R/l_serialaxes.R
l_serialaxes.Rdl_serialaxes is a generic function for displaying multivariate data either as a
stacked star glyph plot, or as a parallel coordinate plot.
l_serialaxes(data, ...) # S3 method for default 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, ... )
| 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 |
| by | loon plot can be separated by some variables into multiple panels.
This argument can take a |
| on | if the |
| layout | layout facets as |
| 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 |
| active | a logical determining whether points appear or not
(default is |
| selected | a logical determining whether points appear selected at first
(default is |
| linewidth | vector with line widths. Default is given by |
| parent | a valid Tk parent widget path. When the parent widget is
specified (i.e. not |
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.
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.
Turn interactive loon plot static loonGrob, grid.loon, plot.loon.
Other loon interactive states:
l_hist(),
l_info_states(),
l_plot(),
l_state_names(),
names.loon()
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. }