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Add pre-computed statistics

Source: R/add-stats.R
add_test_pvalue_manual.Rd

Add pre-computed statistics

Usage

add_test_pvalue_manual(
  plot,
  data,
  padding_top = 0.15,
  label = "{tidyplots::format_p_value(p, 0.0001)}",
  label.size = 7/ggplot2::.pt,
  y.position = "y.position",
  xmin = "group1",
  xmax = "group2",
  step.increase = 0.15,
  vjust = -0.25,
  bracket.nudge.y = 0.1,
  bracket.size = 0.3,
  tip.length = 0.03,
  hide.ns = FALSE,
  remove.bracket = FALSE,
  color = plot$tidyplot$ink,
  ...
)

add_test_asterisks_manual(
  plot,
  data,
  padding_top = 0.1,
  label = "p.signif",
  label.size = 10/ggplot2::.pt,
  y.position = "y.position",
  xmin = "group1",
  xmax = "group2",
  step.increase = 0.2,
  vjust = 0.3,
  bracket.nudge.y = 0.15,
  bracket.size = 0.3,
  tip.length = 0.03,
  hide.ns = TRUE,
  remove.bracket = FALSE,
  color = plot$tidyplot$ink,
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().

data

a data frame containing statitistical test results. The expected default format should contain the following columns: group1 | group2 | p | y.position | etc. group1 and group2 are the groups that have been compared. p is the resulting p-value. y.position is the y coordinates of the p-values in the plot.

padding_top

Extra padding above the data points to accommodate the statistical comparisons.

label

the column containing the label (e.g.: label = "p" or label = "p.adj"), where p is the p-value. Can be also an expression that can be formatted by the glue() package. For example, when specifying label = "t-test, p = {p}", the expression {p} will be replaced by its value.

label.size

Font size of label text.

y.position

column containing the coordinates (in data units) to be used for absolute positioning of the label. Default value is "y.position". Can be also a numeric vector.

xmin

column containing the position of the left sides of the brackets. Default value is "group1".

xmax

(optional) column containing the position of the right sides of the brackets. Default value is "group2". If NULL, the p-values are plotted as a simple text.

step.increase

numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap.

vjust

move the text up or down relative to the bracket. Can be also a column name available in the data.

bracket.nudge.y

Vertical adjustment to nudge brackets by. Useful to move up or move down the bracket. If positive value, brackets will be moved up; if negative value, brackets are moved down.

bracket.size

Width of the lines of the bracket.

tip.length

numeric vector with the fraction of total height that the bar goes down to indicate the precise column. Default is 0.03.

hide.ns

can be logical value or a character vector.

  • Case when logical value. If TRUE, hide ns symbol when displaying significance levels. Filter is done by checking the column p.adj.signif, p.signif, p.adj and p.

  • Case when character value. Possible values are "p" or "p.adj", for filtering out non significant.

remove.bracket

logical, if TRUE, brackets are removed from the plot. Considered only in the situation, where comparisons are performed against reference group or against "all".

color

text and line color. Can be variable name in the data for coloring by groups.

...

Arguments passed on to ggpubr::stat_pvalue_manual().

Value

A tidyplot object.

Details

  • add_test_pvalue_manual() and add_test_asterisks_manual() use ggpubr::stat_pvalue_manual(). Check there for additional arguments. These functions take pre-computed statistics as input via the data parameter.

  • Known limitation: add_test_pvalue_manual() and add_test_asterisks_manual() expect a discrete variable on the x-axis and a continuous variable on the y-axis. To produce horizontal plots, use flip_plot().

Examples

# Add manual p-values from pre-computed statistics
stat_df <- data.frame(
  group1 = "A", group2 = "B",
  p = 0.025, p.signif = "*",
  y.position = 50
)
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points() |>
  add_test_pvalue_manual(data = stat_df)


# Add manual asterisks
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points() |>
  add_test_asterisks_manual(data = stat_df)