Title:	A 'ggplot2' Extension Inspired by 'GraphPad Prism'
Version:	1.0.6
Description:	Provides various themes, palettes, and other functions that are used to customise ggplots to look like they were made in 'GraphPad Prism'. The 'Prism'-look is achieved with theme_prism() and scale_fill|colour_prism(), axes can be changed with custom guides like guide_prism_minor(), and significance indicators added with add_pvalue().
License:	GPL (≥ 3.0)
URL:	https://csdaw.github.io/ggprism/, https://github.com/csdaw/ggprism
BugReports:	https://github.com/csdaw/ggprism/issues
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Depends:	R (≥ 3.2)
Imports:	digest, ggplot2 (≥ 3.2.0), glue, grid, gtable (≥ 0.1.1), rlang (≥ 0.3.0), scales (≥ 0.5.0), stats, tibble, utils
Suggests:	covr, dplyr, ggbeeswarm, ggnewscale, knitr, magrittr, patchwork, rmarkdown, rstatix, tidyr, tinytest
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-05-17 10:19:04 UTC; csd51
Author:	Charlotte Dawson [aut, cre]
Maintainer:	Charlotte Dawson <csdaw@outlook.com>
Repository:	CRAN
Date/Publication:	2025-05-17 10:50:02 UTC

ggprism: A 'ggplot2' Extension Inspired by 'GraphPad Prism'

Description

Provides various themes, palettes, and other functions that are used to customise ggplots to look like they were made in 'GraphPad Prism'. The 'Prism'-look is achieved with theme_prism() and scale_fill|colour_prism(), axes can be changed with custom guides like guide_prism_minor(), and significance indicators added with add_pvalue().

Author(s)

Maintainer: Charlotte Dawson csdaw@outlook.com (ORCID)

See Also

Useful links:

• https://csdaw.github.io/ggprism/

• https://github.com/csdaw/ggprism

• Report bugs at https://github.com/csdaw/ggprism/issues

ggproto classes for ggprism

Description

ggproto classes for ggprism

Usage

StatBracket

GeomBracket

Format

An object of class StatBracket (inherits from Stat, ggproto, gg) of length 4.

An object of class GeomBracket (inherits from Geom, ggproto, gg) of length 5.

Add p-values to a ggplot

Description

Add p-values with or without brackets to a ggplot.

See here or the examples section below for examples of how to use.

add_pvalue is a refactored version of stat_pvalue_manual from kassambara/ggpubr, altered to have less dependencies, and more flexibility with input format and aesthetics. Any examples using stat_pvalue_manual found on Datanovia will also work with add_pvalue.

Usage

add_pvalue(
  data,
  label = NULL,
  xmin = "group1",
  xmax = "group2",
  x = NULL,
  y.position = "y.position",
  parse = FALSE,
  label.size = 3.2,
  colour = NULL,
  color = NULL,
  tip.length = 0.03,
  bracket.size = 0.6,
  bracket.colour = NULL,
  bracket.color = NULL,
  bracket.shorten = 0,
  bracket.nudge.y = 0,
  step.increase = 0,
  step.group.by = NULL,
  remove.bracket = FALSE,
  coord.flip = FALSE,
  position = "identity",
  ...
)

Arguments

Value

Returns a layer ggproto object with either geom = GeomBracket or geom = GeomText.

Allowed ... values

add_pvalue understands the following additional aesthetics or arguments:

fontface

string. Fontface of text (e.g. "bold").

fontfamily

string. Fontfamily of text (e.g. "Arial").

hjust

numeric. Horizontal justification of text.

vjust

numeric. Vertical justification of text.

alpha

numeric. Transparency of text and/or brackets.

linetype

string or numeric. Linetype of brackets (e.g. "dashed").

lineend

string. Lineend of brackets (e.g. "butt").

na.rm

logical. If FALSE (default), removes missing values with a warning. If TRUE silently removes missing values.

show.legend

logical. Should this layer be included in the legends? If NA (default), include if any aesthetics are mapped. If FALSE, never include or if TRUE, always include. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

logical. If FALSE, overrides the default aesthetics, rather than combining with them.

Examples

library(ggplot2)

## we will use the ToothGrowth dataset for all examples
tg <- ToothGrowth
tg$dose <- as.factor(tg$dose)
tg$group <- factor(rep(c("grp1", "grp2"), 30))

## p-value bracket comparing two means
# p-value table (its best to use these column names)
two.means <- tibble::tribble(
  ~group1, ~group2, ~p,     ~y.position,
  "OJ",    "VC",    0.0606, 36
)

# boxplot (or another geom...)
ggplot(tg, aes(x = supp, y = len)) +
  geom_boxplot() +
  add_pvalue(two.means)

# if your table has special column names you will need to specify them
two.means <- tibble::tribble(
  ~apple, ~banana, ~my.pval, ~some.y.position,
  "OJ",    "VC",    0.0606,  36
)

ggplot(tg, aes(x = supp, y = len)) +
  geom_boxplot() +
  add_pvalue(
    two.means,
    xmin = "apple",
    xmax = "banana",
    label = "my.pval",
    y.position = "some.y.position"
  )

## you can make the label a glue expression
two.means <- tibble::tribble(
  ~group1, ~group2, ~p,     ~y.position,
  "OJ",    "VC",    0.0606, 36
)

ggplot(tg, aes(x = supp, y = len)) +
  geom_boxplot() +
  add_pvalue(two.means, label = "p = {p}")

## you can change aesthetics of the bracket and label
ggplot(tg, aes(x = supp, y = len)) +
  geom_boxplot() +
  add_pvalue(
    two.means,
    label = "p = {p}",
    colour = "red", # label
    label.size = 6, # label
    fontface = "bold", # label
    fontfamily = "serif", # label
    angle = 45, # label
    bracket.colour = "blue", # bracket
    bracket.size = 1, # bracket
    linetype = "dashed", # bracket
    lineend = "round" # bracket
  )

## you can change the tip length of the bracket
# make them longer
ggplot(tg, aes(x = supp, y = len)) +
  geom_boxplot() +
  add_pvalue(two.means, tip.length = 0.1)

# make them disappear
ggplot(tg, aes(x = supp, y = len)) +
  geom_boxplot() +
  add_pvalue(two.means, tip.length = 0)

# make one side longer than the other
ggplot(tg, aes(x = supp, y = len)) +
  geom_boxplot() +
  add_pvalue(two.means, tip.length = c(0.1, 0))

## p-value brackets with comparisons to a reference sample
each.vs.ref <- tibble::tribble(
  ~group1, ~group2, ~p.adj,   ~y.position,
  "0.5",   "1",     8.80e-14, 35,
  "0.5",   "2",     1.27e-7,  38
)

ggplot(tg, aes(x = dose, y = len)) +
  geom_boxplot(aes(fill = dose)) +
  add_pvalue(each.vs.ref)

## p-value brackets with pairwise comparisons
pairwise <- tibble::tribble(
  ~group1, ~group2,  ~p.signif, ~y.position,
  "0.5",   "1",      "****",    38,
  "0.5",   "2",      "****",    36,
  "1",     "2",      "****",    38
)

# you can shorten the length of brackets that are close together
ggplot(tg, aes(x = dose, y = len)) +
  geom_boxplot(aes(fill = dose)) +
  add_pvalue(
    pairwise,
    bracket.shorten = c(0.05, 0, 0.05)
  )

# you can nudge brackets that are not quite in the correct y position
# instead of changing the p-value table
ggplot(tg, aes(x = dose, y = len)) +
  geom_boxplot(aes(fill = dose)) +
  add_pvalue(
    pairwise,
    bracket.shorten = c(0.05, 0, 0.05),
    bracket.nudge.y = c(0.5, 0, 0.5)
  )

## p-value brackets with pairwise comparisons of grouped data
pairwise.grouped <- tibble::tribble(
  ~group1, ~group2, ~p.adj,  ~y.position, ~supp,
  "0.5",   "1",     2.63e-4, 33.5,        "OJ",
  "0.5",   "2",     3.96e-6, 37.6,        "OJ",
  "1",     "2",     1.18e-1, 41.6,        "OJ",
  "0.5",   "1",     2.04e-6, 36.5,        "VC",
  "0.5",   "2",     1.40e-7, 40.6,        "VC",
  "1",     "2",     2.75e-4, 44.6,        "VC"
)

# use step.increase to change the spacing between different brackets in the
# groups specified by step.group.by
ggplot(tg, aes(x = dose, y = len)) +
  geom_boxplot(aes(fill = supp)) +
  add_pvalue(
    pairwise.grouped,
    colour = "supp",
    tip.length = 0,
    step.group.by = "supp",
    step.increase = 0.03
  )

## p-value (brackets) with single facet variable
two.means.grouped1 <- tibble::tribble(
  ~group1, ~group2, ~p.adj,  ~y.position, ~dose,
  "OJ",    "VC",    0.0127,  24,          "0.5",
  "OJ",    "VC",    0.00312, 30,          "1",
  "OJ",    "VC",    0.964,   36.5,        "2"
)

ggplot(tg, aes(x = supp, y = len)) +
  geom_boxplot() +
  facet_wrap(~ dose, scales = "free") +
  add_pvalue(two.means.grouped1) # table must have dose column

## p-value (brackets) with single facet variable and multiple brackets per facet
pairwise.grouped <- tibble::tribble(
  ~group1, ~group2, ~p.adj,  ~y.position, ~supp,
  "0.5",   "1",     2.63e-4, 33.5,        "OJ",
  "0.5",   "2",     3.96e-6, 37.6,        "OJ",
  "1",     "2",     1.18e-1, 41.6,        "OJ",
  "0.5",   "1",     2.04e-6, 36.5,        "VC",
  "0.5",   "2",     1.40e-7, 40.6,        "VC",
  "1",     "2",     2.75e-4, 44.6,        "VC"
)

ggplot(tg, aes(x = dose, y = len)) +
  geom_boxplot(aes(fill = supp)) +
  facet_wrap(~ supp) +
  add_pvalue(pairwise.grouped)

## p-value (brackets) with two facet variables
two.means.grouped2 <- tibble::tribble(
  ~group1, ~group2, ~p.signif, ~y.position, ~group, ~dose,
  "OJ",    "VC",    "*",       21,          "grp1", "0.5",
  "OJ",    "VC",    "**",      30,          "grp2", "1"
)

ggplot(tg, aes(x = supp, y = len)) +
  geom_boxplot() +
  facet_wrap(group ~ dose) +
  add_pvalue(two.means.grouped2) # table must have dose and group column

## p-value (text only) comparing two means
two.means <- tibble::tribble(
  ~group1, ~group2, ~p,     ~y.position,
  "OJ",    "VC",    0.0606, 36
)

ggplot(tg, aes(x = supp, y = len)) +
  geom_boxplot() +
  add_pvalue(two.means, remove.bracket = TRUE, x = 1.5)

## p-value (text only) with coord_flip, override y.position, change angle
ggplot(tg, aes(x = supp, y = len)) +
  geom_boxplot() +
  add_pvalue(
    two.means,
    remove.bracket = TRUE,
    x = 1.5,
    y.position = 32,
    angle = 45
  ) +
  coord_flip()

## p-value (text only) comparing to the null
one.mean <- tibble::tribble(
  ~group1, ~group2,      ~p.signif, ~y.position, ~dose,
  "1",     "null model", "****",    35,          "0.5",
  "1",     "null model", "****",    35,          "1",
  "1",     "null model", "****",    35,          "2"
)

ggplot(tg, aes(x = dose, y = len)) +
  geom_boxplot(aes(fill = dose)) +
  add_pvalue(one.mean, x = "dose")

## p-value (text only) with comparisons to a base mean
each.vs.basemean <- tibble::tribble(
  ~group1, ~group2, ~p.adj, ~y.position,
  "all",   "0.5",   "****", 35,
  "all",   "1",     "ns",   35,
  "all",   "2",     "****", 35
)

ggplot(tg, aes(x = dose, y = len)) +
  geom_boxplot(aes(fill = dose)) +
  add_pvalue(each.vs.basemean)

## p-value (text only) with comparison to reference sample
each.vs.ref <- tibble::tribble(
  ~group1, ~group2, ~p.adj,   ~y.position,
  "0.5",   "1",     8.80e-14, 35,
  "0.5",   "2",     1.27e-7,  38
)

ggplot(tg, aes(x = dose, y = len)) +
  geom_boxplot(aes(fill = dose)) +
  add_pvalue(each.vs.ref, coord.flip = TRUE, remove.bracket = TRUE)

## p-value (text only) with a grouping variable
two.means.grouped1 <- tibble::tribble(
  ~group1, ~group2, ~p.adj,  ~y.position, ~dose,
  "OJ",    "VC",    0.0127,  24,          "0.5",
  "OJ",    "VC",    0.00312, 30,          "1",
  "OJ",    "VC",    0.964,   36.5,        "2"
)

ggplot(tg, aes(x = dose, y = len)) +
  geom_boxplot(aes(fill = supp)) +
  add_pvalue(two.means.grouped1, x = "dose")

Add ticks as ggplot annotation

Description

This is an annotation function to add tick marks (major, minor, or both) to a ggplot. Clipping must be turned off if the ticks are to appear outside the plotting area, for example with: coord_cartesian(clip = "off").

Usage

annotation_ticks(
  sides = "b",
  type = "both",
  outside = FALSE,
  tick.length = unit(4.8, "pt"),
  minor.length = unit(2.4, "pt"),
  linewidth = 0.6,
  colour = "black",
  color = NULL,
  linetype = 1,
  lineend = "butt",
  alpha = 1,
  data = data.frame(x = NA)
)

Arguments

Value

Returns a layer ggproto object with geom = GeomTicks.

Source

The code is a slightly modified version of the answer to this Stack Overflow question 58485334, which is itself a refactored version of this annotation_ticks() function.

Examples

## Generally it is better to use the guide_prism_minor function.
## However annotation_ticks is useful in a few specific situations.
library(ggplot2)

## easily put ticks without labels around a plot with a border
ggplot(mtcars, aes(x = mpg, y = disp)) +
  geom_point() +
  theme_prism(border = TRUE) +
  coord_cartesian(clip = "off") +
  annotation_ticks(sides = "tr", type = "major", outside = TRUE) +
  theme(plot.margin = unit(c(4, 4, 4, 4), "mm"))

# the same but with minor ticks as well
ggplot(mtcars, aes(x = mpg, y = disp)) +
  geom_point() +
  scale_x_continuous(guide = "prism_minor") +
  scale_y_continuous(guide = "prism_minor") +
  theme_prism(border = TRUE) +
  coord_cartesian(clip = "off") +
  annotation_ticks(sides = "tr", type = "both", outside = TRUE) +
  theme(plot.margin = unit(c(4, 4, 4, 4), "mm"))

# you can adjust the appearance of annotation_ticks
ggplot(mtcars, aes(x = mpg, y = disp)) +
  geom_point() +
  theme_prism(border = TRUE) +
  coord_cartesian(clip = "off") +
  annotation_ticks(
    sides = "tr",
    type = "major",
    outside = TRUE,
    tick.length = unit(10, "pt"),
    colour = "red",
    linewidth = 2,
    linetype = "dashed",
    lineend = "round"
  ) +
  theme(plot.margin = unit(c(4, 4, 4, 4), "mm"))

## Unfortunately, due to the way they work, secondary axes don't always play
## well with the minor tick axes guides in this package.
## So we can use annotation_ticks instead.
sample.data <- data.frame(
  day = as.Date("2019-01-01") + 0:99,
  temperature = runif(100) + seq(1, 100)^2.5 / 10000,
  price = runif(100) + seq(100, 1)^1.5 / 10
)

# sample graph with secondary axis
ggplot(sample.data, aes(x = day)) +
  geom_line(aes(y = temperature), colour = "magenta") +
  geom_line(aes(y = price / 10), colour = "blue") +
  scale_y_continuous(sec.axis = sec_axis(~. * 10, name = "price")) +
  theme_prism(border = TRUE) +
  coord_cartesian(clip = "off")

# guide_prism_minor only works with the main axis in this case
ggplot(sample.data, aes(x = day)) +
  geom_line(aes(y = temperature), colour = "magenta") +
  geom_line(aes(y = price / 10), colour = "blue") +
  scale_y_continuous(
    sec.axis = sec_axis(~. * 10, name = "price"),
    guide = "prism_minor"
  ) +
  theme_prism(border = TRUE) +
  coord_cartesian(clip = "off")

# we use annotation_ticks to draw the minor ticks on the secondary axis
ggplot(sample.data, aes(x = day)) +
  geom_line(aes(y = temperature), colour = "magenta") +
  geom_line(aes(y = price / 10), colour = "blue") +
  scale_y_continuous(
    sec.axis = sec_axis(~. * 10, name = "price"),
    guide = "prism_minor"
  ) +
  theme_prism(border = TRUE) +
  coord_cartesian(clip = "off") +
  annotation_ticks(sides = "r", type = "minor", outside = TRUE)

Grob for bracket axes

Description

Grob for bracket axes.

Usage

draw_prism_bracket(
  break_positions,
  break_labels,
  axis_position,
  theme,
  check.overlap = FALSE,
  angle = NULL,
  n.dodge = 1,
  width = NULL,
  outside = TRUE
)

Arguments

Grob for axes with minor ticks

Description

Grob for axes with minor ticks

Usage

draw_prism_minor(
  break_positions,
  break_labels,
  breaks_major,
  axis_position,
  theme,
  check.overlap = FALSE,
  angle = NULL,
  n.dodge = 1
)

Arguments

Grob for offset axes

Description

Grob for offset axes

Usage

draw_prism_offset(
  break_positions,
  break_labels,
  axis_position,
  theme,
  check.overlap = FALSE,
  angle = NULL,
  n.dodge = 1
)

Arguments

Grob for offset axes with minor ticks

Description

Grob for offset axes with minor ticks

Usage

draw_prism_offset_minor(
  break_positions,
  break_labels,
  breaks_major,
  axis_position,
  theme,
  check.overlap = FALSE,
  angle = NULL,
  n.dodge = 1
)

Arguments

Palettes and theme data for ggprism

Description

This list object contains the strings and values used in ggprism themes and palettes.

Usage

ggprism_data

Format

An object of class list of length 4.

Axis guide with brackets

Description

This guide turns the axis into brackets drawn around each axis label.

Usage

guide_prism_bracket(
  title = waiver(),
  check.overlap = FALSE,
  angle = NULL,
  n.dodge = 1,
  order = 0,
  position = waiver(),
  width = NULL,
  outside = TRUE
)

Arguments

Details

The number of brackets can be adjusted using the breaks argument in ⁠scale_(x|y)_continuous()⁠ or ⁠scale_(x|y)_discrete()⁠.

Value

Returns a prism_bracket guide class object.

Examples

library(ggplot2)

## base plot
base <- ggplot(mpg, aes(x = as.factor(cyl), y = hwy)) +
  geom_jitter(width = 0.2) +
  theme(axis.line = element_line(colour = "black"))

## use brackets on x axis
# if not specified, the width of the brackets is guessed
base + scale_x_discrete(guide = "prism_bracket")

# you can add brackets using the guide function as well
base + guides(x = "prism_bracket")

## works with coord_flip
base + scale_x_discrete(guide = "prism_bracket") +
  coord_flip()

## adjust bracket width
base + scale_x_discrete(guide = guide_prism_bracket(width = 0.12))

## make brackets point inward
base + scale_x_discrete(guide = guide_prism_bracket(width = 0.12, outside = FALSE))

## change colour with the usual axis.line, axis.ticks, axis.text elements
base + scale_x_discrete(guide = guide_prism_bracket(width = 0.12, outside = FALSE)) +
  theme(axis.line.x = element_line(colour = "red"),
        axis.ticks.x = element_line(colour = "blue"),
        axis.text.x = element_text(colour = "green"))

Axis guide with minor ticks

Description

This guide is like the standard guide_axis, but with minor ticks.

Usage

guide_prism_minor(
  title = waiver(),
  check.overlap = FALSE,
  angle = NULL,
  n.dodge = 1,
  order = 0,
  position = waiver()
)

Arguments

Details

The number of minor ticks can be changed using the minor_breaks argument. Control the length of minor ticks by setting prism.ticks.length to a unit object using theme, for example: prism.ticks.length = unit(2, "pt"). The major tick lengths are adjusted using the standard axis.ticks.length.

Value

Returns a prism_minor guide class object.

Examples

library(ggplot2)

## base plot
base <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point()

## add minor ticks to x and y axes
base +
  scale_x_continuous(
    limits = c(0, 6),
    guide = "prism_minor"
  ) +
  scale_y_continuous(
    limits = c(10, 35),
    guide = "prism_minor"
  )

## you can also use the guides function to add minor ticks
base +
  guides(x = "prism_minor", y = "prism_minor")

## adjust number of minor ticks by adjusting minor breaks
base +
  scale_x_continuous(
    limits = c(0, 6),
    minor_breaks = seq(0, 6, 0.5),
    guide = "prism_minor"
  ) +
  scale_y_continuous(
    limits = c(10, 35),
    minor_breaks = seq(10, 35, 1.25),
    guide = "prism_minor"
  )

## adjust the length of major ticks with the usual axis.ticks.length element
base +
  scale_x_continuous(
    limits = c(0, 6),
    minor_breaks = seq(0, 6, 0.5),
    guide = "prism_minor"
  ) +
  scale_y_continuous(
    limits = c(10, 35),
    minor_breaks = seq(10, 35, 1.25),
    guide = "prism_minor"
  ) +
  theme(
    axis.ticks.length = unit(10, "pt")
  )

## adjust the length of minor ticks with a new prism.ticks.length element
base +
  scale_x_continuous(
    limits = c(0, 6),
    minor_breaks = seq(0, 6, 0.5),
    guide = "prism_minor"
  ) +
  scale_y_continuous(
    limits = c(10, 35),
    minor_breaks = seq(10, 35, 1.25),
    guide = "prism_minor"
  ) +
  theme(
    axis.ticks.length = unit(10, "pt"),
    prism.ticks.length = unit(5, "pt")
  )

## to get log10 minor ticks just use a log10 scale and set the minor breaks
ggplot(msleep, aes(bodywt, brainwt)) +
  geom_point(na.rm = TRUE) +
  scale_x_log10(limits = c(1e0, 1e4),
                minor_breaks = rep(1:9, 4)*(10^rep(0:3, each = 9)),
                guide = "prism_minor")

## change colour with the usual axis.ticks element
base +
  scale_x_continuous(
    limits = c(0, 6),
    minor_breaks = seq(0, 6, 0.5),
    guide = "prism_minor"
  ) +
  scale_y_continuous(
    limits = c(10, 35),
    minor_breaks = seq(10, 35, 1.25),
    guide = "prism_minor"
  ) +
  theme(
    axis.ticks.length = unit(10, "pt"),
    prism.ticks.length = unit(5, "pt"),
    axis.ticks = element_line(colour = "red")
  )

Offset axis guide

Description

This guide draws the axis only as wide as the outermost tick marks, similar to offset axes from Prism.

Usage

guide_prism_offset(
  title = waiver(),
  check.overlap = FALSE,
  angle = NULL,
  n.dodge = 1,
  order = 0,
  position = waiver()
)

Arguments

Details

Control the length of the axis by adjusting the breaks argument in ⁠scale_(x|y)_continuous()⁠ or ⁠scale_(x|y)_discrete()⁠.

Value

Returns a prism_offset guide class object.

Examples

library(ggplot2)

## base plot
base <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  theme(axis.line = element_line(colour = "black"))

## use offset guide via scale_x/y_continuous
base +
  scale_x_continuous(
    limits = c(1, 6),
    breaks = seq(1, 6, by = 1),
    guide = "prism_offset"
  ) +
  scale_y_continuous(
    guide = "prism_offset"
  )

## use offset guide via guides argument
base +
  guides(x = "prism_offset", y = "prism_offset") +
  scale_x_continuous(
    limits = c(1, 6),
    breaks = seq(1, 6, by = 1)
  )

## change colour and tick length with the usual elements
base +
  scale_x_continuous(
    limits = c(0, 6),
    minor_breaks = seq(0, 6, 0.5),
    guide = "prism_offset"
  ) +
  scale_y_continuous(
    limits = c(10, 35),
    minor_breaks = seq(10, 35, 1.25),
    guide = "prism_offset"
  ) +
  theme(
    axis.ticks.length = unit(10, "pt"),
    axis.ticks = element_line(colour = "red"),
    axis.line = element_line(colour = "blue")
  )

Offset axis guide with minor ticks

Description

This guide draws the axis only as wide as the outermost tick marks, similar to offset axes from Prism. It also adds minor ticks.

Usage

guide_prism_offset_minor(
  title = waiver(),
  check.overlap = FALSE,
  angle = NULL,
  n.dodge = 1,
  order = 0,
  position = waiver()
)

Arguments

Details

Control the length of the axis by adjusting the breaks argument in ⁠scale_(x|y)_continuous()⁠ or ⁠scale_(x|y)_discrete()⁠. Similarly, the number of minor ticks can be changed using the minor_breaks argument.

Control the length of minor ticks by setting prism.ticks.length to a unit object using theme, for example: prism.ticks.length = unit(2, "pt"). The major tick lengths are adjusted using the standard axis.ticks.length.

Value

Returns a prism_offset_minor guide class object.

Examples

library(ggplot2)

## base plot
base <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  theme(axis.line = element_line(colour = "black"))

## add minor ticks to x and y axes
base +
  scale_x_continuous(
    limits = c(0, 6),
    guide = "prism_offset_minor"
  ) +
  scale_y_continuous(
    limits = c(10, 35),
    guide = "prism_offset_minor"
  )

## you can also use the guides function to add minor ticks
base +
  guides(x = "prism_offset_minor", y = "prism_offset_minor")

## adjust number of minor ticks by adjusting minor breaks
base +
  scale_x_continuous(
    limits = c(0, 6),
    minor_breaks = seq(0, 6, 0.5),
    guide = "prism_offset_minor"
  ) +
  scale_y_continuous(
    limits = c(10, 35),
    minor_breaks = seq(10, 35, 1.25),
    guide = "prism_offset_minor"
  )

## adjust the length of major ticks with the usual axis.ticks.length element
base +
  scale_x_continuous(
    limits = c(0, 6),
    minor_breaks = seq(0, 6, 0.5),
    guide = "prism_offset_minor"
  ) +
  scale_y_continuous(
    limits = c(10, 35),
    minor_breaks = seq(10, 35, 1.25),
    guide = "prism_offset_minor"
  ) +
  theme(
    axis.ticks.length = unit(10, "pt")
  )

## adjust the length of minor ticks with a new prism.ticks.length element
base +
  scale_x_continuous(
    limits = c(0, 6),
    minor_breaks = seq(0, 6, 0.5),
    guide = "prism_offset_minor"
  ) +
  scale_y_continuous(
    limits = c(10, 35),
    minor_breaks = seq(10, 35, 1.25),
    guide = "prism_offset_minor"
  ) +
  theme(
    axis.ticks.length = unit(10, "pt"),
    prism.ticks.length = unit(5, "pt")
  )

## to get log10 minor ticks just use a log10 scale and set the minor breaks
ggplot(msleep, aes(bodywt, brainwt)) +
  geom_point(na.rm = TRUE) +
  scale_x_log10(limits = c(1e0, 1e4),
                minor_breaks = rep(1:9, 4)*(10^rep(0:3, each = 9)),
                guide = "prism_offset_minor") +
  theme(axis.line = element_line(colour = "black"))

## change colour and tick length with the usual elements
base +
  scale_x_continuous(
    limits = c(0, 6),
    minor_breaks = seq(0, 6, 0.5),
    guide = "prism_offset_minor"
  ) +
  scale_y_continuous(
    limits = c(10, 35),
    minor_breaks = seq(10, 35, 1.25),
    guide = "prism_offset_minor"
  ) +
  theme(
    axis.ticks.length = unit(10, "pt"),
    prism.ticks.length = unit(5, "pt"),
    axis.ticks = element_line(colour = "red"),
    axis.line = element_line(colour = "blue")
  )

Preview Prism themes

Description

Quickly generate a preview of a ggprism theme. See names(ggprism_data$themes) for valid palette names.

Usage

preview_theme(palette)

Arguments

Value

Returns an object of class ggplot.

Examples

library(ggplot2)

## see names of available themes
names(ggprism_data$themes)

## preview a theme
preview_theme("floral")

Prism colour palettes

Description

A collection of colour palettes which mirror the colour schemes available in GraphPad Prism.

Usage

prism_colour_pal(palette = "colors")

prism_color_pal(palette = "colors")

Arguments

Value

Returns a function which takes a single integer as its only argument and returns a character vector of hexadecimal colours. See the examples below for usage.

Examples

library(ggplot2)

## list all available colour palettes and their lengths
lengths(ggprism_data$colour_palettes)

## select some colours from a palette
prism_colour_pal(palette = "starry")(4)

## see all the colours in a specific palette
# define a function for convenience
library(scales)

show_palette <- function(palette) {
  scales::show_col(
    prism_colour_pal(palette = palette)(
      attr(prism_colour_pal(palette = palette), "max_n")
    )
  )
}

# show the colours in the palette "pearl"
show_palette("pearl")

Prism fill palettes

Description

A collection of fill palettes which mirror the colour schemes available in GraphPad Prism.

Usage

prism_fill_pal(palette = "colors")

Arguments

Value

Returns a function which takes a single integer as its only argument and returns a character vector of hexadecimal colours. See the examples below for usage.

Examples

library(ggplot2)

## list all available fill palettes and their lengths
lengths(ggprism_data$fill_palettes)

## select some colours from a palette
prism_fill_pal(palette = "summer")(4)

## see all the colours in a specific palette
# define a function for convenience
library(scales)

show_palette <- function(palette) {
  scales::show_col(
    prism_fill_pal(palette = palette)(
      attr(prism_fill_pal(palette = palette), "max_n")
    )
  )
}

# show the colours in the palette "pearl"
show_palette("floral")

Prism shape palettes

Description

Shape palettes that approximate those used in GraphPad Prism. No unicode characters are used, only the default symbols available in R.

Usage

prism_shape_pal(palette = c("default", "filled", "complete"))

Arguments

Details

The default palette supports up to 9 values. It does not use any symbols with a fill.

The filled palette supports up to 10 values. The first 5 symbols have a fill.

The complete palette supports up to 14 values. Symbols 5 to 9 have a fill.

Value

Returns a function which takes a single integer as its only argument and returns a character vector of integers which correspond to R plot pch symbols. See the examples below for usage.

Examples

library(ggplot2)

## list all available shape palettes
ggprism_data$shape_palettes

## select some shapes from a palette
prism_shape_pal(palette = "filled")(4)

## see all the shapes in a specific palette
# define a function for convenience
show_shapes <- function(palette) {
  df_shapes <- ggprism_data$shape_palettes[[palette]][, -1]
  df_shapes$pch_f <- factor(df_shapes$pch, levels = df_shapes$pch)

  ggplot(df_shapes, aes(x = 0, y = 0, shape = pch)) +
    geom_point(aes(shape = pch), size = 5, fill = 'red') +
    scale_shape_identity() +
    facet_wrap(~ pch_f) +
    theme_void()
}

# show the shapes in the palette "complete"
show_shapes("complete")

Prism colour scales (discrete)

Description

A collection of discrete colour scales that use palettes which mirror the colour schemes available in GraphPad Prism.

Usage

scale_colour_prism(palette = "colors", ...)

scale_color_prism(palette = "colors", ...)

Arguments

Value

Returns a ggproto object of class ScaleDiscrete which works with colour aesthetics.

Examples

library(ggplot2)

## base plot
base <- ggplot(mtcars, aes(x = wt, y = mpg, colour = factor(cyl))) +
  geom_point(size = 3)

## works pretty much the same as ggplot2 scale_colour_manual
base +
  scale_colour_prism(palette = "candy_bright")

## try combining the ggprism colour and fill scales
base2 <- ggplot(mpg, aes(x = class, y = hwy, fill = class, colour = class)) +
  geom_boxplot()

base2 +
  scale_fill_prism(palette = "floral") +
  scale_colour_prism(palette = "floral")

## change colour scale title in legend
base +
  scale_colour_prism(
    palette = "candy_bright",
    name = "Cylinders"
  )

## change colour labels in legend
base +
  scale_colour_prism(
    palette = "candy_bright",
    name = "Cylinders",
    label = c("4 cyl", "6 cyl", "8 cyl")
  )

## change colour labels in legend with a function
base +
  scale_colour_prism(
    palette = "candy_bright",
    name = "Cylinders",
    label = function(x) paste(x, "cyl")
  )

## change order of colours in legend
base +
  scale_colour_prism(
    palette = "candy_bright",
    name = "Cylinders",
    label = function(x) paste(x, "cyl"),
    breaks = c(8, 4, 6)
  )

## to change which colour is assigned to which cyl
## you need to change the factor levels in the underlying data
base <- ggplot(mtcars, aes(x = wt, y = mpg,
                           colour = factor(cyl, levels = c(6, 4, 8)))) +
  geom_point(size = 3)

base +
  scale_colour_prism(
    palette = "candy_bright",
    name = "Cylinders"
  )

Prism fill scales (discrete)

Description

A collection of discrete fill scales that use palettes which mirror the colour schemes available in GraphPad Prism.

Usage

scale_fill_prism(palette = "colors", ...)

Arguments

Value

Returns a ggproto object of class ScaleDiscrete which works with fill aesthetics.

Examples

library(ggplot2)

## base plot
base <- ggplot(mtcars, aes(x = mpg, fill = factor(cyl))) +
  geom_density(alpha = 0.75)

## works pretty much the same as ggplot2 scale_fill_manual
base +
  scale_fill_prism(palette = "candy_bright")

## try combining the ggprism colour and fill scales
base2 <- ggplot(mtcars, aes(x = mpg, fill = factor(cyl), colour = factor(cyl))) +
  geom_density(alpha = 0.75)

base2 +
  scale_fill_prism(palette = "floral") +
  scale_colour_prism(palette = "floral")

## change fill scale title in legend
base +
  scale_fill_prism(
    palette = "candy_bright",
    name = "Cylinders"
  )

## change fill labels in legend
base +
  scale_fill_prism(
    palette = "candy_bright",
    name = "Cylinders",
    label = c("4 cyl", "6 cyl", "8 cyl")
  )

## change fill labels in legend with a function
base +
  scale_fill_prism(
    palette = "candy_bright",
    name = "Cylinders",
    label = function(x) paste(x, "cyl")
  )

## change order of fills in legend
base +
  scale_fill_prism(
    palette = "candy_bright",
    name = "Cylinders",
    label = function(x) paste(x, "cyl"),
    breaks = c(8, 4, 6)
  )

## to change which fill is assigned to which cyl
## you need to change the factor levels in the underlying data
base <- ggplot(mtcars, aes(x = mpg,
                           fill = factor(cyl, levels = c(6, 4, 8)))) +
  geom_density(alpha = 0.75)

base +
  scale_fill_prism(
    palette = "candy_bright",
    name = "Cylinders"
  )

Prism shape scales (discrete)

Description

Shape scales that approximate those used in GraphPad Prism. No unicode characters are used, only the default symbols available in R.

Usage

scale_shape_prism(palette = "default", ...)

Arguments

Details

The default palette supports up to 9 values. It does not use any symbols with a fill.

The filled palette supports up to 10 values. The first 5 symbols have a fill.

The complete palette supports up to 14 values. Symbols 5 to 9 have a fill.

Value

Returns a ggproto object of class ScaleDiscrete which works with shape aesthetics.

Examples

library(ggplot2)

## list all available shape palettes
ggprism_data$shape_palettes

## define a base plot
base <- ggplot(mtcars, aes(x = wt, y = mpg,
                           shape = factor(cyl))) +
  geom_point(size = 3)

## works pretty much the same as ggplot2 scale_shape_manual
base +
  scale_shape_prism(palette = "complete")

## change shape scale title in legend
base +
  scale_shape_prism(
    palette = "default",
    name = "Cylinders"
  )

## change shape labels in legend
base +
  scale_shape_prism(
    palette = "default",
    name = "Cylinders",
    label = c("4 cyl", "6 cyl", "8 cyl")
  )

## change shape labels in legend with a function
base +
  scale_shape_prism(
    palette = "default",
    name = "Cylinders",
    label = function(x) paste(x, "cyl")
  )

## change order of shapes in legend
base +
  scale_shape_prism(
    palette = "default",
    name = "Cylinders",
    label = function(x) paste(x, "cyl"),
    breaks = c(8, 4, 6)
  )

## to change which shape is assigned to which cyl
## you need to change the factor levels in the underlying data
base <- ggplot(mtcars, aes(x = wt, y = mpg,
                           shape = factor(cyl, levels = c(6, 4, 8)))) +
  geom_point(size = 3)

base +
  scale_shape_prism(
    palette = "default",
    name = "Cylinders"
  )

## see all the shapes in a specific palette
# define a function for convenience
show_shapes <- function(palette) {
  df_shapes <- ggprism_data$shape_palettes[[palette]][, -1]
  df_shapes$pch_f <- factor(df_shapes$pch, levels = df_shapes$pch)

  ggplot(df_shapes, aes(x = 0, y = 0, shape = pch)) +
    geom_point(aes(shape = pch), size = 5, fill = 'red') +
    scale_shape_identity() +
    facet_wrap(~ pch_f) +
    theme_void()
}

# show the shapes in the palette "complete"
show_shapes("complete")

Prism themes

Description

A collection of ggplot2 themes that use palettes which mirror the colour schemes available in GraphPad Prism.

Usage

theme_prism(
  palette = "black_and_white",
  base_size = 14,
  base_family = "sans",
  base_fontface = "bold",
  base_line_size = base_size/14,
  base_rect_size = base_size/14,
  axis_text_angle = 0,
  border = FALSE
)

Arguments

Value

Returns a list-like object of class theme.

Examples

library(ggplot2)

# see ?preview_theme for a convenient function to preview ggprism themes
# before using theme_prism

## base plot
base <- ggplot(mpg, aes(x = displ, y = cty, colour = class)) +
  geom_point()

## default palette is "black_and_white"
## default base_size is 14 (compared with 11 for theme_grey)
base +
  theme_prism()

## try some other palettes
base +
  theme_prism(palette = "office")

base +
  theme_prism(palette = "flames")

## try matching the theme_prism palette with same colour palette
base +
  theme_prism(palette = "stained_glass") +
  scale_color_prism(palette = "stained_glass")

base +
  theme_prism(palette = "candy_bright") +
  scale_color_prism(palette = "candy_bright")

## change the font face
base +
  theme_prism(base_fontface = "plain")

## change the font family
base +
  theme_prism(base_family = "serif")

## base_line_size scales automatically as you change base_size
base +
  theme_prism(base_size = 10)

## but you can also change it manually
base +
  theme_prism(base_size = 16, base_line_size = 0.8)

## easily change x axis text angle
base +
  theme_prism(axis_text_angle = 45)

## add a border (need to turn off clipping)
base +
  theme_prism(border = TRUE) +
  coord_cartesian(clip = "off")

## change border thickness
base +
  theme_prism(border = TRUE, base_rect_size = 2) +
  coord_cartesian(clip = "off")

Wing morphology of mutant flies

Description

Fold changes of different measures of wing morphology in heterozygous (Tps1MIC/+) and homozygous (Tps1MIC) Tps1 mutant flies. Data are expressed as percentage change relative to the mean of the heterozygous mutants.

Usage

wings

Format

An object of class tbl_df (inherits from tbl, data.frame) with 120 rows and 4 columns.

Details

40 flies were measured in total, with 3 measurements taken per fly.

sex

factor. Male or female.

genotype

factor. Heterozygous (Tps1MIC/+) or homozygous (Tps1MIC) mutant

measure

factor. Type of wing measurement: wing size, cell size, or cell number

percent.change

double. Value measured.

References

Matsushita, R, Nishimura, T. Trehalose metabolism confers developmental robustness and stability in Drosophila by regulating glucose homeostasis. Commun Biol 3, 170 (2020). doi:10.1038/s42003-020-0889-1