Query a loon widget

Description

A generic function to query the loon (tcl) widget from the given target

Usage

## S3 method for class 'l_tour'
l_getPlots(target)

Arguments

Value

a loon widget

Examples

if(interactive()) {
  p <- l_tour(iris[, -5])
  l_isLoonWidget(p) # FALSE
  q <- l_getPlots(p)
  l_isLoonWidget(q) # TRUE

  # `l_compound` widget
  p <- l_tour_pairs(tourr::flea[, -7])
  l_isLoonWidget(p) # FALSE
  q <- l_getPlots(p)
  l_isLoonWidget(q) # FALSE
  is(q, "l_compound") # TRUE
}

Query the matrix of projection vectors

Description

Query the matrix of projection vectors

Usage

l_getProjection(target, data)

Arguments

Value

a matrix of projection vectors

Examples

if(interactive()) {
  dat <- iris[,-5]
  p <- l_tour(dat, color = iris$Species,
              as.l_tour = FALSE)
  # scroll the bar
  proj <- l_getProjection(p, dat)
  projected_object <- as.matrix(dat) %*% proj
  # it will not be exactly the same
  plot(projected_object[,1], projected_object[,2],
       col = hex12tohex6(p['color']))
}

Tour Layer Configuration

Description

Mainly used in the 2D (or 1D) tour interactive layer configuration

Usage

l_layer_callback(target, layer, ...)

Arguments

Details

It is a S3 method. The object class is determined by the layer **label**

Value

this callback function does not return any object. As the slider bar is scrolled, for the specified layer, the callback function will be fired and the layer will be configured.

Examples

if(interactive() && requireNamespace("tourr")) {
  # 1D tour
  p <- l_tour(iris[, -5], tour = tourr::grand_tour(1L))
  # add layer density
  l <- l_layer(l_getPlots(p),
               stats::density(p['x']),
               label = "density")

  # as we scroll the bar, the density curve does not change
  # unless the following function is executed
  l_layer_callback.density <- function(target, layer, ...) {

      layer <- loon::l_create_handle(c(l_getPlots(target), layer))
      den <- stats::density(target['x'])

      loon::l_configure(layer,
                        x = den$x,
                        y = den$y)

      invisible()
  }
}

2D density layer

Description

Two-dimensional kernel density estimation with an axis-aligned bivariate normal kernel

Usage

l_layer_density2d(
  widget,
  x,
  y,
  h,
  n = 25L,
  lims = NULL,
  color = "black",
  linewidth = 1,
  nlevels = 10,
  levels = NULL,
  label = "density2d",
  parent = "root",
  index = 0,
  group = NULL,
  active = TRUE,
  ...
)

Arguments

Value

an l_layer widget

Examples

if(interactive()) {
p <- l_plot(iris, color = iris$Species)
l <- l_layer_density2d(p)
}

Layer a hull for loon

Description

Creates a layer which is the subset of points lying on the hull (convex or alpha) of the set of points specified.

Usage

l_layer_hull(
  widget,
  x,
  y,
  color = "black",
  linewidth = 1,
  label = "hull",
  parent = "root",
  index = 0,
  group = NULL,
  active = TRUE,
  ...
)

Arguments

Details

Coordinates: the x or y can be a list or a vector.

• If they are vectors, the argument group will be used to set the groups.

• If they are not provided, the x will be inherited from the widget

Value

an l_layer widget

Examples

if(interactive()) {
p <- l_plot(iris, color = iris$Species)
l <- l_layer_hull(p, group = iris$Species)
}

Display tour path with trails

Description

A 2D tour path with trails

Usage

l_layer_trails(
  widget,
  x,
  y,
  xpre,
  ypre,
  color = "black",
  linewidth = 1,
  label = "trails",
  parent = "root",
  index = 0,
  active = TRUE,
  ...
)

Arguments

Value

an l_layer widget

Examples

if(interactive()) {
p <- l_tour(iris[, -5], color = iris$Species)
l <- l_layer_trails(p, color = "grey50")
}

Tour in loon

Description

An interactive tour in loon

Usage

l_tour(
  data,
  scaling = c("data", "variable", "observation", "sphere"),
  by = NULL,
  on,
  as.l_tour = TRUE,
  color = loon::l_getOption("color"),
  tour_path = tourr::grand_tour(),
  group = "color",
  start = NULL,
  slicing = FALSE,
  slicingDistance = NULL,
  numOfTours = 30L,
  interpolation = 40L,
  parent = NULL,
  envir = parent.frame(),
  ...
)

Arguments

Details

• tour_path is a tour generator; available tours are grand_tour, dependence_tour, frozen_tour, guided_tour, planned_tour, and etc

• Argument as.l_tour

  • If set to TRUE, the function returns an l_tour (or an l_tour_compound) object. Essentially, this object is a list with the first element being a loon (Tcl) widget and the second element a matrix of projection vectors. The advantage of this setup is that the matrix of projection vectors can be easily accessed using the `[` function (or the l_cget function). However, a limitation is that it does not constitute a valid loon (Tcl) widget-calling l_isLoonWidget would return FALSE. Consequently, many of loon's functionalities remain inaccessible.

  • If set to FALSE, the function returns either a loon (Tcl) widget (where calling l_isLoonWidget would return TRUE) or an l_compound object. In this case, the matrix of projection vectors is not directly accessible from it. However, the l_getProjection function can be used to retrieve an estimated matrix of projection vectors.

• The scaling state defines how the data is scaled. The axes display 0 at one end and 1 at the other. For the following explanation assume that the data is in a n x p dimensional matrix. The scaling options are then

  variable	per column scaling
  observation	per row scaling
  data	whole matrix scaling
  sphere	transforming variables to principal components

• The default slidingDistance is suggested by Laa, U., Cook, D., & Valencia, G. (2020). First, find the maximum Euclidean distance of each observation (centralized), say maxD. Then, compute the "relative volume" that vRel = (maxD^(d - 2))/10, where d is the dimension of this data set. In the end, the suggested slidingDistance is given by vRel^(1/(d - 2))

Value

an l_tour or an l_tour_compound object that one can query the loon states and a matrix projection vectors

See Also

l_getProjection

Examples

if(interactive() && requireNamespace('tourr')) {
  # 2D projection
  fl <- tourr::flea[, 1:6]
  # different scaling will give very different projections
  # in this dataset, scaling 'variable' will give the best separation
  p <- l_tour(fl, scaling = 'variable',
              color = tourr::flea$species)
  l0 <- l_layer_hull(p, group = p["color"],
                     color = "red", linewidth = 4)
  l1 <- l_layer_density2d(p)
  # a `l_tour` object
  class(p)

  # query the matrix of projection vectors
  proj <- p['projection'] # or `l_getProjection(p)`
  # suppose the scaling is still 'observation'
  new_xy <- as.matrix(
    loon::l_getScaledData(data = fl,
                          scaling = 'observation')) %*%
    proj
  plot(new_xy, xlab = "V1", ylab = "V2",
       col = loon::hex12tohex6(p['color']))

  # A higher dimension projection
  # turn the `tour` to 4 dimensional space
  s <- l_tour(fl, color = tourr::flea$species,
              scaling = "observation",
              tour_path = tourr::grand_tour(4L))

  # set `as.l_tour` FALSE
  p <- l_tour(fl, scaling = 'observation',
              color = tourr::flea$species)
  class(p)
  ## ERROR
  ## p["projection"]

  # query the estimated matrix of projection vectors
  l_getProjection(p)

  ##### facet by region
  olive <- tourr::olive
  p <- with(olive, l_tour(olive[, -c(1, 2)],
                          by = region,
                          color = area))
}

Tour Pairs Plot

Description

A nD tour path with a scatterplot matrix (the default tour is a 4D tour; by setting 'tour_path' to modify the dimension)

Usage

l_tour_pairs(
  data,
  scaling = c("data", "variable", "observation", "sphere"),
  tour_path = tourr::grand_tour(4L),
  numOfTours = 30L,
  interpolation = 40L,
  as.l_tour = TRUE,
  connectedScales = c("none", "cross"),
  linkingGroup,
  linkingKey,
  showItemLabels = TRUE,
  itemLabel,
  showHistograms = FALSE,
  histLocation = c("edge", "diag"),
  histHeightProp = 1,
  histArgs = list(),
  showSerialAxes = FALSE,
  serialAxesArgs = list(),
  color = "grey60",
  group = "color",
  start = NULL,
  parent = NULL,
  span = 10L,
  envir = parent.frame(),
  ...
)

Arguments

Value

an l_tour_compound object that one can query the loon states and a matrix projection vectors

See Also

l_pairs, l_tour

Examples

if(interactive() && requireNamespace('tourr')) {
  # q is a `l_pairs` object
  q <- l_tour_pairs(olive[, -c(1:2)],
                    color = olive$region)
  # query the matrix of projection vectors
  proj <- q["projection"]

  # query the `l_compound` widget
  lc <- l_getPlots(q)
  # pack the `density2d` layers
  layer_pack <- lapply(lc, function(w) l_layer_density2d(w))

  #### set `as.l_tour = FALSE`
  # q is a `l_pairs` object
  q <- l_tour_pairs(tourr::flea[, 1:6],
                    as.l_tour = FALSE,
                    color = tourr::flea$species,
                    showHistogram = TRUE,
                    showSerialAxes = TRUE)

  # proj <- q["projection"] # Return a list of `NA`
  # query estimated matrix of projection vectors
  proj <- l_getProjection(q, tourr::flea[, 1:6])
}