add more in-built data

.Rbuildignore

@@ -11,3 +11,4 @@
 ^codemeta\.json$
 ^cran-comments\.md$
 ^CRAN-RELEASE$
+^data-raw$

NAMESPACE

@@ -52,6 +52,7 @@ importFrom(cli,cli_abort)
 importFrom(ggplot2,"%+replace%")
 importFrom(magrittr,"%>%")
 importFrom(rlang,.data)
+importFrom(rlang,`:=`)
 importFrom(tidyr,unnest)
 importFrom(tourr,basis_random)
 importFrom(tourr,interpolate)

R/calc-smoothness.R

@@ -44,4 +44,4 @@ calc_smoothness <- function(idx, data = sine1000, n_basis = 300, n = 6, d = 2,
 }
 
 
-globalVariables(c("basis"))
+globalVariables(c("basis", "sine1000"))

R/data-pipe-sine.R

@@ -0,0 +1,42 @@
+#' Simulated sine, pipe, and gaussian mixture
+#'
+#' Simulated sine and pipe data for calculating optimisation features.
+#' Each dataset has 1000 observations and the last two columns contain the
+#' intended structure with the rest being noise.
+#' @rdname pipe-sine-boa
+#' @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))
+#'
+#' sine1000 |> ggplot(aes(x = V5, y = V6)) + geom_point() + theme(aspect.ratio = 1)
+#' pipe1000_8d |> ggplot(aes(x = V5, y = V6)) + geom_point() + theme(aspect.ratio = 1)
+#' pipe1000_8d |> ggplot(aes(x = V7, y = V8)) + geom_point() + theme(aspect.ratio = 1)
+"sine1000"
+
+#' @rdname pipe-sine-boa
+"pipe1000"
+
+#' @rdname pipe-sine-boa
+"pipe1000_8d"
+
+#' @rdname pipe-sine-boa
+"pipe1000_10d"
+
+#' @rdname pipe-sine-boa
+"pipe1000_12d"
+
+#' @rdname pipe-sine-boa
+"boa"
+
+#' @rdname pipe-sine-boa
+"boa5"
+
+#' @rdname pipe-sine-boa
+"boa6"

R/data.R

@@ -1,104 +1,27 @@
-#' Simulated data
+#' Data objects collected during the projection pursuit optimisation
 #'
-#' 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,
+#' Simulated data to demonstrate 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_1d_jellyfish <- animate_dist(
-#' boa5, tour_path = guided_tour(
-#' holes(), d = 1, search_f = search_jellyfish, n_jellies = 100,
-#' max.tries = 50), 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)
-#' }
+#' The prefix \code{holes_*} indicates the use of holes index in the guided tour.
+#' The suffix \code{*_better/geo/jellyfish} indicates the optimiser used:
+#' \code{search_better}, \code{search_geodesic}, \code{search_jellyfish}.
 #'
 #' @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))
+#'holes_1d_better %>%
+#' explore_trace_interp(interp_size = 2) +
+#' scale_color_continuous_botanical(palette = "fern")
 #' @rdname data
 "holes_1d_geo"
 
-#' @format
 #' @rdname data
 "holes_1d_better"
 
-#' @format
 #' @rdname data
 "holes_1d_jellyfish"
 
-#' @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"

R/ferrn-package.R

@@ -3,4 +3,5 @@
 #' @importFrom GpGp fit_model
 #' @importFrom cli cli_abort
 #' @importFrom tidyr unnest
+#' @importFrom rlang `:=`
 "_PACKAGE"

data-raw/holes.R

@@ -0,0 +1,49 @@
+#' code to prepare `holes` dataset goes her
+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_1d_jellyfish <- animate_dist(boa5, tour_path = guided_tour(
+ holes(), d = 1, search_f = search_jellyfish, n_jellies = 100,
+ max.tries = 50), 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)
+
+usethis::use_data(holes_1d_geo, overwrite = TRUE)
+usethis::use_data(holes_1d_better, overwrite = TRUE)
+usethis::use_data(holes_1d_jellyfish, overwrite = TRUE)
+usethis::use_data(holes_2d_better, overwrite = TRUE)
+usethis::use_data(holes_2d_max_tries, overwrite = TRUE)
+usethis::use_data(boa, overwrite = TRUE)
+usethis::use_data(boa5, overwrite = TRUE)
+usethis::use_data(boa6, overwrite = TRUE)
+

data-raw/pipe-sine.R

@@ -0,0 +1,27 @@
+## code to prepare `pipe-sine` dataset goes here
+library(spinebil) # devtools::install_github("uschiLaa/spinebil")
+set.seed(123456)
+sine1000 <- spinebil::sinData(6, 1000) %>% scale()
+colnames(sine1000) <- paste0("V", 1:6)
+
+set.seed(123456)
+pipe1000 <- spinebil::pipeData(6, 1000) %>% scale()
+colnames(pipe1000) <- paste0("V", 1:6)
+
+set.seed(123456)
+pipe1000_8d <- spinebil::pipeData(8, 1000) %>% scale()
+colnames(pipe1000_8d) <- paste0("V", 1:8)
+
+set.seed(123456)
+pipe1000_10d <- spinebil::pipeData(10, 1000) %>% scale()
+colnames(pipe1000_10d) <- paste0("V", 1:10)
+
+set.seed(123456)
+pipe1000_12d <- spinebil::pipeData(12, 1000) %>% scale()
+colnames(pipe1000_12d) <- paste0("V", 1:12)
+
+usethis::use_data(sine1000, overwrite = TRUE)
+usethis::use_data(pipe1000, overwrite = TRUE)
+usethis::use_data(pipe1000_8d, overwrite = TRUE)
+usethis::use_data(pipe1000_10d, overwrite = TRUE)
+usethis::use_data(pipe1000_12d, overwrite = TRUE)

docs/pkgdown.yml

@@ -2,7 +2,7 @@ pandoc: 3.1.1
 pkgdown: 2.0.8
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-05-21T16:44Z
+last_built: 2024-05-21T17:40Z
 urls:
  reference: https://huizezhang-sherry.github.io/ferrn/reference
  article: https://huizezhang-sherry.github.io/ferrn/articles