R/fviz-gda-trajectory.R

In inventionate/TimeSpaceAnalysis: Statistical tools for time-space analysis

#' @include utilities.R
#' @include add-theme.R
#' @include get-gda-trajectory.R
NULL
#' Visualization of trajectories (connected active and passive individual points).
#'
#' @param res_gda MCA result (rownames have to be questionnaire IDs including time number, e.g. 87654_1).
#' @param select vector of names, within_inertia of individuals selection (within_inertia: vector containing the number of high variation and low variationindividuals) or case (vector containing NULL, complete, or incomplete).
#' @param axes axes to plot.
#' @param myriad use Myriad Pro font (boolean).
#' @param labels plot individual labels (boolean).
#' @param title the plot title
#' @param labels plot labels (boolean).
#' @param time_point_names vector containing the name of the time points.
#' @param plot_modif_rates plot modified rates instead of eigenvalue percentage (boolean).
#' @param axis_lab_name name of axis label.
#'
#' @return trajectory ggplot2 visualization.
#' @export
fviz_gda_trajectory <- function(res_gda, select = list(name = NULL, within_inertia = NULL, case = NULL),
                                title = "Trajectory individuals plot", axes = 1:2, labels = FALSE,
                                myriad = TRUE, time_point_names = NULL, plot_modif_rates = TRUE, axis_lab_name = "Achse") {

  # Add Myriad Pro font family
  if(myriad) .add_fonts()

  # Trajektoriedaten zusammenstellen
  coord_trajectory <- get_gda_trajectory(res_gda, time_point_names)
  coord_all <-  coord_trajectory$coord_all
  time_point_names <- coord_trajectory$time_point_names

  # Auswahl vornehmen
  selected_ind <- .select_trajectory(coord_all, select, time_point_names, axes)

  # Filterung vornehmen
  coord_ind_timeseries <-  coord_all %>% filter(id %in% selected_ind$id)

  # Plot der Daten
  if(inherits(res_gda, c("MCA"))) p <- .create_plot()
  else stop("Only MCA plots are currently supported!")

  p <- p + scale_colour_brewer(palette = "YlGnBu", direction = -1) +
    geom_point(data = coord_ind_timeseries, aes_string(paste0("Dim.", axes[1]), paste0("Dim.", axes[2])), colour = "black", size = 4) +
    geom_point(data = coord_ind_timeseries, aes_string(paste0("Dim.", axes[1]), paste0("Dim.", axes[2]), colour = "time"), size = 2.5) +
    geom_path(data = coord_ind_timeseries, aes_string(paste0("Dim.", axes[1]), paste0("Dim.", axes[2]), group = "id"), size = 1,
              arrow = arrow(length = unit(0.3, "cm"), type = "closed")) +
    ggtitle(title) +
    xlab(paste0("Achse ", axes[1], "(", round(res_gda$eig$`percentage of variance`[axes[1]], 1), "%)")) +
    ylab(paste0("Achse ", axes[2], "(", round(res_gda$eig$`percentage of variance`[axes[2]], 1), "%)"))

  # Labeln
  if(labels) {
    p <- p + ggrepel::geom_label_repel(data = coord_ind_timeseries %>% filter(time == time_point_names[1]),
                                         aes_string(paste0("Dim.", axes[1]), paste0("Dim.", axes[2]), colour = "time", label = "id"))
  }

  # Theme adaptieren
  p <- add_theme(p)

  # Beschreibung der Punkte
  p <- p + theme(legend.position = "bottom", legend.title = element_blank())

  # Beschriftung anpassen
  p <- .gda_plot_labels(res_gda, p, title, axes, plot_modif_rates, axis_lab_name = axis_lab_name)

  # Plotten
  return(p)
}