'
#' @format
#' The code for simulating each data object is as follows:
#' \preformatted{
#' set.seed(123456);
#' holes_1d_geo <-
#'   animate_dist(boa5, tour_path = guided_tour(holes(), d = 1,
#'                                              search_f =  search_geodesic),
#'                rescale = FALSE)
#'
#' set.seed(123456)
#' holes_1d_better <-
#'   animate_dist(boa5, tour_path = guided_tour(holes(), d = 1,
#'                                              search_f =  search_better),
#'                rescale = FALSE)
#'
#'
#' set.seed(123456)
#' holes_2d_better <-
#'   animate_xy(boa6, tour_path = guided_tour(holes(), d = 2,
#'                                            search_f =  search_better),
#'             rescale = FALSE)
#'
#' set.seed(123456)
#' holes_2d_better_max_tries <-
#'   animate_xy(boa6, tour_path = guided_tour(holes(), d = 2,
#'                                            search_f = search_better,
#'                                            max.tries = 500),
#'              rescale = FALSE)
#'
#' library(tidyverse)
#' set.seed(1234)
#' x1 <- rnorm(1000, 0, 1)
#' x2 <- sample(c(rnorm(500, -3, 1), rnorm(500, 3, 1)), size = 1000)
#' x3 <- sample(c(rep(-1, 500), rep(1, 500)), size = 1000)
#' x4 <- sample(c(rnorm(250, -3, 1), rnorm(750, 3, 1)), size = 1000)
#' x5 <- sample(c(rnorm(330, -5, 1), rnorm(340, 0, 1), rnorm(330, 5, 1)), size = 1000)
#' x6 <- sample(c(rnorm(450, -5, 1), rnorm(100, 0, 1), rnorm(450, 5, 1)), size = 1000)
#' x7 <- sample(c(rnorm(500, -5, 1), rnorm(500, 5, 1)), size = 1000)
#' x8 <- rnorm(1000, 0, 1)
#' x9 <- rnorm(1000, 0, 1)
#' x10 <- rnorm(1000, 0, 1)
#'
#' boa <- tibble(x1 = x1, x2 = x2, x3 = x3, x4 = x4, x5 = x5,
#'               x6 = x6, x7 = x7, x8 = x8, x9 = x9, x10 = x10) %>%
#' boa <- as_tibble(scale(boa))
#' boa5 <- select(boa, x1, x2, x8: x10)
#' boa6 <- select(boa, x1, x2, x7: x10)
#' }
#'
#' @examples
#' library(ggplot2)
#' library(tidyr)
#' library(dplyr)
#' boa %>%
#'   pivot_longer(cols = x1:x10, names_to = "var", values_to = "value") %>%
#'   mutate(var = forcats::fct_relevel(as.factor(var), paste0("x", 1:10))) %>%
#'   ggplot(aes(x = value)) +
#'   geom_density() +
#'   facet_wrap(vars(var))
#' @rdname data
"holes_1d_geo"

#' @format
#' @rdname data
"holes_1d_better"

#' @format
#' @rdname data
"holes_2d_better"

#' @format
#' @rdname data
"holes_2d_better_max_tries"

#' @format
#' @rdname data
"boa"

#' @format
#' @rdname data
"boa5"

#' @format
#' @rdname data
"boa6"