dlvPlot
The dlvPlot function produces a dotviolinline plot, and dlvTheme is the default theme.
 Keywords
 utilities
Usage
dlvPlot(dat, x = NULL, y, z = NULL, conf.level = .95,
jitter = "FALSE", binnedDots = TRUE, binwidth=NULL,
error="lines", dotsize="density", densityDotBaseSize=3,
normalDotBaseSize=1, violinAlpha = .2, dotAlpha = .4,
lineAlpha = 1, connectingLineAlpha = 1,
meanDotSize=5, posDodge=0.2, errorType = "both")
dlvTheme(base_size = 11, base_family = "", ...)
Arguments
 dat
 The dataframe containing x, y and z.
 x
 Character value with the name of the predictor ('independent') variable, must refer to a categorical variable (i.e. a factor).
 y
 Character value with the name of the critetion ('dependent') variable, must refer to a continuous variable (i.e. a numeric vector).
 z
 Character value with the name of the moderator variable, must refer to a categorical variable (i.e. a factor).
 conf.level
 Confidence of confidence intervals.
 jitter
 Logical value (i.e. TRUE or FALSE) whether or not to jitter individual datapoints. Note that jitter cannot be combined with posDodge (see below).
 binnedDots
 Logical value indicating whether to use binning to display the dots. Overrides jitter and dotsize.
 binwidth
 Numeric value indicating how broadly to bin (larger values is more binning, i.e. combining more dots into one big dot).
 error
 Character value: "none", "lines" or "whiskers"; indicates whether to show the confidence interval as lines with (whiskers) or without (lines) horizontal whiskers or not at all (none)
 dotsize
 Character value: "density" or "normal"; when "density", the size of each dot corresponds to the density of the distribution at that point.
 densityDotBaseSize
 Numeric value indicating base size of dots when their size corresponds to the density (bigger = larger dots).
 normalDotBaseSize
 Numeric value indicating base size of dots when their size is fixed (bigger = larger dots).
 violinAlpha
 Numeric value indicating alpha value of violin layer (0 = completely transparent, 1 = completely opaque).
 dotAlpha
 Numeric value indicating alpha value of dot layer (0 = completely transparent, 1 = completely opaque).
 lineAlpha
 Numeric value indicating alpha value of the confidence interval line layer (0 = completely transparent, 1 = completely opaque).
 connectingLineAlpha
 Numeric value indicating alpha value of the layer with the lines connecting the means (0 = completely transparent, 1 = completely opaque).
 meanDotSize
 Numeric value indicating the size of the dot used to indicate the mean in the line layer.
 posDodge
 Numeric value indicating the distance to dodge positions (0 for complete overlap).
 errorType

If the error is shown using lines, this argument indicates Whether the
errorbars should show the confidence interval (
errorType='ci'
), the standard errors (errorType='se'
), or both (errorType='both'
). In this last case, the standard error will be wider than the confidence interval.  base_size, base_family, ...
 Passed on to the ggplot theme_grey() function.
Details
This function creates Dot Violin Line plots. One image says more than a thousand words; I suggest you run the example :)
Value
The behavior of this function depends on the arguments.
If no x and z are provided and y is a character value, dlvPlot produces a univariate plot for the numerical y variable.
If no x and z are provided, and y is c character vector, dlvPlot produces multiple Univariate plots, with variable names determining categories on xaxis and with numerical y variables on yaxis
If both x and y are a character value, and no z is provided, dlvPlot produces a bivariate plot where factor x determines categories on xaxis with numerical variable y on the yaxis (roughly a line plot with a single line)
Finally, if x, y and z are each a character value, dlvPlot produces multivariate plot where factor x determines categories on xaxis, factor z determines the different lines, and with the numerical y variable on the yaxis
An object is returned with the following elements:
Examples
### Note: the 'not run' is simply because running takes a lot of time,
### but these examples are all safe to run!
## Not run: 
# ### Create simple dataset
# dat < data.frame(x1 = factor(rep(c(0,1), 20)),
# x2 = factor(c(rep(0, 20), rep(1, 20))),
# y=rep(c(4,5), 20) + rnorm(40));
# ### Generate a simple dlvPlot of y
# dlvPlot(dat, y='y');
# ### Now add a predictor
# dlvPlot(dat, x='x1', y='y');
# ### And finally also a moderator:
# dlvPlot(dat, x='x1', y='y', z='x2');
# ### The number of datapoints might be a bit clearer if we jitter
# dlvPlot(dat, x='x1', y='y', z='x2', jitter=TRUE);
# ### Although just dodging the densitysized dots might work better
# dlvPlot(dat, x='x1', y='y', z='x2', posDodge=.3);
## 