A function for plotting static parallel coordinate plots, utilizing
the `ggplot2`

graphics package.

```
ggparcoord(
data,
columns = 1:ncol(data),
groupColumn = NULL,
scale = "std",
scaleSummary = "mean",
centerObsID = 1,
missing = "exclude",
order = columns,
showPoints = FALSE,
splineFactor = FALSE,
alphaLines = 1,
boxplot = FALSE,
shadeBox = NULL,
mapping = NULL,
title = ""
)
```

- data
the dataset to plot

- columns
a vector of variables (either names or indices) to be axes in the plot

- groupColumn
a single variable to group (color) by

- scale
method used to scale the variables (see Details)

- scaleSummary
if scale=="center", summary statistic to univariately center each variable by

- centerObsID
if scale=="centerObs", row number of case plot should univariately be centered on

- missing
method used to handle missing values (see Details)

- order
method used to order the axes (see Details)

- showPoints
logical operator indicating whether points should be plotted or not

- splineFactor
logical or numeric operator indicating whether spline interpolation should be used. Numeric values will multiplied by the number of columns,

`TRUE`

will default to cubic interpolation,`AsIs`

to set the knot count directly and`0`

,`FALSE`

, or non-numeric values will not use spline interpolation.- alphaLines
value of alpha scaler for the lines of the parcoord plot or a column name of the data

- boxplot
logical operator indicating whether or not boxplots should underlay the distribution of each variable

- shadeBox
color of underlying box which extends from the min to the max for each variable (no box is plotted if

`shadeBox == NULL`

)- mapping
aes string to pass to ggplot object

- title
character string denoting the title of the plot

ggplot object that if called, will print

`scale`

is a character string that denotes how to scale the variables
in the parallel coordinate plot. Options:

`std`

: univariately, subtract mean and divide by standard deviation

`robust`

: univariately, subtract median and divide by median absolute deviation

`uniminmax`

: univariately, scale so the minimum of the variable is zero, and the maximum is one

`globalminmax`

: no scaling is done; the range of the graphs is defined by the global minimum and the global maximum

`center`

: use

`uniminmax`

to standardize vertical height, then center each variable at a value specified by the`scaleSummary`

param`centerObs`

: use

`uniminmax`

to standardize vertical height, then center each variable at the value of the observation specified by the`centerObsID`

param

`missing`

is a character string that denotes how to handle missing
missing values. Options:

`exclude`

: remove all cases with missing values

`mean`

: set missing values to the mean of the variable

`median`

: set missing values to the median of the variable

`min10`

: set missing values to 10% below the minimum of the variable

`random`

: set missing values to value of randomly chosen observation on that variable

`order`

is either a vector of indices or a character string that denotes how to
order the axes (variables) of the parallel coordinate plot. Options:

`(default)`

: order by the vector denoted by

`columns`

`(given vector)`

: order by the vector specified

`anyClass`

: order variables by their separation between any one class and the rest (as opposed to their overall variation between classes). This is accomplished by calculating the F-statistic for each class vs. the rest, for each axis variable. The axis variables are then ordered (decreasing) by their maximum of k F-statistics, where k is the number of classes.

`allClass`

: order variables by their overall F statistic (decreasing) from an ANOVA with

`groupColumn`

as the explanatory variable (note: it is required to specify a`groupColumn`

with this ordering method). Basically, this method orders the variables by their variation between classes (most to least).`skewness`

: order variables by their sample skewness (most skewed to least skewed)

`Outlying`

: order by the scagnostic measure, Outlying, as calculated by the package

`scagnostics`

. Other scagnostic measures available to order by are`Skewed`

,`Clumpy`

,`Sparse`

,`Striated`

,`Convex`

,`Skinny`

,`Stringy`

, and`Monotonic`

. Note: To use these methods of ordering, you must have the`scagnostics`

package loaded.

```
# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive
# use sample of the diamonds data for illustrative purposes
data(diamonds, package = "ggplot2")
diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 100), ]
# basic parallel coordinate plot, using default settings
p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10))
p_(p)
# this time, color by diamond cut
p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2)
p_(p)
# underlay univariate boxplots, add title, use uniminmax scaling
p <- ggparcoord(
data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2,
scale = "uniminmax", boxplot = TRUE, title = "Parallel Coord. Plot of Diamonds Data"
)
p_(p)
#> Warning: The following aesthetics were dropped during statistical transformation: colour
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#> the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#> variable into a factor?
# utilize ggplot2 aes to switch to thicker lines
p <- ggparcoord(
data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2,
title = "Parallel Coord. Plot of Diamonds Data", mapping = ggplot2::aes(linewidth = 1)
) +
ggplot2::scale_linewidth_identity()
p_(p)
# basic parallel coord plot of the msleep data, using 'random' imputation and
# coloring by diet (can also use variable names in the columns and groupColumn
# arguments)
data(msleep, package = "ggplot2")
p <- ggparcoord(
data = msleep, columns = 6:11, groupColumn = "vore", missing =
"random", scale = "uniminmax"
)
p_(p)
# center each variable by its median, using the default missing value handler,
# 'exclude'
p <- ggparcoord(
data = msleep, columns = 6:11, groupColumn = "vore", scale =
"center", scaleSummary = "median"
)
p_(p)
# with the iris data, order the axes by overall class (Species) separation using
# the anyClass option
p <- ggparcoord(data = iris, columns = 1:4, groupColumn = 5, order = "anyClass")
p_(p)
# add points to the plot, add a title, and use an alpha scalar to make the lines
# transparent
p <- ggparcoord(
data = iris, columns = 1:4, groupColumn = 5, order = "anyClass",
showPoints = TRUE, title = "Parallel Coordinate Plot for the Iris Data",
alphaLines = 0.3
)
p_(p)
# color according to a column
iris2 <- iris
iris2$alphaLevel <- c("setosa" = 0.2, "versicolor" = 0.3, "virginica" = 0)[iris2$Species]
p <- ggparcoord(
data = iris2, columns = 1:4, groupColumn = 5, order = "anyClass",
showPoints = TRUE, title = "Parallel Coordinate Plot for the Iris Data",
alphaLines = "alphaLevel"
)
p_(p)
## Use splines on values, rather than lines (all produce the same result)
columns <- c(1, 5:10)
p <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = TRUE)
p_(p)
p <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = 3)
p_(p)
```