The dlvPlot function produces a dot-violin-line plot, and dlvTheme is the default theme.
dlvTheme(base_size = 11, base_family = "", ...)
dlvPlot(
dat,
x = NULL,
y,
z = NULL,
conf.level = 0.95,
jitter = "FALSE",
binnedDots = TRUE,
binwidth = NULL,
error = "lines",
dotsize = "density",
singleColor = "black",
comparisonColors = rosetta::opts$get("dlvPlotCompCols"),
densityDotBaseSize = 3,
normalDotBaseSize = 1,
violinAlpha = 0.2,
dotAlpha = 0.4,
lineAlpha = 1,
connectingLineAlpha = 1,
meanDotSize = 5,
posDodge = 0.2,
errorType = "both",
outputFile = NULL,
outputWidth = 10,
outputHeight = 10,
ggsaveParams = list(units = "cm", dpi = 300, type = "cairo")
)
# S3 method for dlvPlot
print(x, ...)Passed on to the ggplot theme_grey() function.
The dataframe containing x, y and z.
Character value with the name of the predictor ('independent') variable, must refer to a categorical variable (i.e. a factor).
Character value with the name of the critetion ('dependent') variable, must refer to a continuous variable (i.e. a numeric vector).
Character value with the name of the moderator variable, must refer to a categorical variable (i.e. a factor).
Confidence of confidence intervals.
Logical value (i.e. TRUE or FALSE) whether or not to jitter individual datapoints. Note that jitter cannot be combined with posDodge (see below).
Logical value indicating whether to use binning to display the dots. Overrides jitter and dotsize.
Numeric value indicating how broadly to bin (larger values is more binning, i.e. combining more dots into one big dot).
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)
Character value: "density" or "normal"; when "density", the size of each dot corresponds to the density of the distribution at that point.
The color to use when drawing one or more univariate
distributions (i.e. when no z is specified.
The colors to use when a z is specified. This
should be at least as many colors as z has levels. By default,
palette Set1 from RColorBrewer is used.
Numeric value indicating base size of dots when their size corresponds to the density (bigger = larger dots).
Numeric value indicating base size of dots when their size is fixed (bigger = larger dots).
Numeric value indicating alpha value of violin layer (0 = completely transparent, 1 = completely opaque).
Numeric value indicating alpha value of dot layer (0 = completely transparent, 1 = completely opaque).
Numeric value indicating alpha value of the confidence interval line layer (0 = completely transparent, 1 = completely opaque).
Numeric value indicating alpha value of the layer with the lines connecting the means (0 = completely transparent, 1 = completely opaque).
Numeric value indicating the size of the dot used to indicate the mean in the line layer.
Numeric value indicating the distance to dodge positions (0 for complete overlap).
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.
A file to which to save the plot.
Width and height of saved plot (specified in
centimeters by default, see ggsaveParams).
Parameters to pass to ggsave when saving the plot.
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 x-axis and with numerical y variables on y-axis
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 x-axis with numerical variable y on the y-axis (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 x-axis, factor z determines the different lines, and with the numerical y variable on the y-axis
An object is returned with the following elements:
Raw datafile provided when calling dlvPlot
Transformed (long) datafile dlvPlot uses
Dataframe with extracted descriptives used to plot the mean and confidence intervals
The range of the Y variable used to construct the plot
The plot itself
This function creates Dot Violin Line plots. One image says more than a thousand words; I suggest you run the example :-)
### Note: the 'not run' is simply because running takes a lot of time,
### but these examples are all safe to run!
if (FALSE) {
### 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 density-sized dots might work better
dlvPlot(dat, x='x1', y='y', z='x2', posDodge=.3);
}