Title:	Langevin Tour
Version:	0.8.1
Description:	An HTML widget that randomly tours 2D projections of numerical data. A random walk through projections of the data is shown. The user can manipulate the plot to use specified axes, or turn on Guided Tour mode to find an informative projection of the data. Groups within the data can be hidden or shown, as can particular axes. Points can be brushed, and the selection can be linked to other widgets using crosstalk. The underlying method to produce the random walk and projection pursuit uses Langevin dynamics. The widget can be used from within R, or included in a self-contained R Markdown or Quarto document or presentation, or used in a Shiny app.
URL:	https://logarithmic.net/langevitour/
BugReports:	https://github.com/pfh/langevitour/issues/
Imports:	htmlwidgets, crosstalk, RANN, assertthat
Suggests:	shiny, knitr, rmarkdown, GGally, DT, plotly, palmerpenguins, tourr, geozoo, liminal, uwot
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
Depends:	R (≥ 2.10)
NeedsCompilation:	no
Packaged:	2025-05-24 01:44:56 UTC; pfh
Author:	Paul Harrison [aut, cre]
Maintainer:	Paul Harrison <pfh@logarithmic.net>
Repository:	CRAN
Date/Publication:	2025-05-24 06:00:07 UTC

k-nearest neighbor denoising of a set of points

Description

Reduce noise in a high-dimensional dataset by averaging each point with its nearby neighbors.

Usage

knnDenoise(X, block = rep(1, nrow(X)), k = 30, steps = 2)

Arguments

Details

knnDenoise first finds the k-nearest neighbors to each point (including the point itself). Then, for each point, the average is found of the points reachable in steps steps along the directed k-nearest neighbor graph.

Examples

library(palmerpenguins)

completePenguins <- na.omit(penguins[,c(1,3,4,5,6)])

# Dimensions need to be on comparable scales to apply knnDenoise
scaled <- scale(completePenguins[,-1])

denoised <- knnDenoise(scaled)

langevitour(denoised, completePenguins$species, pointSize=2)

Langevin Tour

Description

Make a Langevin Tour HTML widget, which can be used to explore high-dimensional numerical datasets.

Usage

langevitour(
  X,
  group = NULL,
  name = NULL,
  center = NULL,
  scale = NULL,
  extraAxes = NULL,
  lineFrom = NULL,
  lineTo = NULL,
  lineColors = NULL,
  axisColors = NULL,
  levelColors = NULL,
  colorVariation = 0.1,
  pointSize = 1,
  subsample = NULL,
  state = NULL,
  width = NULL,
  height = NULL,
  elementId = NULL,
  link = NULL,
  linkFilter = TRUE,
  enableControls = TRUE
)

Arguments

Details

The only required argument is X, the high-dimensional collection of points. The group argument is also commonly used so that groups of points can be distinguished by color. Further arguments adjust the appearance or provide advanced features.

langevitour will by default not scale variables individually. If you want variables to be individually scaled, use something like scale=apply(X,2,sd)*4. Using the scale argument rather than modifying X directly ensures the plot axes within the widgets retain the original units.

In Javascript, the langevitour object can be obtained using document.getElementById(elementId).langevitour. For example you could have a button that sets the state of a widget using document.getElementById(elementId).langevitour.setState(desiredState).

Value

An htmlwidget object.

Examples

library(palmerpenguins)

completePenguins <- na.omit(penguins[,c(1,3,4,5,6)])
scale <- apply(completePenguins[,-1], 2, sd)*4

langevitour(
    completePenguins[,-1], 
    completePenguins$species, 
    scale=scale, pointSize=2)


# An example setting the widget's initial state
 
langevitour(
    completePenguins[,-1], 
    completePenguins$species, 
    scale=scale, pointSize=2,
    state='{"guideType":"pca","labelInactive":["bill_length_mm"]}')


# An example with controls and interaction disabled

langevitour(
    completePenguins[,-1], 
    completePenguins$species, 
    scale=scale, pointSize=2,
    enableControls=FALSE)

Shiny bindings for langevitour

Description

Output and render functions for using langevitour within Shiny applications and interactive Rmd documents.

Usage

langevitourOutput(outputId, width = "100%", height = "600px")

renderLangevitour(expr, env = parent.frame(), quoted = FALSE)

Arguments

Examples

library(shiny)
library(palmerpenguins)

completePenguins <- na.omit(penguins[,c(1,3,4,5,6)])
scale <- apply(completePenguins[,-1], 2, sd)*4

ui <- fluidPage(
    sliderInput('zoom', 'Zoom', 0, min=-1, max=1, step=0.1),
    langevitourOutput('widget')
)

server <- function(input,output) { 
    output$widget <- renderLangevitour({
        langevitour(
            completePenguins[,-1], 
            completePenguins$species, 
            scale=scale * 10^input$zoom, pointSize=2)
    })
}

app <- shinyApp(ui, server)

# Use runApp(app) or runGadget(app) to run app.

Principal components of scRNA-Seq of mouse brain cells

Description

Single-cell RNA-Seq gene expression of 2,816 mouse brain cells (Zeisel, 2015). The top 10 principal components were produced using the steps in the Bioconductor OSCA workflow.

Usage

data(zeiselPC)

Format

A data frame with 2,816 rows representing brain cells and 11 columns:

type

Cell type.

PC1

Principal component score.

PC2

Principal component score.

PC3

Principal component score.

PC4

Principal component score.

PC5

Principal component score.

PC6

Principal component score.

PC7

Principal component score.

PC8

Principal component score.

PC9

Principal component score.

PC10

Principal component score.

References

Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., Rolny, C., Castelo-Branco, G., Hjerling-Leffler, J., & Linnarsson, S. (2015). Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science, 347(6226), 1138–1142. doi:10.1126/science.aaa1934

Examples

data(zeiselPC)
langevitour(zeiselPC[,-1], zeiselPC$type)