The dlvPlot function produces a dot-violin-line plot, and dlvTheme is the default theme.
dlvPlot(dat, x = NULL, y, z = NULL, conf.level = .95, jitter = "FALSE", binnedDots = TRUE, binwidth=NULL, error="lines", dotsize="density", singleColor = "black", comparisonColors = brewer.pal(8, 'Set1'), densityDotBaseSize=3, normalDotBaseSize=1, violinAlpha = .2, dotAlpha = .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")) dlvTheme(base_size = 11, base_family = "", ...)
dat | The dataframe containing x, y and z. |
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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. |
singleColor | The color to use when drawing one or more univariate distributions (i.e. when no |
comparisonColors | The colors to use when a |
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 ( |
outputFile | A file to which to save the plot. |
outputWidth, outputHeight | Width and height of saved plot (specified in centimeters by default, see |
ggsaveParams | Parameters to pass to ggsave when saving the plot. |
base_size, base_family, ... | Passed on to the ggplot theme_grey() function. |
This function creates Dot Violin Line plots. One image says more than a thousand words; I suggest you run the example :-)
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
### 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 density-sized dots might work better dlvPlot(dat, x='x1', y='y', z='x2', posDodge=.3); # }