dlvPlot: dlvPlot
In userfriendlyscience: Quantitative Analysis Made Accessible

Description Usage Arguments Details Value Examples

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.
`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 `z` is specified.
`comparisonColors`	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.
`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.
`outputFile`	A file to which to save the plot.
`outputWidth, outputHeight`	Width and height of saved plot (specified in centimeters by default, see `ggsaveParams`).
`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:

`dat.raw`	Raw datafile provided when calling dlvPlot
`dat`	Transformed (long) datafile dlvPlot uses
`descr`	Dataframe with extracted descriptives used to plot the mean and confidence intervals
`yRange`	The range of the Y variable used to construct the plot
`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);

## End(Not run)

Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2
Registered S3 methods overwritten by 'lme4':
  method                          from
  cooks.distance.influence.merMod car 
  influence.merMod                car 
  dfbeta.influence.merMod         car 
  dfbetas.influence.merMod        car 
Warning message:
`fun.y` is deprecated. Use `fun` instead. 
Warning message:
`fun.y` is deprecated. Use `fun` instead. 
Warning message:
`fun.y` is deprecated. Use `fun` instead. 
Warning message:
`fun.y` is deprecated. Use `fun` instead.