Easy violin plots

Make nice violin plots easily with 95% (possibly bootstrapped) confidence intervals.

Usage

nice_violin(
  data,
  response,
  group = NULL,
  boot = FALSE,
  bootstraps = 2000,
  colours,
  xlabels = NULL,
  ytitle = response,
  xtitle = NULL,
  has.ylabels = TRUE,
  has.xlabels = TRUE,
  comp1 = 1,
  comp2 = 2,
  signif_annotation = NULL,
  signif_yposition = NULL,
  signif_xmin = NULL,
  signif_xmax = NULL,
  ymin,
  ymax,
  yby = 1,
  CIcap.width = 0.1,
  obs = FALSE,
  alpha = 1,
  border.colour = "black",
  border.size = 2,
  has.d = FALSE,
  d.x = mean(c(comp1, comp2)) * 1.1,
  d.y = mean(data[[response]]) * 1.3,
  groups.order = "none",
  xlabels.angle = 0
)

Arguments

data: The data frame.
response: The dependent variable to be plotted.
group: The group by which to plot the variable.
boot: Logical, whether to use bootstrapping for the confidence interval or not.
bootstraps: How many bootstraps to use.
colours: Desired colours for the plot, if desired.
xlabels: The individual group labels on the x-axis.
ytitle: An optional y-axis label, if desired.
xtitle: An optional x-axis label, if desired.
has.ylabels: Logical, whether the x-axis should have labels or not.
has.xlabels: Logical, whether the y-axis should have labels or not.
comp1: The first unit of a pairwise comparison, if the goal is to compare two groups. Automatically displays *, **, or *** depending on significance of the difference. Can take either a numeric value (based on the group number) or the name of the group directly. Must be provided along with argument comp2.
comp2: The second unit of a pairwise comparison, if the goal is to compare two groups. Automatically displays "", "", or "" depending on significance of the difference. Can take either a numeric value (based on the group number) or the name of the group directly. Must be provided along with argument comp1.
signif_annotation: Manually provide the required annotations/numbers of stars (as character strings). Useful if the automatic pairwise comparison annotation does not work as expected, or yet if one wants more than one pairwise comparison. Must be provided along with arguments signif_yposition, signif_xmin, and signif_xmax.
signif_yposition: Manually provide the vertical position of the annotations/stars, based on the y-scale.
signif_xmin: Manually provide the first part of the horizontal position of the annotations/stars (start of the left-sided bracket), based on the x-scale.
signif_xmax: Manually provide the second part of the horizontal position of the annotations/stars (end of the right-sided bracket), based on the x-scale.
ymin: The minimum score on the y-axis scale.
ymax: The maximum score on the y-axis scale.
yby: How much to increase on each "tick" on the y-axis scale.
CIcap.width: The width of the confidence interval cap.
obs: Logical, whether to plot individual observations or not. The type of plotting can also be specified, either "dotplot" (same as obs = TRUE for backward compatibility) or "jitter", useful when there are a lot of observations.
alpha: The transparency of the plot.
border.colour: The colour of the violins border.
border.size: The size of the violins border.
has.d: Whether to display the d-value.
d.x: The x-axis coordinates for the d-value.
d.y: The y-axis coordinates for the d-value.
groups.order: How to order the group factor levels on the x-axis. Either "increasing" or "decreasing", to order based on the value of the variable on the y axis, or "string.length", to order from the shortest to the longest string (useful when working with long string names). "Defaults to "none".
xlabels.angle: How much to tilt the labels of the x-axis. Useful when working with long string names. "Defaults to 0.

Value

A violin plot of class ggplot, by group.

Details

Using boot = TRUE uses bootstrapping (for the confidence intervals only) with the BCa method, using the rcompanion_groupwiseMean function.

For the easystats equivalent, see: see::geom_violindot().

Examples

# Make the basic plot
nice_violin(
  data = ToothGrowth,
  response = "len"
)

# \donttest{
.old_wd <- setwd(tempdir())
# Save a high-resolution image file to specified directory
ggplot2::ggsave("niceviolinplothere.pdf", width = 7,
  height = 7, unit = "in", dpi = 300
) # change for your own desired path
setwd(.old_wd)
# Change x- and y- axes labels
nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  ytitle = "Length of Tooth",
  xtitle = "Vitamin C Dosage"
)


# See difference between two groups
nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  comp1 = "0.5",
  comp2 = "2"
)


nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  comp1 = 2,
  comp2 = 3
)


# Compare all three groups
nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  signif_annotation = c("*", "**", "***"),
  # manually enter the number of stars
  signif_yposition = c(30, 35, 40),
  # What height (y) should the stars appear
  signif_xmin = c(1, 2, 1),
  # Where should the left-sided brackets start (x)
  signif_xmax = c(2, 3, 3)
)

# Where should the right-sided brackets end (x)

# Set the colours manually
nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  colours = c("darkseagreen", "cadetblue", "darkslateblue")
)


# Changing the names of the x-axis labels
nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  xlabels = c("Low", "Medium", "High")
)


# Removing the x-axis or y-axis titles
nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  ytitle = NULL,
  xtitle = NULL
)


# Removing the x-axis or y-axis labels (for whatever purpose)
nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  has.ylabels = FALSE,
  has.xlabels = FALSE
)


# Set y-scale manually
nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  ymin = 5,
  ymax = 35,
  yby = 5
)
#> Warning: Removed 1 rows containing non-finite values (`stat_ydensity()`).


# Plotting individual observations
nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  obs = TRUE
)
#> Bin width defaults to 1/30 of the range of the data. Pick better value with
#> `binwidth`.


# Micro-customizations
nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  CIcap.width = 0,
  alpha = .70,
  border.size = 1,
  border.colour = "white",
  comp1 = 1,
  comp2 = 2,
  has.d = TRUE
)

# }

Usage

Arguments

Value

Details

See also

Examples