Invalid domain for l_serialaxes

[chriskuchar]

I am working with marketing data and the spend of some advertisements may be over 100,000. Can you change the default max and min that is allowed in the parallel coordinate serial axes plots to be unlimited?

[rwoldford]

The default range is always going to be from 0 to 1 on each axis. You might want to rescale your data appropriately before calling l_serialaxes().

I am adding the following to the examples section on l_serialaxes() man page which might be helpful to you. Most likely the scaling = "none" choice at the bottom is the most relevant to you if I have understood your request.

#'
#' #######
#' #
#' # 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.