eda_viol: Violin plot.

In mgimond/tukeyedar: Tukey Inspired Exploratory Data Analysis Functions

Violin plot.

Description

eda_viol generates a violin plot.

Usage

eda_viol(
  dat,
  x = NULL,
  grp = NULL,
  p = 1,
  tukey = FALSE,
  show.par = TRUE,
  sq = FALSE,
  inner = 0.6826,
  bw = "SJ-dpi",
  kernel = "gaussian",
  stat = "both",
  pch = 16,
  size = 0.8,
  alpha = 0.3,
  p.col = "grey50",
  p.fill = "grey80",
  grey = 0.6,
  col.ends = "grey90",
  col.mid = "#EBC89B",
  col.ends.dens = "grey90",
  col.mid.dens = "#EBC89B",
  offset = 0.02,
  tsize = 1.5,
  reorder = FALSE,
  reorder.stat = median,
  xlab = NULL,
  ylab = NULL,
  ...
)

Arguments

dat	Single continuous variable vector, or a dataframe.
x	Continuous variable if dat is a dataframe, ignored otherwise.
grp	Categorical Variable if dat is a dataframe, ignored otherwise.
p	Power transformation to apply to all values.
tukey	Boolean determining if a Tukey transformation should be adopted (TRUE) or if a Box-Cox transformation should be adopted (FALSE).
show.par	Boolean determining if the power transformation used with the data should be displayed in the plot's upper-right corner.
sq	Boolean determining if the plot should be square.
inner	Fraction of values that should be captured by the inner color band of the normal and density plots. Defaults to 0.6826 (inner 68\ values).
bw	Bandwidth parameter passed to the density() function.
kernel	Kernel parameter passed to the density() function.
stat	Statistical summary to display in the plot. Choice of "median", "mean", "both" or "none". Defaults to both.
pch	Point symbol type.
size	Point size.
alpha	Fill transparency (0 = transparent, 1 = opaque). Only applicable if rgb() is not used to define fill colors.
p.col	Color for point symbol.
p.fill	Point fill color passed to bg (Only used for pch ranging from 21-25).
grey	Grey level to apply to plot elements such as axes, labels, etc... (0 to 1 with 1 = black).
col.ends	Fill color for tail-ends of the distribution.
col.mid	Fill color for mid-portion of the distribution.
col.ends.dens	Fill color for tail-ends of the density distribution.
col.mid.dens	Fill color for mid-portion of the density distribution.
offset	A value (in x-axis units) that defines the gap between left and right side plots. Ignored if dens is FALSE.
tsize	Size of plot title.
reorder	Boolean determining if factors have to be reordered based on reoder.stat.
reorder.stat	Choice of summary statistic to use for reordering plots. reorder.stat can be either mean or median.
xlab	X variable label.
ylab	Y variable label.
...	Note used.

Details

This function will generate a violin plot from the data. It implements the stats::density function when generating the shape of the plots. As such the bw and kernel arguments are passed on to the stats::density function.

The plots have two fill colors: one for the inner band and the other for the outer band. The inner band shows the area of the curve that encompasses the desired fraction of mid-values defined by inner. By default, this value is 0.6826, or 68.26\ covered by +/- 1 standard deviations of a Normal distribution). The range is computed from the actual values and not from a fitted normal distribution.

Measures of centrality are added to the plot. By default, both the mean (dashed line) and the median (solid line) are added to the plot.

Value

Does not return a value.

See Also

density

Examples

# Explore a skewed distribution
set.seed(132)
x <- rnbinom(500, 10, .5)

# Generate violin plot
eda_viol(x)

# The inner band's range can be modified. Here, we view the interquartile
# range, the +/- 1 standard deviation range and the inner 95% range)
OP <- par(mfrow = c(1,3))
invisible(sapply(c(0.5, 0.6826, 0.95),
      function(prop) eda_viol(x, inner = prop, tsize = 1,
                                 ylab = paste(prop*100,"% of values"))))
par(OP)

# The bandwidth selector can also be specified
OP <- par(mfrow=c(2,3))
 invisible(sapply(c("SJ-dpi", "nrd0", "nrd", "SJ-ste", "bcv", "ucv" ),
       function(band) eda_viol(x, bw = band, tsize=0.9, size=0, offset=0.005,
                                  ylab = band)))
par(OP)

# The bandwidth argument can also be passed a numeric value
# (bw = 0.75, 0.5 and 0.3)
OP <- par(mfrow=c(1,3))
invisible(sapply(c(0.75, 0.5, 0.3 ),
       function(band) eda_viol(x, bw = band, tsize=1,size=.5, offset=0.01,
                                  ylab = band)))
par(OP)

# Examples of a few kernel options
OP <- par(mfrow=c(1,3))
invisible(sapply(c("gaussian", "optcosine", "rectangular" ),
      function(k) eda_viol(x, kernel = k, tsize=1, size=.5, offset=0.01,
                               ylab = k)))
par(OP)

# Another example where data are passed as a dataframe
set.seed(540)
dat <- data.frame(value = rbeta(20, 1, 50),
                 grp = sample(letters[1:3], 100, replace = TRUE))
eda_viol(dat, value, grp)

# Points can be removed and the gap rendered narrower
eda_viol(dat, value, grp, size = 0, offset = 0.01)

# Gap can be removed all together
eda_viol(dat, value, grp, size = 0, offset = 0)

# Remove both mean and medians
eda_viol(dat, value, grp, size = 0, offset = 0, stat = "none")

# Color can be modified. Here we modify the density plot  fill colors
eda_viol(dat, value, grp, size = 0, offset = 0.01,
            col.ends.dens = "#A1D99B", col.mid.dens = "#E5F5E0")

# A power transformation can be applied to the data. Here
# we'll apply a log transformation
eda_viol(dat, value, grp, p = 0)

mgimond/tukeyedar documentation built on Feb. 1, 2025, 4:02 a.m.