ggparcoord: ggparcoord - A ggplot2 Parallel Coordinate Plot

In lrutter/RNASeqVisualization:

Description

A function for plotting static parallel coordinate plots, utilizing the ggplot2 graphics package.

Usage

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 = "")

Arguments

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 be 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 underlaying 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

Details

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 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.

Value

ggplot object that if called, will print

Author(s)

Jason Crowley [e-mail], Barret Schloerke [e-mail], Di Cook [e-mail], Heike Hofmann [e-mail], Hadley Wickham [e-mail]

Examples

 # small function to display plots only if it's interactive
p_ <- function(pm) {
  if (interactive()) {
    print(pm)
  }
  invisible()
}

# 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)

# 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(size = 1)) +
  ggplot2::scale_size_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)
splineFactor <- length(columns) * 3
p <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = I(splineFactor))
p_(p)

lrutter/RNASeqVisualization documentation built on May 21, 2019, 7:52 a.m.