# dlvPlot

##### dlvPlot

The dlvPlot function produces a dot-violin-line 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 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

##### Examples

```
# NOT RUN {
### 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);
# }
```

*Documentation reproduced from package userfriendlyscience, version 0.6-1, License: GPL (>= 2)*