userfriendlyscience (version 0.5-2)

dlvPlot: dlvPlot

Description

The dlvPlot function produces a dot-violin-line plot, and dlvTheme is the default theme.

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.

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-axisIf 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-axisAn object is returned with the following elements:

Details

This function creates Dot Violin Line plots. One image says more than a thousand words; I suggest you run the example :-)

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 density-sized dots might work better
# dlvPlot(dat, x='x1', y='y', z='x2', posDodge=.3);
# ## End(Not run)