lionfish: an expLoratory Interactive tOol for dyNamic visualization and identiFicatIon multidimenSional mecHanisms

This package is an extension of the tourr R-package. For a general overview of the tourr package please refer to the tourr documentation. lionfish adds interactive displays to the functionality of tourr allowing users to direct the path of the tours.

Installation

You can install the development version of lionfish from github with:

install.packages("remotes")
remotes::install_github("mmedl94/lionfish")

Make sure you have git installed. You can download and install git from https://git-scm.com/downloads.

Complications may arise when installing and accessing the Python backend of this package. If you run into any, please don't refrain from opening an issue!

Example

To run an interactive tour you will first have to initialize the python backend with

library(lionfish)
init_env()

Then you can display saved tour objects, scatterplots or histograms with interactive_tour()

library(tourr)
data <- apply(flea[,1:6], 2, function(x) (x-mean(x))/sd(x))
clusters <- as.numeric(flea$species)
flea_subspecies <- unique(flea$species)

guided_tour_history <- save_history(data,
                                    tour_path = guided_tour(holes()))
grand_tour_history_1d <- save_history(data,
                                      tour_path = grand_tour(d=1))

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

init_env()

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")

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)