l_serialaxes: Create an interactive serialaxes (parallel axes or radial...
In loon: Interactive Statistical Data Visualization

l_serialaxes

R Documentation

Create an interactive serialaxes (parallel axes or radial axes) plot

Description

l_serialaxes is a generic function for displaying multivariate data either as a stacked star glyph plot, or as a parallel coordinate plot.

Usage

l_serialaxes(data, ...)

## Default S3 method:
l_serialaxes(
  data,
  sequence,
  scaling = "variable",
  axesLayout = "radial",
  by = NULL,
  on,
  layout = c("grid", "wrap", "separate"),
  andrews = FALSE,
  showAxes = TRUE,
  color = l_getOption("color"),
  active = TRUE,
  selected = FALSE,
  linewidth = l_getOption("linewidth"),
  parent = NULL,
  ...
)

Arguments

`data`	a data frame with numerical data only
`...`	named arguments to modify the serialaxes states or layouts, see details.
`sequence`	vector with variable names that defines the axes sequence
`scaling`	one of 'variable', 'data', 'observation' or 'none' to specify how the data is scaled. See Details and Examples for more information.
`axesLayout`	either `"radial"` or `"parallel"`
`by`	loon plot can be separated by some variables into multiple panels. This argument can take a `formula`, `n` dimensional state names (see `l_nDimStateNames`) an `n`-dimensional `vector` and `data.frame` or a `list` of same lengths `n` as input.
`on`	if the `x` or `by` is a formula, an optional data frame containing the variables in the `x` or `by`. If the variables are not found in data, they are taken from environment, typically the environment from which the function is called.
`layout`	layout facets as `'grid'`, `'wrap'` or `'separate'`
`andrews`	Andrew's plot (a 'Fourier' transformation)
`showAxes`	boolean to indicate whether axes should be shown or not
`color`	vector with line colors. Default is given by `l_getOption("color")`.
`active`	a logical determining whether points appear or not (default is `TRUE` for all points). If a logical vector is given of length equal to the number of points, then it identifies which points appear (`TRUE`) and which do not (`FALSE`).
`selected`	a logical determining whether points appear selected at first (default is `FALSE` for all points). If a logical vector is given of length equal to the number of points, then it identifies which points are (`TRUE`) and which are not (`FALSE`).
`linewidth`	vector with line widths. Default is given by `l_getOption("linewidth")`.
`parent`	a valid Tk parent widget path. When the parent widget is specified (i.e. not `NULL`) then the plot widget needs to be placed using some geometry manager like `tkpack` or `tkplace` in order to be displayed. See the examples below.

Details

For more information run: l_help("learn_R_display_hist")

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 nxp dimensional matrix. The scaling options are then

variable per column scaling

observation per row scaling

data whole matrix scaling

none do not scale
Some arguments to modify layouts can be passed through, e.g. "separate", "byrow", etc. Check l_facet to see how these arguments work.

Value

if the argument by is not set, a loon widget will be returned; else an l_facet object (a list) will be returned and each element is a loon widget displaying a subset of interest.

Examples

if(interactive()){

#######
#
# Effect of the choice of the argument "scaling"
#
# To illustrate we will look at the four measurements of
# 150 iris flowers from the iris data of Edgar Anderson made
# famous by R.A. Fisher.
#
# First separate the measurements
irisFlowers <- iris[, 1:4]
# from their species
species <- iris[,5]
# and get some identifiers for the individual flowers
flowerIDs <- paste(species, 1:50)
#
# Now create parallel axes plots of the measurements
# using different scaling values.

#
# scaling = "variable"
#
# This is the standard scaling of most serial axes plots,
# scaling each axis from the minimum to the maximum of that variable.
# Hence it is the default scaling.
#
# More precisely, it maps the minimum value in each column (variable) to
# zero and the maximum to one.  The result is every parallel
# axis will have a point at 0 and a point at 1.
#
# This scaling highlights the relationships (e.g. correlations)
# between the variables (removes the effect of the location and scale of
# each variable).
#
# For the iris data, ignoring species we see for example that
# Sepal.Length and Sepal.Width are negatively correlated (lots of
# crossings) across species but more positively correlated (mostly
# parallel lines) within each species (colour).
#
sa_var <- l_serialaxes(irisFlowers,
                       scaling = "variable",    # scale within column
                       axesLayout = "parallel",
                       color = species,
                       linewidth = 2,
                       itemLabel = flowerIDs,
                       showItemLabels = TRUE,
                       title = "scaling = variable (initially)",
                       linkingGroup = "irisFlowers data")

#
# scaling = "observation"
#
# This maps the minimum value in each row (observation) to
# zero and the maximum value in each row to one.
#
# The result is that every observation (curve in the parallel
# coordinate plot) will touch 0 on at least one axis and touch
# 1 on another.
#
# This scaling highlights the differences between observations (rows)
# in terms of the relative measurements across the variables for each
# observation.
#
# For example, for the iris data we can see that for every flower (row)
# the Sepal.Length is the largest measurement and the Petal.Width
# is the smallest.  Each curve gives some sense of the *shape* of each
# flower without regard to its size.  Two species (versicolor and
# virginica) have similar shaped flowers (relatively long but narrow
# sepals and petals), whereas the third (setosa) has relatively large
# sepals compared to small petals.
#
sa_obs <- l_serialaxes(irisFlowers,
                       scaling = "observation", # scale within row
                       axesLayout = "parallel",
                       color = species,
                       linewidth = 2,
                       itemLabel = flowerIDs,
                       showItemLabels = TRUE,
                       title = "scaling = observation (initially)",
                       linkingGroup = "irisFlowers data")

#
# scaling = "data"
#
# This maps the minimum value in the whole dataset (over all elements)
# to zero and the maximum value in the whole dataset to one.
#
# The result is that every measurement is on the same numeric (if not
# measurement) scale.  Highlighting the relative magnitudes of all
# numerical values in the data set, each curve shows the relative magnitudes
# without rescaling by variable.
#
# This is most sensible data such as the iris flower where all four measurements
# appear to have been taken on the same measuring scale.
#
# For example, for the iris data full data scaling preserves the size
# and shape of each flower.  Again virginica is of roughly the same
# shape as versicolor but has distinctly larger petals.
# Setosa in contrast is quite differently shaped in both sepals and petals
# but with sepals more similar in size to the two other flowers and
# with significantly smaller petals.
sa_dat <- l_serialaxes(irisFlowers,
                       scaling = "data",        # scale using all data
                       axesLayout = "parallel",
                       color = species,
                       linewidth = 2,
                       itemLabel = flowerIDs,
                       showItemLabels = TRUE,
                       title = "scaling = data (initially)",
                       linkingGroup = "irisFlowers data")

#
#  scaling = "none"
#
#  Sometimes we might wish to choose a min and max to use
#  for the whole data set; or perhaps a separate min and max
#  for each variable.

#  This would be done outside of the construction of the plot
#  and displayed by having scaling = "none" in the plot.
#
#  For example, for the iris data, we might choose scales so that
#  the minimum and the maximum values within the data set do not
#  appear at the end points 0 and 1 of the axes but instead inside.
#
#  Suppose we choose the following limits for all variables
lower_lim <- -3 ; upper_lim <- max(irisFlowers) + 1

#  These are the limits we want to use to define the end points of
#  the axes for all variables.
#  We need only scale the data as
irisFlowers_0_1 <- (irisFlowers - lower_lim)/(upper_lim - lower_lim)
#  Or alternatively using the built-in scale function
#  (which allows different scaling for each variable)
irisFlowers_0_1 <- scale(irisFlowers,
                         center = rep(lower_lim, 4),
                         scale = rep((upper_lim - lower_lim), 4))

# Different scales for different
# And instruct the plot to not scale the data but plot it on the 0-1 scale
# for all axes.  (Note any rescaled date outside of [0,1] will not appear.)
#
sa_none <- l_serialaxes(irisFlowers_0_1,
                        scaling = "none",        # do not scale
                        axesLayout = "parallel",
                        color = species,
                        linewidth = 2,
                        itemLabel = flowerIDs,
                        showItemLabels = TRUE,
                        title = "scaling = none (initially)",
                        linkingGroup = "irisFlowers data")

# This is particularly useful for "radial" axes to keep the polygons away from
# the centre of the display.
# For example
sa_none["axesLayout"] <- "radial"
# now displays each flower as a polygon where shapes and sizes are easily
# compared.
#
#  NOTE: rescaling the data so that all values are within [0,1] is perhaps
#        the best way to proceed (especially if there are natural lower and
#        upper limits for each variable).
#        Then scaling can always be changed via the inspector.

}

loon documentation built on July 9, 2023, 5:48 p.m.