R Wrapper for 'interactive_tour' Function Written in 'python'

Launches the lionfish GUI and at minimum requires the data do be loaded and the plot_objects. The other parameters are optional. For technical reasons the parameters half_range, n_plot_cols, n_subsets, color_scale, label_size and display_size cannot be adjusted from within the GUI. The GUI has to be closed and relaunched (possibly with load_interactive_tour) if you want to change them. Please visit https://mmedl94.github.io/lionfish/index.html for a detailed description of the GUI and its features.

Usage

interactive_tour(
  data,
  plot_objects,
  feature_names = NULL,
  half_range = NULL,
  n_plot_cols = 2,
  preselection = FALSE,
  preselection_names = FALSE,
  n_subsets = 3,
  display_size = 5,
  hover_cutoff = 10,
  label_size = 15,
  color_scale = "default",
  color_scale_heatmap = "default",
  axes_blendout_threshhold = 1
)

Arguments

data: the dataset you want to investigate
plot_objects: a named list of objects you want to be displayed. Each entry requires a definition of the type of display and a specification of what should be plotted.
feature_names: names of the features of the dataset
half_range: factor that influences the scaling of the displayed tour plots. Small values lead to more spread out datapoints (that might not fit the plotting area), while large values lead to the data being more compact. If not provided a good estimate will be calculated and used.
n_plot_cols: specifies the number of columns of the grid of the final display.
preselection: a vector that specifies in which subset each datapoint should be put initially.
preselection_names: a vector that specifies the names of the preselection subsets
n_subsets: the total number of available subsets.
display_size: rough size of each subplot in inches
hover_cutoff: number of features at which the switch from intransparent to transparent labels that can be hovered over to make them intransparent occurs
label_size: size of the labels of the feature names of 1d and 2d tours
color_scale: a viridis/matplotlib colormap to define the color scheme of the subgroups
color_scale_heatmap: a viridis/matplotlib colormap to define the color scheme of the heatmap
axes_blendout_threshhold: initial value of the threshold for blending out projection axes with a smaller length

Value

opens the interactive GUI

Examples

library(tourr)
data("flea", package = "tourr")
data <- flea[1:6]
clusters <- as.numeric(flea$species)
flea_subspecies <- unique(flea$species)
feature_names <- colnames(data)

guided_tour_history <- tourr::save_history(data,
  tour_path = tourr::guided_tour(holes())
)
#> Converting input data to the required matrix format.
#> Target: 1.075, 18.4% better 
#> Target: 1.129, 5.0% better 
#> Target: 1.159, 2.6% better 
#> Target: 1.169, 0.9% better 
#> Target: 1.185, 1.4% better 
#> Target: 1.193, 0.6% better 
#> Target: 1.206, 1.1% better 
#> Target: 1.229, 1.9% better 
#> Target: 1.233, 0.3% better 
#> Target: 1.243, 0.8% better 
#> Target: 1.252, 0.7% better 
#> Target: 1.257, 0.4% better 
#> Target: 1.262, 0.4% better 
#> Target: 1.264, 0.2% better 
#> Target: 1.269, 0.4% better 
#> Target: 1.273, 0.3% better 
#> Target: 1.274, 0.1% better 
#> Target: 1.276, 0.1% better 
#> Target: 1.278, 0.1% better 
#> No better bases found after 25 tries.  Giving up.
#> Final projection: 
#> -0.459   0.478  
#> -0.665  -0.152  
#> -0.361  -0.263  
#> -0.272  -0.540  
#> -0.377   0.384  
#> -0.044  -0.491  
grand_tour_history_1d <- tourr::save_history(data,
  tour_path = tourr::grand_tour(d = 1)
)
#> Converting input data to the required matrix format.

half_range <- max(sqrt(rowSums(data^2)))

obj1 <- list(type = "2d_tour", obj = guided_tour_history)
obj2 <- list(type = "1d_tour", obj = grand_tour_history_1d)
obj3 <- list(type = "scatter", obj = c("tars1", "tars2"))
obj4 <- list(type = "hist", obj = "head")

if (check_venv()){
init_env(env_name = "r-lionfish", virtual_env = "virtual_env")
} else if (check_conda_env()){
init_env(env_name = "r-lionfish", virtual_env = "anaconda")
}

if (interactive()){
interactive_tour(
  data = data,
  plot_objects = list(obj1, obj2, obj3, obj4),
  feature_names = feature_names,
  half_range = half_range,
  n_plot_cols = 2,
  preselection = clusters,
  preselection_names = flea_subspecies,
  n_subsets = 5,
  display_size = 5
)
}