Package 'tidyplots'

Title: Tidy Plots for Scientific Papers
Description: The goal of 'tidyplots' is to streamline the creation of publication-ready plots for scientific papers. It allows to gradually add, remove and adjust plot components using a consistent and intuitive syntax.
Authors: Jan Broder Engler [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-3169-2076>)
Maintainer: Jan Broder Engler <[email protected]>
License: MIT + file LICENSE
Version: 0.4.0.9000
Built: 2026-06-01 08:01:05 UTC
Source: https://github.com/jbengler/tidyplots

Help Index

Add ggplot2 code to a tidyplot

Description

Add ggplot2 code to a tidyplot

Usage

add()

Value

A tidyplot object.

Examples

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add(ggplot2::geom_point())

Add annotation

Description

Add annotation

Usage

add_annotation_text(plot, text, x, y, fontsize = 7, ...)

add_annotation_rectangle(
  plot,
  xmin,
  xmax,
  ymin,
  ymax,
  fill = plot$tidyplot$ink,
  color = NA,
  alpha = 0.1,
  ...
)

add_annotation_line(plot, x, xend, y, yend, color = plot$tidyplot$ink, ...)

Arguments

plot

A tidyplot generated with the function tidyplot().
text

String for annotation text.
x, xmin, xmax, xend, y, ymin, ymax, yend

Coordinates for the annotation.
fontsize

Font size in points. Defaults to 7.
...

Arguments passed on to ggplot2::annotate().
fill

A hex color for the fill color. For example, "#FFFFFF" for white.
color

A hex color for the stroke color. For example, "#FFFFFF" for white.
alpha

A number between 0 and 1 for the opacity of an object. A value of 0 is completely transparent, 1 is completely opaque.

Value

A tidyplot object.

Examples

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_boxplot() |>
  add_annotation_text("Look here!", x = 2, y = 25)

eu_countries |>
  tidyplot(x = area, y = population) |>
  add_data_points() |>
  add_annotation_rectangle(xmin = 2.5e5, xmax = Inf, ymin = 42, ymax = Inf)

eu_countries |>
  tidyplot(x = area, y = population) |>
  add_data_points() |>
  add_annotation_rectangle(xmin = 2.5e5, xmax = 6e5, ymin = 42, ymax = 90,
                           color = "#E69F00", fill = NA)

eu_countries |>
  tidyplot(x = area, y = population) |>
  add_data_points() |>
  add_annotation_line(x = 0, xend = Inf, y = 0, yend = Inf)

Add area stack

Description

Add area stack

Usage

add_areastack_absolute(
  plot,
  linewidth = 0.25,
  alpha = 0.4,
  reverse = FALSE,
  replace_na = FALSE,
  ...
)

add_areastack_relative(
  plot,
  linewidth = 0.25,
  alpha = 0.4,
  reverse = FALSE,
  replace_na = FALSE,
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.
alpha

A number between 0 and 1 for the opacity of an object. A value of 0 is completely transparent, 1 is completely opaque.
reverse

Whether the order should be reversed or not. Defaults to FALSE, meaning not reversed.
replace_na

Whether to replace count = NA with count = 0.
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

# for a `count` provide `x` and `color`
# `count` of the data points in each `energy_type` category
energy |>
  tidyplot(x = year, color = energy_type) |>
  add_areastack_absolute()

energy |>
  tidyplot(x = year, color = energy_type) |>
  add_areastack_relative()

# for a `sum` provide `x`, `y` and `color`
# `sum` of `energy` in each `energy_type` category
energy |>
  tidyplot(x = year, y = energy, color = energy_type) |>
  add_areastack_absolute()

energy |>
  tidyplot(x = year, y = energy, color = energy_type) |>
  add_areastack_relative()

# Flip x and y-axis
energy |>
  tidyplot(x = energy, y = year, color = energy_type) |>
  add_areastack_absolute(orientation = "y")

energy |>
  tidyplot(x = energy, y = year, color = energy_type) |>
  add_areastack_relative(orientation = "y")

Add bar stack

Description

Add bar stack

Usage

add_barstack_absolute(plot, width = 0.8, reverse = FALSE, ...)

add_barstack_relative(plot, width = 0.8, reverse = FALSE, ...)

Arguments

plot

A tidyplot generated with the function tidyplot().
width

Horizontal width of the plotted object (bar, error bar, boxplot, violin plot, etc). Typical values range between 0 and 1.
reverse

Whether the order should be reversed or not. Defaults to FALSE, meaning not reversed.
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

# for a `count` only provide `color`
# `count` of the data points in each `energy_type` category
energy |>
  tidyplot(color = energy_type) |>
  add_barstack_absolute()

energy |>
  tidyplot(color = energy_type) |>
  add_barstack_relative()

# for a `sum` provide `color` and `y`
# `sum` of `energy` in each `energy_type` category
energy |>
  tidyplot(y = energy, color = energy_type) |>
  add_barstack_absolute()

energy |>
  tidyplot(y = energy, color = energy_type) |>
  add_barstack_relative()

# Include variable on second axis
energy |>
  tidyplot(x = year, y = energy, color = energy_type) |>
  add_barstack_absolute()

energy |>
  tidyplot(x = year, y = energy, color = energy_type) |>
  add_barstack_relative()

# Flip x and y-axis
energy |>
  tidyplot(x = energy, y = year, color = energy_type) |>
  add_barstack_absolute(orientation = "y")

energy |>
  tidyplot(x = energy, y = year, color = energy_type) |>
  add_barstack_relative(orientation = "y")

Add boxplot

Description

Add boxplot

Usage

add_boxplot(
  plot,
  dodge_width = NULL,
  alpha = 0.3,
  saturation = 1,
  show_whiskers = TRUE,
  show_outliers = TRUE,
  box_width = 0.6,
  whiskers_width = 0.8,
  outlier.size = 0.5,
  coef = 1.5,
  outlier.shape = 19,
  outlier.alpha = 1,
  linewidth = 0.25,
  preserve = "total",
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
alpha

A number between 0 and 1 for the opacity of an object. A value of 0 is completely transparent, 1 is completely opaque.
saturation

A number between 0 and 1 for the color saturation of an object. A value of 0 is completely desaturated (white), 1 is the original color.
show_whiskers

Whether to show boxplot whiskers. Defaults to TRUE.
show_outliers

Whether to show outliers. Defaults to TRUE.
box_width

Width of the boxplot. Defaults to 0.6.
whiskers_width

Width of the whiskers. Defaults to 0.8.
outlier.size

Size of the outliers. Defaults to 0.5.
coef

Length of the whiskers as multiple of IQR. Defaults to 1.5.
outlier.shape

Shape of the outliers. Defaults to 19.
outlier.alpha

Opacity of the outliers. Defaults to 1.
linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.
preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_boxplot()

# Changing arguments:
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_boxplot(show_whiskers = FALSE)

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_boxplot(show_outliers = FALSE)

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_boxplot(box_width = 0.2)

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_boxplot(whiskers_width = 0.2)

Add count

Description

Add count

Usage

add_count_bar(
  plot,
  dodge_width = NULL,
  width = 0.6,
  saturation = 1,
  preserve = "total",
  ...
)

add_count_dash(
  plot,
  dodge_width = NULL,
  width = 0.6,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_count_dot(plot, dodge_width = NULL, size = 2, preserve = "total", ...)

add_count_value(
  plot,
  dodge_width = NULL,
  accuracy = 0.1,
  scale_cut = NULL,
  fontsize = 7,
  extra_padding = 0.15,
  vjust = NULL,
  hjust = NULL,
  preserve = "total",
  ...
)

add_count_line(
  plot,
  group,
  dodge_width = NULL,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_count_area(
  plot,
  group,
  dodge_width = NULL,
  linewidth = 0.25,
  preserve = "total",
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
width

Horizontal width of the plotted object (bar, error bar, boxplot, violin plot, etc). Typical values range between 0 and 1.
saturation

A number between 0 and 1 for the color saturation of an object. A value of 0 is completely desaturated (white), 1 is the original color.
preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?
...

Arguments passed on to the geom function.
linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.
size

A number representing the size of the plot symbol. Typical values range between 1 and 3.
accuracy

A number to round to. Use (e.g.) 0.01 to show 2 decimal places of precision. If NULL, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values.

Applied to rescaled data.
scale_cut

Scale cut function to be applied. See scales::cut_short_scale() and friends.
fontsize

Font size in points. Defaults to 7.
extra_padding

Extra padding to create space for the value label.
vjust

Vertical position adjustment of the value label.
hjust

Horizontal position adjustment of the value label.
group

Variable in the dataset to be used for grouping.

Value

A tidyplot object.

Examples

dinosaurs |>
  tidyplot(x = time_lived, color = time_lived) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_bar()

dinosaurs |>
  tidyplot(x = time_lived, color = time_lived) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_dash()

dinosaurs |>
  tidyplot(x = time_lived, color = time_lived) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_dot()

dinosaurs |>
  tidyplot(x = time_lived, color = time_lived) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_value()

dinosaurs |>
  tidyplot(x = time_lived) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_line()

dinosaurs |>
  tidyplot(x = time_lived) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_area()

# Combination
dinosaurs |>
  tidyplot(x = time_lived) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_bar(alpha = 0.4) |>
  add_count_dash() |>
  add_count_dot() |>
  add_count_value() |>
  add_count_line()

# Changing arguments: alpha
# Makes objects transparent
dinosaurs |>
  tidyplot(x = time_lived, color = time_lived) |>
  theme_minimal_y() |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_bar(alpha = 0.4)

# Changing arguments: saturation
# Reduces fill color saturation without making the object transparent
dinosaurs |>
  tidyplot(x = time_lived, color = time_lived) |>
  theme_minimal_y() |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_bar(saturation = 0.3)

# Changing arguments: accuracy
dinosaurs |>
  tidyplot(x = time_lived, color = time_lived) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_value(accuracy = 1)

# Changing arguments: fontsize
dinosaurs |>
  tidyplot(x = time_lived, color = time_lived) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_value(fontsize = 10)

# Changing arguments: color
dinosaurs |>
  tidyplot(x = time_lived, color = time_lived) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_count_value(color = "black")

Add curve fit

Description

Add curve fit

Usage

add_curve_fit(
  plot,
  dodge_width = NULL,
  method = "loess",
  linewidth = 0.25,
  alpha = 0.4,
  preserve = "total",
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
method

Smoothing method (function) to use, accepts either NULL or a character vector, e.g. "lm", "glm", "gam", "loess" or a function, e.g. MASS::rlm or mgcv::gam, stats::lm, or stats::loess. "auto" is also accepted for backwards compatibility. It is equivalent to NULL.

For method = NULL the smoothing method is chosen based on the size of the largest group (across all panels). stats::loess() is used for less than 1,000 observations; otherwise mgcv::gam() is used with formula = y ~ s(x, bs = "cs") with method = "REML". Somewhat anecdotally, loess gives a better appearance, but is O(N2)O(N^{2}) in memory, so does not work for larger datasets.

If you have fewer than 1,000 observations but want to use the same gam() model that method = NULL would use, then set ⁠method = "gam", formula = y ~ s(x, bs = "cs")⁠.
linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.
alpha

A number between 0 and 1 for the opacity of an object. A value of 0 is completely transparent, 1 is completely opaque.
preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?
...

Arguments passed on to ggplot2::geom_smooth().

Value

A tidyplot object.

Examples

time_course |>
  tidyplot(x = day, y = score, color = treatment) |>
  add_curve_fit()

# Changing arguments
time_course |>
  tidyplot(x = day, y = score, color = treatment) |>
  add_curve_fit(linewidth = 1)

time_course |>
  tidyplot(x = day, y = score, color = treatment) |>
  add_curve_fit(alpha = 0.8)

# Remove confidence interval
time_course |>
  tidyplot(x = day, y = score, color = treatment) |>
  add_curve_fit(se = FALSE)

Add data labels

Description

Add data labels

Usage

add_data_labels(
  plot,
  label,
  data = all_rows(),
  fontsize = 7,
  dodge_width = NULL,
  jitter_width = 0,
  jitter_height = 0,
  preserve = "total",
  background = FALSE,
  background_color = NULL,
  background_alpha = 0.6,
  label_position = c("below", "above", "left", "right", "center"),
  seed = 42,
  ...
)

add_data_labels_repel(
  plot,
  label,
  data = all_rows(),
  fontsize = 7,
  dodge_width = NULL,
  jitter_width = 0,
  jitter_height = 0,
  preserve = "total",
  segment.size = 0.2,
  box.padding = 0.2,
  max.overlaps = Inf,
  background = FALSE,
  background_color = NULL,
  background_alpha = 0.6,
  seed = 42,
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
label

Variable in the dataset to be used for the text label.
data

The data to be displayed in this layer. There are three options:

  • If all_rows() (the default) the complete dataset is displayed.

  • A function to subset the plot data. See filter_rows() and friends.

  • A data.frame to override the plot data.

fontsize

Font size in points. Defaults to 7.
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
jitter_width

Amount of random noise to be added to the horizontal position of the of the data points. This can be useful to deal with overplotting. Typical values range between 0 and 1.
jitter_height

Amount of random noise to be added to the vertical position of the of the data points. This can be useful to deal with overplotting. Typical values range between 0 and 1.
preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?
background

Whether to include semitransparent background box behind the labels to improve legibility. Defaults to FALSE.
background_color

Hex color of the background box. The default (NULL) retrieves the setting from the paper color of the plot, which defaults to "#FFFFFF".
background_alpha

Opacity of the background box. Defaults to 0.6.
label_position

Position of the label in relation to the data point. Can be one of c("below", "above", "left", "right", "center").
seed

Random seed controlling point and label jitter.
...

Arguments passed on to the geom function.
segment.size

Thickness of the line connecting the label with the data point. Defaults to 0.2.
box.padding

Amount of padding around bounding box, as unit or number. Defaults to 0.25. (Default unit is lines, but other units can be specified by passing unit(x, "units")).
max.overlaps

Exclude text labels when they overlap too many other things. For each text label, we count how many other text labels or other data points it overlaps, and exclude the text label if it has too many overlaps. Defaults to 10.

Details

  • add_data_labels_repel() uses ggrepel::geom_text_repel(). Check there and in ggrepel examples for additional arguments.

  • add_data_labels() and add_data_labels_repel() support data subsetting. See Advanced plotting.

Value

A tidyplot object.

Examples

# Create plot and increase padding to make more space for labels
p <-
  animals |>
  dplyr::slice_head(n = 5) |>
  tidyplot(x = weight, y = speed) |>
  theme_ggplot2() |>
  add_data_points() |>
  adjust_padding(all = 0.3)

# Default label position is `below` the data point
p |> add_data_labels(label = animal)

# Alternative label positions
p |> add_data_labels(label = animal, label_position = "above")

p |> add_data_labels(label = animal, label_position = "right")

p |> add_data_labels(label = animal, label_position = "left")

# Include white background box
p |> add_data_labels(label = animal, background = TRUE)

p |> add_data_labels(label = animal, background = TRUE,
  background_color = "pink")

# Black labels
p |> add_data_labels(label = animal, color = "black")

# Use repelling data labels
p |> add_data_labels_repel(label = animal, color = "black")

p |> add_data_labels_repel(label = animal, color = "black",
  background = TRUE)

p |> add_data_labels_repel(label = animal, color = "black",
  background = TRUE, min.segment.length = 0)

Add data points

Description

Add data points

Usage

add_data_points(
  plot,
  data = all_rows(),
  shape = 19,
  size = 1,
  white_border = FALSE,
  dodge_width = NULL,
  preserve = "total",
  rasterize = FALSE,
  rasterize_dpi = 300,
  ...
)

add_data_points_jitter(
  plot,
  data = all_rows(),
  shape = 19,
  size = 1,
  white_border = FALSE,
  dodge_width = NULL,
  jitter_width = 0.2,
  jitter_height = 0,
  preserve = "total",
  rasterize = FALSE,
  rasterize_dpi = 300,
  seed = 42,
  ...
)

add_data_points_beeswarm(
  plot,
  data = all_rows(),
  shape = 19,
  size = 1,
  white_border = FALSE,
  cex = 3,
  corral = "wrap",
  corral.width = 0.5,
  dodge_width = NULL,
  preserve = "total",
  rasterize = FALSE,
  rasterize_dpi = 300,
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
data

The data to be displayed in this layer. There are three options:

  • If all_rows() (the default) the complete dataset is displayed.

  • A function to subset the plot data. See filter_rows() and friends.

  • A data.frame to override the plot data.

shape

An integer between 0 and 24, representing the shape of the plot symbol.

unnamed-chunk-1-1.png
size

A number representing the size of the plot symbol. Typical values range between 1 and 3.
white_border

Whether to include a white border around data points. Defaults to FALSE.
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?
rasterize

If FALSE (the default) the layer will be constructed of vector shapes. If TRUE the layer will be rasterized to a pixel image. This can be useful when plotting many individual objects (1,000 or more) compromises the performance of the generated PDF file.
rasterize_dpi

The resolution in dots per inch (dpi) used for rastering the layer if rasterize is TRUE. The default is 300 dpi.
...

Arguments passed on to the geom function.
jitter_width

Amount of random noise to be added to the horizontal position of the of the data points. This can be useful to deal with overplotting. Typical values range between 0 and 1.
jitter_height

Amount of random noise to be added to the vertical position of the of the data points. This can be useful to deal with overplotting. Typical values range between 0 and 1.
seed

Random seed controlling point and label jitter.
cex

Scaling for adjusting point spacing (see beeswarm::swarmx()). Values between 1 (default) and 3 tend to work best.
corral

Method used to adjust points that would be placed too wide horizontally. Options are "none" (default), "gutter", "wrap", "random", and "omit". See Details below.
corral.width

Width of the corral, if not "none". Default is 0.9.

Details

  • add_data_points_beeswarm() is based on ggbeeswarm::geom_beeswarm(). Check there for additional arguments.

  • add_data_points() and friends support rasterization. See examples and Advanced plotting.

  • add_data_points() and friends support data subsetting. See examples and Advanced plotting.

Value

A tidyplot object.

Examples

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_jitter()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm()

# Changing arguments
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_jitter(jitter_width = 1)

animals |>
  tidyplot(x = weight, y = size) |>
  add_data_points(white_border = TRUE)

animals |>
  tidyplot(x = weight, y = size) |>
  add_data_points(alpha = 0.4)

# Rasterization
animals |>
  tidyplot(x = weight, y = size) |>
  add_data_points(rasterize = TRUE, rasterize_dpi = 50)

# Data subsetting
animals |>
  tidyplot(x = weight, y = size) |>
  add_data_points() |>
  add_data_points(data = filter_rows(size > 300), color = "red")

Add ellipse

Description

Add ellipse

Usage

add_ellipse(plot, ...)

Arguments

plot

A tidyplot generated with the function tidyplot().
...

Arguments passed on to ggplot2::stat_ellipse().

Value

A tidyplot object.

Examples

pca |>
  tidyplot(x = pc1, y = pc2, color = group) |>
  add_data_points() |>
  add_ellipse()

pca |>
  tidyplot(x = pc1, y = pc2, color = group) |>
  add_data_points() |>
  add_ellipse(level = 0.75)

pca |>
  tidyplot(x = pc1, y = pc2, color = group) |>
  add_data_points() |>
  add_ellipse(type = "norm")

Add heatmap

Description

Add heatmap

Usage

add_heatmap(
  plot,
  scale = c("none", "row", "column"),
  rotate_labels = 90,
  rasterize = FALSE,
  rasterize_dpi = 300,
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
scale

Whether to compute row z scores for "row" or "column". Defaults to "none".
rotate_labels

Degree to rotate the x-axis labels. Defaults to 90.
rasterize

If FALSE (the default) the layer will be constructed of vector shapes. If TRUE the layer will be rasterized to a pixel image. This can be useful when plotting many individual objects (1,000 or more) compromises the performance of the generated PDF file.
rasterize_dpi

The resolution in dots per inch (dpi) used for rastering the layer if rasterize is TRUE. The default is 300 dpi.
...

Arguments passed on to the geom function.

Details

Value

A tidyplot object.

Examples

climate |>
  tidyplot(x = month, y = year, color = max_temperature) |>
  add_heatmap()

# Calculate row-wise z score
climate |>
  tidyplot(x = month, y = year, color = max_temperature) |>
  add_heatmap(scale = "row")

# Calculate column-wise z score
climate |>
  tidyplot(x = month, y = year, color = max_temperature) |>
  add_heatmap(scale = "column")

# Rasterize heatmap
climate |>
  tidyplot(x = month, y = year, color = max_temperature) |>
  add_heatmap(rasterize = TRUE, rasterize_dpi = 20)

Add histogram

Description

Add histogram

Usage

add_histogram(plot, binwidth = NULL, bins = NULL, ...)

Arguments

plot

A tidyplot generated with the function tidyplot().
binwidth

The width of the bins. Can be specified as a numeric value or as a function that takes x after scale transformation as input and returns a single numeric value. When specifying a function along with a grouping structure, the function will be called once per group. The default is to use the number of bins in bins, covering the range of the data. You should always override this value, exploring multiple widths to find the best to illustrate the stories in your data.

The bin width of a date variable is the number of days in each time; the bin width of a time variable is the number of seconds.
bins

Number of bins. Overridden by binwidth. Defaults to 30.
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

energy |>
  tidyplot(x = energy) |>
  add_histogram()

energy |>
  tidyplot(x = energy, color = energy_type) |>
  add_histogram()

Add line or area

Description

add_line() and add_area() connect individual data points, which is rarely needed. In most cases, you are probably looking for add_sum_line(), add_mean_line(), add_sum_area() or add_mean_area().

Usage

add_line(
  plot,
  group,
  dodge_width = NULL,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_area(
  plot,
  group,
  dodge_width = NULL,
  linewidth = 0.25,
  alpha = 0.4,
  preserve = "total",
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
group

Variable in the dataset to be used for grouping.
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.
preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?
...

Arguments passed on to the geom function.
alpha

A number between 0 and 1 for the opacity of an object. A value of 0 is completely transparent, 1 is completely opaque.

Value

A tidyplot object.

Examples

# Paired data points
study |>
  tidyplot(x = treatment, y = score, color = group) |>
  reorder_x_axis_labels("A", "C", "B", "D") |>
  add_data_points() |>
  add_line(group = participant, color = "grey")

study |>
  tidyplot(x = treatment, y = score) |>
  reorder_x_axis_labels("A", "C", "B", "D") |>
  add_data_points() |>
  add_area(group = participant)

Add mean

Description

Add mean

Usage

add_mean_bar(
  plot,
  dodge_width = NULL,
  width = 0.6,
  saturation = 1,
  preserve = "total",
  ...
)

add_mean_dash(
  plot,
  dodge_width = NULL,
  width = 0.6,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_mean_dot(plot, dodge_width = NULL, size = 2, preserve = "total", ...)

add_mean_value(
  plot,
  dodge_width = NULL,
  accuracy = 0.1,
  scale_cut = NULL,
  fontsize = 7,
  extra_padding = 0.15,
  vjust = NULL,
  hjust = NULL,
  preserve = "total",
  ...
)

add_mean_line(
  plot,
  group,
  dodge_width = NULL,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_mean_area(
  plot,
  group,
  dodge_width = NULL,
  linewidth = 0.25,
  preserve = "total",
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
width

Horizontal width of the plotted object (bar, error bar, boxplot, violin plot, etc). Typical values range between 0 and 1.
saturation

A number between 0 and 1 for the color saturation of an object. A value of 0 is completely desaturated (white), 1 is the original color.
preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?
...

Arguments passed on to the geom function.
linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.
size

A number representing the size of the plot symbol. Typical values range between 1 and 3.
accuracy

A number to round to. Use (e.g.) 0.01 to show 2 decimal places of precision. If NULL, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values.

Applied to rescaled data.
scale_cut

Scale cut function to be applied. See scales::cut_short_scale() and friends.
fontsize

Font size in points. Defaults to 7.
extra_padding

Extra padding to create space for the value label.
vjust

Vertical position adjustment of the value label.
hjust

Horizontal position adjustment of the value label.
group

Variable in the dataset to be used for grouping.

Value

A tidyplot object.

Examples

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_dash()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_dot()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_value()

study |>
  tidyplot(x = treatment, y = score) |>
  add_mean_line()

study |>
  tidyplot(x = treatment, y = score) |>
  add_mean_area()

# Combination
study |>
  tidyplot(x = treatment, y = score) |>
  add_mean_bar(alpha = 0.4) |>
  add_mean_dash() |>
  add_mean_dot() |>
  add_mean_value() |>
  add_mean_line()

# Changing arguments: alpha
# Makes objects transparent
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  theme_minimal_y() |>
  add_mean_bar(alpha = 0.4)

# Changing arguments: saturation
# Reduces fill color saturation without making the object transparent
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  theme_minimal_y() |>
  add_mean_bar(saturation = 0.3)

# Changing arguments: accuracy
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_value(accuracy = 0.01)

# Changing arguments: fontsize
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_value(fontsize = 10)

# Changing arguments: color
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_value(color = "black")

Add median

Description

Add median

Usage

add_median_bar(
  plot,
  dodge_width = NULL,
  width = 0.6,
  saturation = 1,
  preserve = "total",
  ...
)

add_median_dash(
  plot,
  dodge_width = NULL,
  width = 0.6,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_median_dot(plot, dodge_width = NULL, size = 2, preserve = "total", ...)

add_median_value(
  plot,
  dodge_width = NULL,
  accuracy = 0.1,
  scale_cut = NULL,
  fontsize = 7,
  extra_padding = 0.15,
  vjust = NULL,
  hjust = NULL,
  preserve = "total",
  ...
)

add_median_line(
  plot,
  group,
  dodge_width = NULL,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_median_area(
  plot,
  group,
  dodge_width = NULL,
  linewidth = 0.25,
  preserve = "total",
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
width

Horizontal width of the plotted object (bar, error bar, boxplot, violin plot, etc). Typical values range between 0 and 1.
saturation

A number between 0 and 1 for the color saturation of an object. A value of 0 is completely desaturated (white), 1 is the original color.
preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?
...

Arguments passed on to the geom function.
linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.
size

A number representing the size of the plot symbol. Typical values range between 1 and 3.
accuracy

A number to round to. Use (e.g.) 0.01 to show 2 decimal places of precision. If NULL, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values.

Applied to rescaled data.
scale_cut

Scale cut function to be applied. See scales::cut_short_scale() and friends.
fontsize

Font size in points. Defaults to 7.
extra_padding

Extra padding to create space for the value label.
vjust

Vertical position adjustment of the value label.
hjust

Horizontal position adjustment of the value label.
group

Variable in the dataset to be used for grouping.

Value

A tidyplot object.

Examples

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_median_bar()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_median_dash()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_median_dot()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_median_value()

study |>
  tidyplot(x = treatment, y = score) |>
  add_median_line()

study |>
  tidyplot(x = treatment, y = score) |>
  add_median_area()

# Combination
study |>
  tidyplot(x = treatment, y = score) |>
  add_median_bar(alpha = 0.4) |>
  add_median_dash() |>
  add_median_dot() |>
  add_median_value() |>
  add_median_line()

# Changing arguments: alpha
# Makes objects transparent
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  theme_minimal_y() |>
  add_median_bar(alpha = 0.4)

# Changing arguments: saturation
# Reduces fill color saturation without making the object transparent
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  theme_minimal_y() |>
  add_median_bar(saturation = 0.3)

# Changing arguments: accuracy
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_median_value(accuracy = 0.01)

# Changing arguments: fontsize
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_median_value(fontsize = 10)

# Changing arguments: color
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_median_value(color = "black")

Add pie or donut chart

Description

Add pie or donut chart

Usage

add_pie(plot, width = 1, reverse = FALSE, ...)

add_donut(plot, width = 1, reverse = FALSE, ...)

Arguments

plot

A tidyplot generated with the function tidyplot().
width

Width of the donut ring.
reverse

Whether the order should be reversed or not. Defaults to FALSE, meaning not reversed.
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

# for a `count` only provide `color`
# `count` of the data points in each `energy_type` category
energy |>
  tidyplot(color = energy_type) |>
  add_pie()

energy |>
  tidyplot(color = energy_type) |>
  add_donut()

energy |>
  tidyplot(color = energy_type) |>
  add_donut(width = 0.5)

# for a `sum` provide `color` and `y`
# `sum` of `energy` in each `energy_type` category
energy |>
  tidyplot(y = energy, color = energy_type) |>
  add_pie()

energy |>
  tidyplot(y = energy, color = energy_type) |>
  add_donut()

energy |>
  tidyplot(y = energy, color = energy_type) |>
  add_donut(width = 0.5)

Add reference lines

Description

Add reference lines

Usage

add_reference_lines(
  plot,
  x = NULL,
  y = NULL,
  linetype = "dashed",
  linewidth = 0.25,
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
x

Numeric values where the reference lines should meet the x-axis. For example, x = 4 or x = c(2,3,4).
y

Numeric values where the reference lines should meet the y-axis. For example, y = 4 or y = c(2,3,4).
linetype

Either an integer (0-6) or a name (0 = blank, 1 = solid, 2 = dashed, 3 = dotted, 4 = dotdash, 5 = longdash, 6 = twodash).
linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

animals |>
  tidyplot(x = weight, y = speed) |>
   add_reference_lines(x = 4000, y = c(100, 200)) |>
   add_data_points()

animals |>
  tidyplot(x = weight, y = speed) |>
   add_reference_lines(x = 4000, y = c(100, 200), linetype = "dotdash") |>
   add_data_points()

Add error bar

Description

  • add_sem_errorbar() adds the standard error of mean.

  • add_range_errorbar() adds the range from smallest to largest value.

  • add_sd_errorbar() adds the standard deviation.

  • add_ci95_errorbar() adds the 95% confidence interval.

Usage

add_sem_errorbar(
  plot,
  dodge_width = NULL,
  width = 0.4,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_range_errorbar(
  plot,
  dodge_width = NULL,
  width = 0.4,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_sd_errorbar(
  plot,
  dodge_width = NULL,
  width = 0.4,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_ci95_errorbar(
  plot,
  dodge_width = NULL,
  width = 0.4,
  linewidth = 0.25,
  preserve = "total",
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
width

Width of the error bar.
linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.
preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

# Standard error of the mean
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_sem_errorbar()

# Range from minimum to maximum value
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_range_errorbar()

# Standard deviation
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_sd_errorbar()

# 95% confidence interval
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_ci95_errorbar()

# Changing arguments: error bar width
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_sem_errorbar(width = 0.8)

# Changing arguments: error bar line width
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_sem_errorbar(linewidth = 1)

Add ribbon

Description

  • add_sem_ribbon() adds the standard error of mean.

  • add_range_ribbon() adds the range from smallest to largest value.

  • add_sd_ribbon() adds the standard deviation.

  • add_ci95_ribbon() adds the 95% confidence interval.

Usage

add_sem_ribbon(plot, dodge_width = NULL, alpha = 0.4, color = NA, ...)

add_range_ribbon(plot, dodge_width = NULL, alpha = 0.4, color = NA, ...)

add_sd_ribbon(plot, dodge_width = NULL, alpha = 0.4, color = NA, ...)

add_ci95_ribbon(plot, dodge_width = NULL, alpha = 0.4, color = NA, ...)

Arguments

plot

A tidyplot generated with the function tidyplot().
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
alpha

A number between 0 and 1 for the opacity of an object. A value of 0 is completely transparent, 1 is completely opaque.
color

A hex color for the stroke color. For example, "#FFFFFF" for white.
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

# Standard error of the mean
time_course |>
  tidyplot(x = day, y = score, color = treatment) |>
  add_mean_line() |>
  add_sem_ribbon()

# Range from minimum to maximum value
time_course |>
  tidyplot(x = day, y = score, color = treatment) |>
  add_mean_line() |>
  add_range_ribbon()

# Standard deviation
time_course |>
  tidyplot(x = day, y = score, color = treatment) |>
  add_mean_line() |>
  add_sd_ribbon()

# 95% confidence interval
time_course |>
  tidyplot(x = day, y = score, color = treatment) |>
  add_mean_line() |>
  add_ci95_ribbon()

# Changing arguments: alpha
time_course |>
  tidyplot(x = day, y = score, color = treatment) |>
  add_mean_line() |>
  add_sem_ribbon(alpha = 0.7)

Add sum

Description

Add sum

Usage

add_sum_bar(
  plot,
  dodge_width = NULL,
  width = 0.6,
  saturation = 1,
  preserve = "total",
  ...
)

add_sum_dash(
  plot,
  dodge_width = NULL,
  width = 0.6,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_sum_dot(plot, dodge_width = NULL, size = 2, preserve = "total", ...)

add_sum_value(
  plot,
  dodge_width = NULL,
  accuracy = 0.1,
  scale_cut = NULL,
  fontsize = 7,
  extra_padding = 0.15,
  vjust = NULL,
  hjust = NULL,
  preserve = "total",
  ...
)

add_sum_line(
  plot,
  group,
  dodge_width = NULL,
  linewidth = 0.25,
  preserve = "total",
  ...
)

add_sum_area(
  plot,
  group,
  dodge_width = NULL,
  linewidth = 0.25,
  preserve = "total",
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
width

Horizontal width of the plotted object (bar, error bar, boxplot, violin plot, etc). Typical values range between 0 and 1.
saturation

A number between 0 and 1 for the color saturation of an object. A value of 0 is completely desaturated (white), 1 is the original color.
preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?
...

Arguments passed on to the geom function.
linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.
size

A number representing the size of the plot symbol. Typical values range between 1 and 3.
accuracy

A number to round to. Use (e.g.) 0.01 to show 2 decimal places of precision. If NULL, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values.

Applied to rescaled data.
scale_cut

Scale cut function to be applied. See scales::cut_short_scale() and friends.
fontsize

Font size in points. Defaults to 7.
extra_padding

Extra padding to create space for the value label.
vjust

Vertical position adjustment of the value label.
hjust

Horizontal position adjustment of the value label.
group

Variable in the dataset to be used for grouping.

Value

A tidyplot object.

Examples

spendings |>
  tidyplot(x = category, y = amount, color = category) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_bar()

spendings |>
  tidyplot(x = category, y = amount, color = category) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_dash()

spendings |>
  tidyplot(x = category, y = amount, color = category) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_dot()

spendings |>
  tidyplot(x = category, y = amount, color = category) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_value()

spendings |>
  tidyplot(x = category, y = amount) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_line()

spendings |>
  tidyplot(x = category, y = amount) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_area()

# Combination
spendings |>
  tidyplot(x = category, y = amount) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_median_bar(alpha = 0.4) |>
  add_median_dash() |>
  add_median_dot() |>
  add_median_value() |>
  add_median_line()

# Changing arguments: alpha
# Makes objects transparent
spendings |>
  tidyplot(x = category, y = amount, color = category) |>
  theme_minimal_y() |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_bar(alpha = 0.4)

# Changing arguments: saturation
# Reduces fill color saturation without making the object transparent
spendings |>
  tidyplot(x = category, y = amount, color = category) |>
  theme_minimal_y() |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_bar(saturation = 0.3)

# Changing arguments: accuracy
spendings |>
  tidyplot(x = category, y = amount, color = category) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_value(accuracy = 1)

# Changing arguments: fontsize
spendings |>
  tidyplot(x = category, y = amount, color = category) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_value(fontsize = 10)

# Changing arguments: color
spendings |>
  tidyplot(x = category, y = amount, color = category) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_value(color = "black")

# Changing arguments: extra_padding
spendings |>
  tidyplot(x = category, y = amount, color = category) |>
  adjust_x_axis(rotate_labels = TRUE) |>
  add_sum_value(extra_padding = 0.5)

Add statistical test

Description

Add statistical test

Usage

add_test_pvalue(
  plot,
  padding_top = 0.15,
  method = "t_test",
  p.adjust.method = "none",
  ref.group = NULL,
  comparisons = NULL,
  paired_by = NULL,
  label = "{tidyplots::format_p_value(p.adj, 0.0001)}",
  label.size = 7/ggplot2::.pt,
  step.increase = 0.15,
  vjust = -0.25,
  bracket.nudge.y = 0.1,
  hide.ns = FALSE,
  p.adjust.by = "panel",
  symnum.args = list(cutpoints = c(0, 0.001, 0.01, 0.05, Inf), symbols = c("***", "**",
    "*", "ns")),
  color = plot$tidyplot$ink,
  hide_info = FALSE,
  ...
)

add_test_asterisks(
  plot,
  padding_top = 0.1,
  method = "t_test",
  p.adjust.method = "none",
  ref.group = NULL,
  comparisons = NULL,
  paired_by = NULL,
  label = "p.adj.signif",
  label.size = 10/ggplot2::.pt,
  step.increase = 0.2,
  vjust = 0.3,
  bracket.nudge.y = 0.15,
  hide.ns = TRUE,
  p.adjust.by = "panel",
  symnum.args = list(cutpoints = c(0, 0.001, 0.01, 0.05, Inf), symbols = c("***", "**",
    "*", "ns")),
  color = plot$tidyplot$ink,
  hide_info = FALSE,
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
padding_top

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

a character string indicating which method to be used for pairwise comparisons. Default is "t_test". Allowed methods include pairwise comparisons methods implemented in the rstatix R package. These methods are: "wilcox_test", "t_test", "sign_test", "dunn_test", "emmeans_test", "tukey_hsd", "games_howell_test".
p.adjust.method

method for adjusting p values (see p.adjust). Has impact only in a situation, where multiple pairwise tests are performed; or when there are multiple grouping variables. Ignored when the specified method is "tukey_hsd" or "games_howell_test" because they come with internal p adjustment method. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none".
ref.group

a character string or a numeric value specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group).

ref.group can be also "all". In this case, each of the grouping variable levels is compared to all (i.e. basemean).

Allowed values can be:

  • numeric value: specifying the rank of the reference group. For example, use ref.group = 1 when the first group is the reference; use ref.group = 2 when the second group is the reference, and so on. This works for all situations, including i) when comparisons are performed between x-axis groups and ii) when comparisons are performed between legend groups.

  • character value: For example, you can use ref.group = "ctrl" instead of using the numeric rank value of the "ctrl" group.

  • "all": In this case, each of the grouping variable levels is compared to all (i.e. basemean).

comparisons

A list of length-2 vectors. The entries in the vector are 2 integers that correspond to the index of the groups of interest, to be compared.
paired_by

Variable to be used for paired analysis.
label

character string specifying label. Can be:

  • the column containing the label (e.g.: label = "p" or label = "p.adj"), where p is the p-value. Other possible values are "p.signif", "p.adj.signif", "p.format", "p.adj.format".

  • an expression that can be formatted by the glue() package. For example, when specifying label = "Wilcoxon, p = \{p\}", the expression {p} will be replaced by its value.

  • a combination of plotmath expressions and glue expressions. You may want some of the statistical parameter in italic; for example:label = "Wilcoxon, italic(p)= {p}"

.
label.size

change the size of the label 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.
bracket.nudge.y

Vertical adjustment to nudge brackets by (in fraction of the total height). Useful to move up or move down the bracket. If positive value, brackets will be moved up; if negative value, brackets are moved down.
hide.ns

can be logical value (TRUE or FALSE) or a character vector ("p.adj" or "p").
p.adjust.by

possible value is one of c("group", "panel"). Default is "group": for a grouped data, if pairwise test is performed, then the p-values are adjusted for each group level independently. P-values are adjusted by panel when p.adjust.by = "panel".
symnum.args

a list of arguments to pass to the function symnum for symbolic number coding of p-values. For example, symnum.args = list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns")).

In other words, we use the following convention for symbols indicating statistical significance:

  • ns: p > 0.05

  • *: p <= 0.05

  • **: p <= 0.01

  • ***: p <= 0.001

  • ****: p <= 0.0001

color

A hex color for the stroke color. For example, "#FFFFFF" for white.
hide_info

Whether to hide details about the statistical testing as caption. Defaults to FALSE.
...

Arguments passed on to ggpubr::geom_pwc().

Details

  • add_test_pvalue() and add_test_asterisks() use ggpubr::geom_pwc(). Check there for additional arguments.

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

Value

A tidyplot object.

Examples

# Add p value
study |>
  tidyplot(x = dose, y = score, color = group) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points() |>
  add_test_pvalue()

# Add asterisks
study |>
  tidyplot(x = dose, y = score, color = group) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points() |>
  add_test_asterisks()

# Change stat method
study |>
  tidyplot(x = dose, y = score, color = group) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points() |>
  add_test_pvalue(method = "wilcoxon")

# Change p.adjust method
study |>
  tidyplot(x = dose, y = score, color = group) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points() |>
  add_test_pvalue(p.adjust.method = "bonferroni")

# Define reference group to test against
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points() |>
  add_test_asterisks(ref.group = 1)

# Define selected comparisons
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points() |>
  add_test_pvalue(comparisons = list(c(1,3),c(2,4)))

# Paired analysis
x <- c(2.3, 4.5, 6.3, 3.4, 7.8, 6.7)
df <- data.frame(
  x = c(x, x + c(0.8, 0.75)),
  group = paste0("g", rep(c(1, 2), each = 6)),
  batch = paste0("b", c(1:6, 1:6)),
  shuffle = paste0("c", c(1:6, 6:1))
)

df |>
  tidyplot(group, x, color = group) |>
  add_boxplot() |>
  add_data_points() |>
  add_test_pvalue(paired_by = shuffle) |>
  add_line(group = shuffle, color = "black")

df |>
  tidyplot(group, x, color = group) |>
  add_boxplot() |>
  add_data_points() |>
  add_test_pvalue(paired_by = batch) |>
  add_line(group = batch, color = "black")

# hide non-significant p values
gene_expression |>
  # filter to one gene
  dplyr::filter(external_gene_name == "Apol6") |>
  # start plotting
  tidyplot(x = condition, y = expression, color = sample_type) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points() |>
  add_test_pvalue(hide.ns = TRUE)

# Adjust top padding for statistical comparisons
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points() |>
  add_test_pvalue(padding_top = 0.08)

# Hide stats information
study |>
  tidyplot(x = dose, y = score, color = group) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points() |>
  add_test_pvalue(hide_info = TRUE)

Add pre-computed statistics

Description

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().

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().

Value

A tidyplot object.

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)

Add plot title or caption

Description

Add plot title or caption

Usage

add_title(plot, title = ggplot2::waiver())

add_caption(plot, caption = ggplot2::waiver())

Arguments

plot

A tidyplot generated with the function tidyplot().
title

Title of the plot.
caption

Caption of the plot.

Details

Value

A tidyplot object.

Examples

study |>
  tidyplot(x = treatment, y = score) |>
  add_data_points_beeswarm() |>
  add_title("This is my title")

study |>
  tidyplot(x = treatment, y = score) |>
  add_data_points_beeswarm() |>
  add_caption("This is the fine print in the caption")

# Plotmath expression
study |>
  tidyplot(x = treatment, y = score) |>
  add_data_points_beeswarm() |>
  add_title("$H[2]*O~and~E==m*c^{2}$")

Add violin plot

Description

Add violin plot

Usage

add_violin(
  plot,
  dodge_width = NULL,
  alpha = 0.3,
  saturation = 1,
  trim = TRUE,
  linewidth = 0.25,
  scale = "width",
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.
alpha

A number between 0 and 1 for the opacity of an object. A value of 0 is completely transparent, 1 is completely opaque.
saturation

A number between 0 and 1 for the color saturation of an object. A value of 0 is completely desaturated (white), 1 is the original color.
trim

If TRUE (default), trim the tails of the violins to the range of the data. If FALSE, don't trim the tails.
linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.
scale

if "area" (default), all violins have the same area (before trimming the tails). If "count", areas are scaled proportionally to the number of observations. If "width", all violins have the same maximum width.
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_violin()

# Changing arguments:
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_violin(saturation = 0.6)

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_violin(draw_quantiles = c(0.25, 0.5, 0.75))

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_violin(trim = FALSE)

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_violin(linewidth = 1)

Adjust colors

Description

Adjust colors

Usage

adjust_colors(
  plot,
  new_colors = NULL,
  saturation = 1,
  labels = tidyplot_parse_labels(),
  downsample = c("evenly", "first", "last", "middle"),
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
new_colors

A character vector of new hex colors to use. Can be a named character vector of hex colors to assign certain data labels to specific colors.
saturation

A number between 0 and 1 for the color saturation of an object. A value of 0 is completely desaturated (white), 1 is the original color.
labels

One of the options below. Please note that when labels is a vector, it is highly recommended to also set the breaks argument as a vector to protect against unintended mismatches.

  • NULL for no labels

  • waiver() for the default labels computed by the transformation object

  • A character vector giving labels (must be same length as breaks)

  • An expression vector (must be the same length as breaks). See ?plotmath for details.

  • A function that takes the breaks as input and returns labels as output. Also accepts rlang lambda function notation.

downsample

If too many colors are provided, whether to downsample evenly, or use the first, the last or the middle colors of the color vector. Defaults to evenly.
...

Arguments passed on to the ggplot2 scale function.

Value

A tidyplot object.

See Also

colors_discrete_friendly(), colors_continuous_viridis(), colors_diverging_blue2brown(), and new_color_scheme()

Examples

# Plot without adjustments
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Provide hex colors
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_colors(new_colors = c("#644296","#F08533","#3B78B0", "#D1352C"))

# Provide discrete color scheme
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_colors(new_colors = colors_discrete_seaside)

# Provide named vector
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_colors(new_colors = c(
    "A" = "pink",
    "B" = "purple",
    "C" = "grey",
    "D" = "blue"))

# Provide continuous color scheme
climate |>
  tidyplot(x = month, y = year, color = max_temperature) |>
  add_heatmap() |>
  adjust_colors(new_colors = colors_continuous_turbo)

Adjust font

Description

Adjust font

Usage

adjust_font(plot, fontsize = 7, family = NULL, face = NULL)

Arguments

plot

A tidyplot generated with the function tidyplot().
fontsize

Font size in points. Defaults to 7.
family

The typeface to use. The validity of this value will depend on the graphics device being used for rendering the plot. See the systemfonts vignette for guidance on the best way to access fonts installed on your computer. The values "sans", "serif", and "mono" should always be valid and will select the default typeface for the respective styles. However, what is considered default is dependant on the graphics device and the operating system.
face

Font face ("plain", "italic", "bold", "bold.italic")

Value

A tidyplot object.

Examples

# Plot without adjustments
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Increase font size
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_font(fontsize = 16)

# Change font family
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_font(family = "mono")

# Change font face
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_font(face = "bold")

Adjust legend

Description

Adjust legend

Usage

adjust_legend_title(
  plot,
  title = ggplot2::waiver(),
  fontsize = NULL,
  family = NULL,
  face = NULL,
  color = NULL,
  ...
)

adjust_legend_position(plot, position = "right")

Arguments

plot

A tidyplot generated with the function tidyplot().
title

Legend title.
fontsize

Font size in points. Defaults to 7.
family

The typeface to use. The validity of this value will depend on the graphics device being used for rendering the plot. See the systemfonts vignette for guidance on the best way to access fonts installed on your computer. The values "sans", "serif", and "mono" should always be valid and will select the default typeface for the respective styles. However, what is considered default is dependant on the graphics device and the operating system.
face

Font face ("plain", "italic", "bold", "bold.italic")
color

A hex color for the stroke color. For example, "#FFFFFF" for white.
...

Arguments passed on to ggplot2::element_text().
position

The position of the legend. Can be one of c("right", "left", "bottom", "top", "none"). Defaults to "right".

Details

Value

A tidyplot object.

Examples

# Plot without adjustments
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# New title
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_legend_title("My new legend title")

# New title with plotmath expression
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_legend_title("$E==m*c^{2}$")

# Alternative legend positions
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_legend_position("left")

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_legend_position("top")

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_legend_position("bottom")

# `position = "none"` hides the legend
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_legend_position("none")

Adjust plot area padding

Description

Adjust plot area padding

Usage

adjust_padding(
  plot,
  top = NA,
  right = NA,
  bottom = NA,
  left = NA,
  all = NA,
  force_continuous = FALSE,
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
top

Extra space between the data points and the top. Defaults to NA, which does not change the padding.
right

Extra space between the data points and the right. Defaults to NA, which does not change the padding.
bottom

Extra space between the data points and the bottom. Defaults to NA, which does not change the padding.
left

Extra space between the data points and the left. Defaults to NA, which does not change the padding.
all

Extra space around the data pointst. Overwrites top, right, bottom, left if set. Defaults to NA, which does not change the padding.
force_continuous

Whether to force the axis to be continuous. Defaults to FALSE.
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

# Plot without adjustments
animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_padding()

# Increase plot area padding
animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_padding(all = 0.2)

animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_padding(top = 0.8)

animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_padding(bottom = 0.8)

animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_padding(right = 0.8)

animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_padding(left = 0.8)

Adjust plot area size

Description

Adjust plot area size

Usage

adjust_size(
  plot,
  width = NULL,
  height = NULL,
  unit = NULL,
  overall_width = NULL,
  overall_height = NULL
)

Arguments

plot

A tidyplot generated with the function tidyplot().
width, height

Dimensions of the plot area. The default (NULL) retrieves the setting from the tidyplots options, which defaults to 50. NA results in taking all available space (ggplot2 default).
unit

Unit of the plot area width and height. The default (NULL) retrieves the setting from the tidyplots options, which defaults to "mm".
overall_width, overall_height

The overall dimensions of a multiplot layout generated with split_plot().

Value

A tidyplot object.

Examples

# Plot without adjustments
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm(shape = 1) |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Resize to 15 x 15 mm
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm(shape = 1) |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_size(width = 15, height = 15)

# Resize to 4 x 4 cm
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm(shape = 1) |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_size(width = 4, height = 4, unit = "cm")

# Remove absolute dimensions and take all available space.
# This is the ggplot2 default.
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm(shape = 1) |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_size(width = NA, height = NA)

Adjust theme details

Description

This function is a wrapper around ggplot2::theme(). To use the required theme helper functions ggplot2::element_blank(), ggplot2::element_rect(), ggplot2::element_line(), and ggplot2::element_text() you need to either load the ggplot2 package via library(ggplot2) or use the ⁠ggplot2::⁠ prefix as shown above.

Usage

adjust_theme_details(plot, ...)

Arguments

plot

A tidyplot generated with the function tidyplot().
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm() |>
  add_mean_bar(alpha = 0.4) |>
  adjust_theme_details(plot.background = ggplot2::element_rect(fill = "#FFEBFF"))

Adjust titles and caption

Description

Adjust titles and caption

Usage

adjust_title(
  plot,
  title = ggplot2::waiver(),
  fontsize = NULL,
  family = NULL,
  face = NULL,
  color = NULL,
  ...
)

adjust_x_axis_title(
  plot,
  title = ggplot2::waiver(),
  fontsize = NULL,
  family = NULL,
  face = NULL,
  color = NULL,
  ...
)

adjust_y_axis_title(
  plot,
  title = ggplot2::waiver(),
  fontsize = NULL,
  family = NULL,
  face = NULL,
  color = NULL,
  ...
)

adjust_caption(
  plot,
  caption = ggplot2::waiver(),
  fontsize = NULL,
  family = NULL,
  face = NULL,
  color = NULL,
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
title

Plot or axes title.
fontsize

Font size in points. Defaults to 7.
family

The typeface to use. The validity of this value will depend on the graphics device being used for rendering the plot. See the systemfonts vignette for guidance on the best way to access fonts installed on your computer. The values "sans", "serif", and "mono" should always be valid and will select the default typeface for the respective styles. However, what is considered default is dependant on the graphics device and the operating system.
face

Font face ("plain", "italic", "bold", "bold.italic")
color

A hex color for the stroke color. For example, "#FFFFFF" for white.
...

Arguments passed on to ggplot2::element_text().
caption

Plot caption.

Details

Adjust the plot title, axis titles and caption

Value

A tidyplot object.

Examples

# Plot without adjustments
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Adjust description
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_title("This is my fantastic plot title") |>
  adjust_x_axis_title("Treatment group") |>
  adjust_y_axis_title("Disease score") |>
  adjust_legend_title("Legend title") |>
  adjust_caption("Here goes the caption")

# Plotmath expressions
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  adjust_title("$H[2]*O$") |>
  adjust_x_axis_title("$H[2]*O$") |>
  adjust_y_axis_title("$H[2]*O$") |>
  adjust_legend_title("$H[2]*O$") |>
  adjust_caption("$H[2]*O$")

Adjust axes

Description

Adjust axes

Usage

adjust_x_axis(
  plot,
  title = ggplot2::waiver(),
  breaks = ggplot2::waiver(),
  labels = NULL,
  limits = NULL,
  padding = c(NA, NA),
  rotate_labels = FALSE,
  transform = "identity",
  cut_short_scale = FALSE,
  force_continuous = FALSE,
  ...
)

adjust_y_axis(
  plot,
  title = ggplot2::waiver(),
  breaks = ggplot2::waiver(),
  labels = NULL,
  limits = NULL,
  padding = c(NA, NA),
  rotate_labels = FALSE,
  transform = "identity",
  cut_short_scale = FALSE,
  force_continuous = FALSE,
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
title

Axis title.
breaks

One of:

  • NULL for no breaks

  • waiver() for the default breaks computed by the transformation object

  • A numeric vector of positions

  • A function that takes the limits as input and returns breaks as output (e.g., a function returned by scales::extended_breaks()). Note that for position scales, limits are provided after scale expansion. Also accepts rlang lambda function notation.

labels

One of the options below. Please note that when labels is a vector, it is highly recommended to also set the breaks argument as a vector to protect against unintended mismatches.

  • NULL for no labels

  • waiver() for the default labels computed by the transformation object

  • A character vector giving labels (must be same length as breaks)

  • An expression vector (must be the same length as breaks). See ?plotmath for details.

  • A function that takes the breaks as input and returns labels as output. Also accepts rlang lambda function notation.

limits

Axis limits. For example, with limits = c(20, 90) the axis starts at 20 and ends at 90.
padding

Extra space between the data points and the axes. Defaults to c(NA, NA), which does not change the padding.
rotate_labels

Whether to rotate axis labels. If TRUE is set to 45 degrees. You can also provide custom degree values, for example, rotate_labels = 90. Defaults to FALSE.
transform

For continuous scales, the name of a transformation object or the object itself. Built-in transformations include "asn", "atanh", "boxcox", "date", "exp", "hms", "identity", "log", "log10", "log1p", "log2", "logit", "modulus", "probability", "probit", "pseudo_log", "reciprocal", "reverse", "sqrt" and "time".

A transformation object bundles together a transform, its inverse, and methods for generating breaks and labels. Transformation objects are defined in the scales package, and are called ⁠transform_<name>⁠. If transformations require arguments, you can call them from the scales package, e.g. scales::transform_boxcox(p = 2). You can create your own transformation with scales::new_transform().
cut_short_scale

Whether to shorten axis labels using K for thousand, M for million, and so on. Defaults to FALSE.
force_continuous

Whether to force the axis to be continuous. Defaults to FALSE.
...

Arguments passed on to ggplot2 scale function.

Details

Value

A tidyplot object.

Examples

# Plot without adjustments
animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points()

# New titles
animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_x_axis(title = "My new x-axis title") |>
  adjust_y_axis(title = "My new y-axis title")

# New titles with plotmath expressions
animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_x_axis(title = "$H[2]*O$") |>
  adjust_y_axis(title = "$E==m*c^{2}$")

# Axes limits
animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_x_axis(limits = c(-1000, 4000)) |>
  adjust_y_axis(limits = c(-200, 600))

# Rotate labels
animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_x_axis(rotate_labels = 90) |>
  adjust_y_axis(rotate_labels = 90)

# Increase plot area padding
animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_x_axis(padding = c(0.2, 0.2)) |>
  adjust_y_axis(padding = c(0.2, 0.2))

# Scale transformation
animals |>
  tidyplot(x = weight, y = size, color = family) |>
  add_data_points() |>
  adjust_x_axis(transform = "log10") |>
  adjust_y_axis(transform = "log2")

Subset data rows

Description

Subset data rows

Usage

all_rows()

filter_rows(..., .by = NULL)

max_rows(order_by, n, by = NULL, with_ties = TRUE, na_rm = FALSE)

min_rows(order_by, n, by = NULL, with_ties = TRUE, na_rm = FALSE)

first_rows(n, by = NULL)

last_rows(n, by = NULL)

sample_rows(n, by = NULL)

Arguments

...

<data-masking> Expressions that return a logical vector, defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. To combine expressions using | instead, wrap them in when_any(). Only rows for which all expressions evaluate to TRUE are kept (for filter()) or dropped (for filter_out()).
.by, by

<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.
order_by

<data-masking> Variable or function of variables to order by. To order by multiple variables, wrap them in a data frame or tibble.
n

The number of rows to select. If not are supplied, n = 1 will be used. If n is greater than the number of rows in the group, the result will be silently truncated to the group size.

A negative value of n will be subtracted from the group size. For example, n = -2 with a group of 5 rows will select 5 - 2 = 3 rows.
with_ties

Should ties be kept together? The default, TRUE, may return more rows than you request. Use FALSE to ignore ties, and return the first n rows.
na_rm

Should missing values in order_by be removed from the result? If FALSE, NA values are sorted to the end (like in dplyr::arrange()), so they will only be included if there are insufficient non-missing values to reach n.

Value

A function to achieve the desired data subsetting.

Examples

# Highlight all animals
animals |>
 tidyplot(x = weight, y = size) |>
 add_data_points() |>
 add_data_points(data = all_rows(),
  color = "red", shape = 1, size = 3)

# Highlight 3 animals with the highest weight
animals |>
 tidyplot(x = weight, y = size) |>
 add_data_points() |>
 add_data_points(data = max_rows(weight, n = 3),
  color = "red", shape = 1, size = 3)

# Highlight 3 animals with the lowest weight
animals |>
 tidyplot(x = weight, y = size) |>
 add_data_points() |>
 add_data_points(data = min_rows(weight, n = 3),
  color = "red", shape = 1, size = 3)

# Highlight the first 3 animals in the dataset
animals |>
 tidyplot(x = weight, y = size) |>
 add_data_points() |>
 add_data_points(data = first_rows(n = 3),
  color = "red", shape = 1, size = 3)

# Highlight the last 3 animals in the dataset
animals |>
 tidyplot(x = weight, y = size) |>
 add_data_points() |>
 add_data_points(data = last_rows(n = 3),
  color = "red", shape = 1, size = 3)

# Highlight 3 random animals
animals |>
 tidyplot(x = weight, y = size) |>
 add_data_points() |>
 add_data_points(data = sample_rows(n = 3),
  color = "red", shape = 1, size = 3)

Animals data

Description

Animals data

Usage

animals

Format

A data frame.

Source

ChatGPT-3.5, Caution: The accuracy of the data has not been verified.

Examples

dplyr::glimpse(animals)

Climate data

Description

Climate data

Usage

climate

Format

A data frame.

Source

National Oceanic and Atmospheric Administration, Temperature data, weather station Hamburg Fuhlsbüttel, Germany

Examples

dplyr::glimpse(climate)

Continuous color schemes

Description

For more information about the use of color schemes in tidyplots, check out this article: Color schemes

Usage

colors_continuous_viridis

colors_continuous_magma

colors_continuous_inferno

colors_continuous_plasma

colors_continuous_cividis

colors_continuous_rocket

colors_continuous_mako

colors_continuous_turbo

colors_continuous_bluepinkyellow

Format

An object of class tidycolor (inherits from character) of length 265.

An object of class tidycolor (inherits from character) of length 265.

An object of class tidycolor (inherits from character) of length 265.

An object of class tidycolor (inherits from character) of length 265.

An object of class tidycolor (inherits from character) of length 265.

An object of class tidycolor (inherits from character) of length 265.

An object of class tidycolor (inherits from character) of length 265.

An object of class tidycolor (inherits from character) of length 265.

An object of class tidycolor (inherits from character) of length 11.

Details

Color schemes can be conveniently previewed by using the print method of the tidycolor class. This will send a html preview to the RStudio Viewer pane. 

colors_continuous_viridis

colors_continuous_viridis

A tidyplots color scheme with 265 colors, downsampled to 42 colors.

c(
"#440154FF","#460A5DFF","#471264FF","#481B6DFF","#482374FF","#472C7AFF","#46337FFF","#443A83FF","#424186FF","#3F4889FF","#3C508BFF","#39568CFF","#365D8DFF","#33638DFF","#306A8EFF","#2D708EFF","#2B758EFF","#297B8EFF","#26818EFF","#24878EFF","#228D8DFF","#20928CFF","#1F988BFF","#1F9F88FF","#20A486FF","#24AA83FF","#29AF7FFF","#31B57BFF","#3BBB75FF","#45C06FFF","#53C569FF","#5EC962FF","#6ECE58FF","#7BD250FF","#8AD647FF","#9CD93CFF","#AADC32FF","#BDDF26FF","#CCE11EFF","#DEE318FF","#EDE51BFF","#FDE725FF") 
colors_continuous_magma

colors_continuous_magma

A tidyplots color scheme with 265 colors, downsampled to 42 colors.

c(
"#000004FF","#02020DFF","#060519FF","#0C0927FF","#130D34FF","#1C1044FF","#231252FF","#2E1162FF","#38106CFF","#420F75FF","#4E117BFF","#57157EFF","#611880FF","#6A1C81FF","#752181FF","#7D2482FF","#882781FF","#922B81FF","#9B2E7FFF","#A6317DFF","#AF357BFF","#BB3978FF","#C43C75FF","#CF4070FF","#D8456CFF","#DF4B68FF","#E85362FF","#EE5B5EFF","#F4675CFF","#F7715CFF","#FA7E5EFF","#FC8961FF","#FD9668FF","#FEA16EFF","#FEAB75FF","#FEB87EFF","#FEC287FF","#FECF92FF","#FDD99BFF","#FDE6A8FF","#FCF0B2FF","#FCFDBFFF") 
colors_continuous_inferno

colors_continuous_inferno

A tidyplots color scheme with 265 colors, downsampled to 42 colors.

c(
"#000004FF","#02020EFF","#07051AFF","#0D082AFF","#150B37FF","#1F0C48FF","#290B54FF","#350A60FF","#3E0966FF","#490B6AFF","#540F6DFF","#5D126EFF","#68166EFF","#71196EFF","#7C1D6DFF","#85216BFF","#8F2568FF","#992766FF","#A22B62FF","#AD305DFF","#B53458FF","#C03952FF","#C73E4CFF","#D04545FF","#D84C3EFF","#DF5237FF","#E55C30FF","#EB6429FF","#F06F20FF","#F47918FF","#F7840FFF","#F98E09FF","#FB9B06FF","#FCA60CFF","#FCB115FF","#FBBF24FF","#F9C932FF","#F6D746FF","#F3E259FF","#F1EE73FF","#F3F68BFF","#FCFFA4FF") 
colors_continuous_plasma

colors_continuous_plasma

A tidyplots color scheme with 265 colors, downsampled to 42 colors.

c(
"#0D0887FF","#1C068EFF","#290593FF","#360498FF","#3F049CFF","#4B03A1FF","#5502A4FF","#5F01A6FF","#6700A8FF","#7100A8FF","#7B02A8FF","#8405A7FF","#8E0BA5FF","#9511A1FF","#9E199DFF","#A62098FF","#AD2792FF","#B42E8DFF","#BB3488FF","#C23C81FF","#C8437BFF","#CD4A76FF","#D35171FF","#D9586AFF","#DE5F65FF","#E26560FF","#E76E5BFF","#EB7556FF","#EF7E50FF","#F3854BFF","#F68E44FF","#F89540FF","#FB9F3AFF","#FCA835FF","#FDB030FF","#FEBA2CFF","#FDC328FF","#FCCE25FF","#FAD824FF","#F7E325FF","#F4ED27FF","#F0F921FF") 
colors_continuous_cividis

colors_continuous_cividis

A tidyplots color scheme with 265 colors, downsampled to 42 colors.

c(
"#00204DFF","#002557FF","#002961FF","#002D6DFF","#00306FFF","#01366EFF","#173A6DFF","#253E6CFF","#2E436CFF","#36476BFF","#3F4C6BFF","#45506BFF","#4C546CFF","#52596CFF","#595E6DFF","#5E626EFF","#64666FFF","#696B71FF","#6E6F73FF","#747475FF","#797977FF","#7F7D78FF","#848279FF","#8B8779FF","#918C78FF","#979178FF","#9D9677FF","#A39A76FF","#ABA074FF","#B1A573FF","#B7AA71FF","#BEAF6FFF","#C5B56CFF","#CCBB69FF","#D2C066FF","#D9C562FF","#E0CB5EFF","#E8D259FF","#EED753FF","#F7DD4DFF","#FDE346FF","#FFEA46FF") 
colors_continuous_rocket

colors_continuous_rocket

A tidyplots color scheme with 265 colors, downsampled to 42 colors.

c(
"#03051AFF","#0A091FFF","#120D25FF","#1C112BFF","#241432FF","#2E1739FF","#36193FFF","#411B44FF","#491D49FF","#531E4DFF","#5E1F52FF","#671F55FF","#721F57FF","#7B1F59FF","#871E5BFF","#921C5BFF","#9D1B5BFF","#A7195AFF","#B01759FF","#BC1656FF","#C51852FF","#CE1D4EFF","#D62449FF","#DE2E44FF","#E43841FF","#E8413EFF","#ED4F3EFF","#EF5A41FF","#F26747FF","#F3724EFF","#F47E57FF","#F58860FF","#F5946BFF","#F69D75FF","#F6A77FFF","#F6B28CFF","#F6BB97FF","#F7C6A6FF","#F7CEB2FF","#F8D8C1FF","#F9E0CEFF","#FAEBDDFF") 
colors_continuous_mako

colors_continuous_mako

A tidyplots color scheme with 265 colors, downsampled to 42 colors.

c(
"#0B0405FF","#12080DFF","#180D16FF","#1E111FFF","#241628FF","#2A1B33FF","#2F1F3DFF","#342547FF","#372852FF","#3B2D5BFF","#3E3367FF","#403871FF","#413E7EFF","#414387FF","#3F4B90FF","#3D5296FF","#3A5A9AFF","#38619DFF","#37689FFF","#3670A0FF","#3576A2FF","#357DA3FF","#3484A5FF","#348CA7FF","#3492A8FF","#3499AAFF","#35A0ABFF","#37A6ACFF","#3AAEADFF","#3FB5ADFF","#44BDADFF","#4BC2ADFF","#55CAADFF","#61CFACFF","#72D4ADFF","#86D9B1FF","#96DDB5FF","#A9E1BDFF","#B6E6C5FF","#C5EAD0FF","#D0EFDAFF","#DEF5E5FF") 
colors_continuous_turbo

colors_continuous_turbo

A tidyplots color scheme with 265 colors, downsampled to 42 colors.

c(
"#30123BFF","#372365FF","#3D3489FF","#4147AEFF","#4456C8FF","#4669E0FF","#4777EFFF","#4688FBFF","#4197FFFF","#38A5FBFF","#2CB7F0FF","#22C4E3FF","#1AD3D1FF","#18DDC2FF","#1DE7B2FF","#29EFA2FF","#3DF58CFF","#53FA79FF","#69FD66FF","#83FF51FF","#98FE43FF","#AAFB39FF","#BAF635FF","#CBED34FF","#D9E436FF","#E4DA38FF","#F0CC3AFF","#F7C13AFF","#FCB136FF","#FEA230FF","#FE8F28FF","#FB7D21FF","#F56918FF","#EF5911FF","#E74A0CFF","#DD3C08FF","#D23105FF","#C32503FF","#B51C01FF","#A31301FF","#910B01FF","#7A0403FF") 
colors_continuous_bluepinkyellow

colors_continuous_bluepinkyellow

A tidyplots color scheme with 11 colors.

c(
"#00034D","#000F9F","#001CEF","#241EF5","#5823F6","#A033E0","#E85AB1","#F1907C","#F4AF63","#FCE552","#FFFB6D")

Discrete color schemes

Description

For more information about the use of color schemes in tidyplots, check out this article: Color schemes

Usage

colors_discrete_friendly

colors_discrete_seaside

colors_discrete_apple

colors_discrete_friendly_long

colors_discrete_okabeito

colors_discrete_ibm

colors_discrete_metro

colors_discrete_candy

colors_discrete_alger

colors_discrete_rainbow

Format

An object of class tidycolor (inherits from character) of length 6.

An object of class tidycolor (inherits from character) of length 5.

An object of class tidycolor (inherits from character) of length 7.

An object of class tidycolor (inherits from character) of length 7.

An object of class tidycolor (inherits from character) of length 7.

An object of class tidycolor (inherits from character) of length 5.

An object of class tidycolor (inherits from character) of length 5.

An object of class tidycolor (inherits from character) of length 5.

An object of class tidycolor (inherits from character) of length 5.

An object of class tidycolor (inherits from character) of length 9.

Details

The signature theme of tidyplots colors_discrete_friendly was adapted from the Okabe & Ito color palette that was designed to work well for people with color vision deficiency.

Color schemes can be conveniently previewed by using the print method of the tidycolor class. This will send a html preview to the RStudio Viewer pane. 

colors_discrete_friendly

colors_discrete_friendly

A tidyplots color scheme with 6 colors.

c(
"#0072B2","#56B4E9","#009E73","#F5C710","#E69F00","#D55E00") 
colors_discrete_seaside

colors_discrete_seaside

A tidyplots color scheme with 5 colors.

c(
"#8ecae6","#219ebc","#023047","#ffb703","#fb8500") 
colors_discrete_apple

colors_discrete_apple

A tidyplots color scheme with 7 colors.

c(
"#ff3b30","#ff9500","#ffcc00","#4cd964","#5ac8fa","#007aff","#5856d6") 
colors_discrete_friendly_long

colors_discrete_friendly_long

A tidyplots color scheme with 7 colors.

c(
"#CC79A7","#0072B2","#56B4E9","#009E73","#F5C710","#E69F00","#D55E00") 
colors_discrete_okabeito

colors_discrete_okabeito

A tidyplots color scheme with 7 colors.

c(
"#E69F00","#56B4E9","#009E73","#F0E442","#0072B2","#D55E00","#CC79A7") 
colors_discrete_ibm

colors_discrete_ibm

A tidyplots color scheme with 5 colors.

c(
"#5B8DFE","#725DEE","#DD227D","#FE5F00","#FFB109") 
colors_discrete_metro

colors_discrete_metro

A tidyplots color scheme with 5 colors.

c(
"#4DACD6","#4FAE62","#F6C54D","#E37D46","#C02D45") 
colors_discrete_candy

colors_discrete_candy

A tidyplots color scheme with 5 colors.

c(
"#9b5de5","#f15bb5","#fee440","#00bbf9","#00f5d4") 
colors_discrete_alger

colors_discrete_alger

A tidyplots color scheme with 5 colors.

c(
"#000000","#1A5B5B","#ACC8BE","#F4AB5C","#D1422F") 
colors_discrete_rainbow

colors_discrete_rainbow

A tidyplots color scheme with 9 colors.

c(
"#FF7777","#FFAB74","#FFE577","#DBF47B","#91E480","#7CC9E5","#7DA8E6","#887DE6","#BC7BE4")

Diverging color schemes

Description

For more information about the use of color schemes in tidyplots, check out this article: Color schemes

Usage

colors_diverging_blue2red

colors_diverging_blue2brown

colors_diverging_BuRd

colors_diverging_BuYlRd

colors_diverging_spectral

colors_diverging_icefire

Format

An object of class tidycolor (inherits from character) of length 17.

An object of class tidycolor (inherits from character) of length 17.

An object of class tidycolor (inherits from character) of length 11.

An object of class tidycolor (inherits from character) of length 11.

An object of class tidycolor (inherits from character) of length 96.

An object of class tidycolor (inherits from character) of length 96.

Details

Color schemes can be conveniently previewed by using the print method of the tidycolor class. This will send a html preview to the RStudio Viewer pane. 

colors_diverging_blue2red

colors_diverging_blue2red

A tidyplots color scheme with 17 colors.

c(
"#0000FF","#1F1FFF","#3F3FFF","#5F5FFF","#7F7FFF","#9F9FFF","#BFBFFF","#DFDFFF","#FFFFFF","#FFDFDF","#FFBFBF","#FF9F9F","#FF7F7F","#FF5F5F","#FF3F3F","#FF1F1F","#FF0000") 
colors_diverging_blue2brown

colors_diverging_blue2brown

A tidyplots color scheme with 17 colors.

c(
"#1961A5","#2671B5","#2D80BF","#268CC9","#119DD8","#00B2EB","#66C5EF","#C4E5F8","#FEFCF6","#FDEEB8","#FCDD67","#F6C445","#E78B43","#DD5642","#DB3E34","#CA3632","#B3322E") 
colors_diverging_BuRd

colors_diverging_BuRd

A tidyplots color scheme with 11 colors.

c(
"#053061","#2166AC","#4393C3","#92C5DE","#D1E5F0","#F7F7F7","#FDDBC7","#F4A582","#D6604D","#B2182B","#67001F") 
colors_diverging_BuYlRd

colors_diverging_BuYlRd

A tidyplots color scheme with 11 colors.

c(
"#313695","#4575B4","#74ADD1","#ABD9E9","#E0F3F8","#FFFFBF","#FEE090","#FDAE61","#F46D43","#D73027","#A50026") 
colors_diverging_spectral

colors_diverging_spectral

A tidyplots color scheme with 96 colors, downsampled to 42 colors.

c(
"#5b53a4","#525fa9","#486cb0","#3f77b5","#3389bd","#3d95b8","#47a0b3","#58b2ac","#64c0a6","#71c6a5","#86cfa5","#94d4a4","#a2d9a4","#b5e1a2","#c3e79f","#cfec9d","#e1f399","#e9f69d","#eef8a4","#f6fbb0","#fcfeba","#fffdbc","#fff7b2","#feeda1","#fee797","#fee08b","#fed27f","#fdc776","#fdbd6d","#fdad60","#fba05b","#f99153","#f67f4b","#f47044","#ef6645","#e55749","#df4e4b","#d9444d","#cb334d","#be254a","#b41947","#a90d45") 
colors_diverging_icefire

colors_diverging_icefire

A tidyplots color scheme with 96 colors, downsampled to 42 colors.

c(
"#b7e3d9","#a9d9d6","#98cdd2","#8ac4d0","#72b6ce","#63adcd","#55a3cd","#4394ce","#3987cf","#377cd0","#3f69c9","#465ebe","#4954b0","#474792","#42407b","#3c3a69","#323050","#2c2b42","#272636","#212028","#1f1e21","#201e1e","#261e1f","#332023","#3d2228","#4a252e","#5c2935","#6d2b3b","#7b2d40","#932e44","#a22f44","#b33341","#c53c3c","#d24737","#da5334","#e66734","#eb753a","#ef8445","#f39a5f","#f7ab75","#fab887","#fcc69a")

Dinosaurs data

Description

Dinosaurs data

Usage

dinosaurs

Format

A data frame.

Source

ChatGPT-3.5, Caution: The accuracy of the data has not been verified.

Examples

dplyr::glimpse(dinosaurs)

Distributions data

Description

Distributions data

Usage

distributions

Format

A data frame.

Source

tidyplots package

Examples

dplyr::glimpse(distributions)

Energy data

Description

Energy data

Usage

energy

Format

A data frame.

Source

Energy-Charts, Energy production data, Germany

Examples

dplyr::glimpse(energy)

Energy week data

Description

Energy week data

Usage

energy_week

Format

A data frame.

Source

Energy-Charts, Energy production data, Germany

Examples

dplyr::glimpse(energy_week)

EU countries data

Description

EU countries data

Usage

eu_countries

Format

A data frame.

Source

ChatGPT-3.5, Caution: The accuracy of the data has not been verified.

Examples

dplyr::glimpse(eu_countries)

Flip x and y-axis

Description

[Superseded]

This function is superseded because in many cases, flip_plot() can easily be replaced by swapping the x and y axis. Some plot components additionally require to set the orientation argument to "y".

Usage

flip_plot(plot, ...)

Arguments

plot

A tidyplot generated with the function tidyplot().
...

Arguments passed on to ggplot2::coord_flip().

Value

A tidyplot object.

Examples

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  flip_plot()

energy |>
  tidyplot(x = year, y = energy, color = energy_type) |>
  add_barstack_absolute() |>
  flip_plot()

# Better solutions without `flip_plot()`
study |>
  tidyplot(x = score, y = treatment, color = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

energy |>
  tidyplot(x = energy, y = year, color = energy_type) |>
  add_barstack_absolute(orientation = "y")

Format p values

Description

Format p values

Usage

format_p_value(x, accuracy = 1e-04)

Arguments

x

A number to format.
accuracy

A number to round to. For example, use 0.01 to show 2 decimal places of precision. Defaults to 0.0001, corresponding to 4 decimal places of precision.

Value

Formatted number as character string.

Examples

format_p_value(0.03445553)
format_p_value(0.0003445553)
format_p_value(0.00003445553)

RNA-Seq expression data

Description

RNA-Seq expression data

Usage

gene_expression

Format

A data frame.

Source

Bassoon proteinopathy drives neurodegeneration in multiple sclerosis, Nature Neuroscience 2019

GSE104899, Gene Expression Omnibus

Examples

dplyr::glimpse(gene_expression)

New color scheme

Description

For more information about the use of color schemes in tidyplots, check out this article: Color schemes

Usage

new_color_scheme(x, name = "Untitled color scheme", reverse = FALSE)

Arguments

x

Character vector of hex colors. For example x = c("#FF00FF", "#00FFFF").
name

Name of the custom color scheme.
reverse

Whether the order should be reversed or not. Defaults to FALSE, meaning not reversed.

Value

A tidyplot object.

Examples

new_color_scheme(c("#ECA669","#E06681","#8087E2","#E2D269"))

new_color_scheme(c("#ECA669","#E06681","#8087E2","#E2D269"),
  name = "my_custom_color_scheme")

Principle component analysis data

Description

Principle component analysis data

Usage

pca

Format

A data frame.

Source

Bassoon proteinopathy drives neurodegeneration in multiple sclerosis, Nature Neuroscience 2019

Examples

dplyr::glimpse(pca)

Remove plot area clipping

Description

Remove plot area clipping

Usage

remove_clipping(plot)

Arguments

plot

A tidyplot generated with the function tidyplot().

Value

A tidyplot object.

Examples

# Before removing
spendings |>
  tidyplot(x = amount, y = category, color = category) |>
  add_sum_bar(alpha = 0.2) |>
  add_sum_dash() |>
  add_sum_value(accuracy = 1, color = "black")

# After removing
spendings |>
  tidyplot(x = amount, y = category, color = category) |>
  add_sum_bar(alpha = 0.2) |>
  add_sum_dash() |>
  add_sum_value(accuracy = 1, color = "black") |>
  remove_clipping()

Remove legend or legend title

Description

Remove legend or legend title

Usage

remove_legend(plot)

remove_legend_title(plot)

Arguments

plot

A tidyplot generated with the function tidyplot().

Value

A tidyplot object.

Examples

# Before removing
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar()

# After removing
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_legend_title()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_legend()

Remove plot area padding

Description

Remove plot area padding

Usage

remove_padding(plot, force_continuous = FALSE)

Arguments

plot

A tidyplot generated with the function tidyplot().
force_continuous

Whether to force the axis to be continuous. Defaults to FALSE.

Value

A tidyplot object.

Examples

# Before removing
animals |>
  tidyplot(x = weight, y = speed, color = family) |>
  add_data_points()

# After removing
animals |>
  tidyplot(x = weight, y = speed, color = family) |>
  add_data_points() |>
  remove_padding()

Remove plot title or caption

Description

Remove plot title or caption

Usage

remove_title(plot)

remove_caption(plot)

Arguments

plot

A tidyplot generated with the function tidyplot().

Value

A tidyplot object.

Examples

# Before removing
animals |>
  tidyplot(x = weight, y = speed, color = family) |>
  add_data_points() |>
  add_title("Name of the plot") |>
  add_caption("This is the caption")

# After removing
animals |>
  tidyplot(x = weight, y = speed, color = family) |>
  add_data_points() |>
  add_title("Name of the plot") |>
  add_caption("This is the caption") |>
  remove_title()

animals |>
  tidyplot(x = weight, y = speed, color = family) |>
  add_data_points() |>
  add_title("Name of the plot") |>
  add_caption("This is the caption") |>
  remove_caption()

Remove x-axis or parts of it

Description

Remove x-axis or parts of it

Usage

remove_x_axis(plot)

remove_x_axis_line(plot)

remove_x_axis_ticks(plot)

remove_x_axis_labels(plot)

remove_x_axis_title(plot)

Arguments

plot

A tidyplot generated with the function tidyplot().

Value

A tidyplot object.

Examples

# Before removing
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar()

# After removing
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_x_axis_line()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_x_axis_ticks()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_x_axis_labels()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_x_axis_title()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_x_axis()

Remove y-axis or parts of it

Description

Remove y-axis or parts of it

Usage

remove_y_axis(plot)

remove_y_axis_line(plot)

remove_y_axis_ticks(plot)

remove_y_axis_labels(plot)

remove_y_axis_title(plot)

Arguments

plot

A tidyplot generated with the function tidyplot().

Value

A tidyplot object.

Examples

# Before removing
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar()

# After removing
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_y_axis_line()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_y_axis_ticks()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_y_axis_labels()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_y_axis_title()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar() |>
  remove_y_axis()

Rename axis or color levels

Description

Rename axis or color levels

Usage

rename_x_axis_levels(plot, new_names)

rename_y_axis_levels(plot, new_names)

rename_color_levels(plot, new_names)

Arguments

plot

A tidyplot generated with the function tidyplot().
new_names

Named character vector in the format c("old1" = "new1", "old2" = "new2").

Value

A tidyplot object.

Examples

# Before adjustments
study |>
  tidyplot(x = treatment, y = score) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Rename x-axis levels
study |>
  tidyplot(x = treatment, y = score) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  rename_x_axis_levels(new_names = c(
    "A" = "This",
    "B" = "is",
    "C" = "totally",
    "D" = "new"))

# Before adjustments
study |>
  tidyplot(x = score, y = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Rename y-axis levels
study |>
  tidyplot(x = score, y = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  rename_y_axis_levels(new_names = c(
    "A" = "This",
    "B" = "is",
    "C" = "totally",
    "D" = "new"))

# Before adjustment
study |>
  tidyplot(x = group, y = score, color = dose) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Rename color levels
study |>
  tidyplot(x = group, y = score, color = dose) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  rename_color_levels(new_names = c(
    "high" = "Sky high",
    "low" = "Deep low"))

Reorder axis or color levels

Description

Reorder axis or color levels

Usage

reorder_x_axis_levels(plot, ...)

reorder_y_axis_levels(plot, ...)

reorder_color_levels(plot, ...)

Arguments

plot

A tidyplot generated with the function tidyplot().
...

Arguments passed on to forcats::fct_relevel().

Value

A tidyplot object.

Examples

# Before adjustments
study |>
  tidyplot(x = treatment, y = score) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Reorder x-axis levels
study |>
  tidyplot(x = treatment, y = score) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  reorder_x_axis_levels("D", "B", "A")

# Before adjustments
study |>
  tidyplot(x = score, y = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Reorder y-axis levels
study |>
  tidyplot(x = score, y = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  reorder_y_axis_levels("D", "B", "A")

# Before adjustment
study |>
  tidyplot(x = group, y = score, color = dose) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Reorder color levels
study |>
  tidyplot(x = group, y = score, color = dose) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  reorder_color_levels("low")

Reverse axis or color levels

Description

Reverse axis or color levels

Usage

reverse_x_axis_levels(plot)

reverse_y_axis_levels(plot)

reverse_color_levels(plot)

Arguments

plot

A tidyplot generated with the function tidyplot().

Value

A tidyplot object.

Examples

# Before adjustments
study |>
  tidyplot(x = treatment, y = score) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Reverse x-axis levels
study |>
  tidyplot(x = treatment, y = score) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  reverse_x_axis_levels()

# Before adjustments
study |>
  tidyplot(x = score, y = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Reverse y-axis levels
study |>
  tidyplot(x = score, y = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  reverse_y_axis_levels()

# Before adjustment
study |>
  tidyplot(x = group, y = score, color = dose) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Reverse color levels
study |>
  tidyplot(x = group, y = score, color = dose) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  reverse_color_levels()

Save plots to file

Description

This function takes a plot or list of plots and writes them to a (multipage) file.

Usage

save_plot(
  plot = ggplot2::last_plot(),
  filename,
  width = NA,
  height = NA,
  units = c("mm", "cm", "in"),
  padding = 0.1,
  multiple_files = FALSE,
  view_plot = TRUE,
  ...
)

Arguments

plot

Plot to save, defaults to last plot displayed.
filename

File name to create on disk.
width, height

Dimensions of the graphic device to save the plot. Defaults to NA. In case of NA, the dimensions are inferred from the incoming plot object (see Details).
units

Units of length. Defaults to "mm".
padding

Extra space around the saved plot. Defaults to 0.1 meaning 10%.
multiple_files

Whether to save multiple pages as individual files.
view_plot

Whether to view the plot on screen after saving.
...

Other arguments passed on to the graphics device function, as specified by device.

Details

Handling of file dimensions. Output file dimensions are determined according the the following precedence.

  1. The width and height arguments.

  2. Dimensions inferred from the incoming plot object with absolute dimensions.

  3. System default device dimensions.

Value

A tidyplot object.

Examples

# Save plot to file
study |>
  tidyplot(treatment, score) |>
  add_data_points() |>
  save_plot("single_plot.pdf")

# Save intermediate stages to file
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  add_data_points_beeswarm() |>
  save_plot("before.pdf") |>
  adjust_colors(colors_discrete_seaside) |>
  save_plot("after.pdf")



# Save multipage PDF file
gene_expression |>
  dplyr::slice_head(n = 160) |>
  tidyplot(group, expression, color = sample_type) |>
  add_data_points() |>
  adjust_size(width = 30, height = 25) |>
  split_plot(by = external_gene_name, nrow = 2, ncol = 2) |>
  save_plot("multipage_plot.pdf")

# Save multiple PDF files
gene_expression |>
  dplyr::slice_head(n = 160) |>
  tidyplot(group, expression, color = sample_type) |>
  add_data_points() |>
  adjust_size(width = 30, height = 25) |>
  split_plot(by = external_gene_name, nrow = 2, ncol = 2) |>
  save_plot("plot.pdf", multiple_files = TRUE)

Sort axis or color levels

Description

Sort axis or color levels

Usage

sort_x_axis_levels(plot, ..., .fun = NULL, .reverse = FALSE)

sort_y_axis_levels(plot, ..., .fun = NULL, .reverse = FALSE)

sort_color_levels(plot, ..., .fun = NULL, .reverse = FALSE)

Arguments

plot

A tidyplot generated with the function tidyplot().
...

Optional variables to use for sorting.
.fun

Override the function used for sorting. Is automatically determined from the plot.
.reverse

Whether the order should be reversed or not. Defaults to FALSE, meaning not reversed.

Value

A tidyplot object.

Examples

# Before adjustments
study |>
  tidyplot(x = treatment, y = score) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Sort x-axis levels by score
study |>
  tidyplot(x = treatment, y = score) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  sort_x_axis_levels()

# Before adjustments
study |>
  tidyplot(x = score, y = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Sort y-axis levels by score
study |>
  tidyplot(x = score, y = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  sort_y_axis_levels()

# Before adjustment
study |>
  tidyplot(x = group, y = score, color = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar()

# Sort color levels by score
study |>
  tidyplot(x = group, y = score, color = treatment) |>
  add_data_points() |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  sort_color_levels()

Spending data

Description

Spending data

Usage

spendings

Format

A data frame.

Source

tidyplots package

Examples

dplyr::glimpse(spendings)

Split plot into multiple subplots

Description

Split plot into multiple subplots

Usage

split_plot(
  plot,
  by = NULL,
  rows = NULL,
  cols = NULL,
  ncol = NULL,
  nrow = NULL,
  axes = "all",
  axis.titles = "all",
  scales = NULL,
  ...
)

Arguments

plot

A tidyplot generated with the function tidyplot().
by

One variable that should be used for splitting.
rows, cols

Two variables that should be used for splitting, representing rows and columns, respectively.
ncol, nrow

The number of columns and rows per page. Only takes effect when using by to split by a single variable.
axes

Determines which axes will be drawn in case of fixed scales. When "margins", axes will be drawn at the exterior margins. "all_x" and "all_y" will draw the respective axes at the interior panels too, whereas "all" (the default) will draw all axes at all panels.
axis.titles

Determines which axis titles will be drawn. When "margins", axis titles will be drawn at the exterior margins. "all" (the default) will draw all axis titles at all panels. "single" will draw a single axis title per multiplot layout.
scales

Should scales be fixed "fixed", free ("free"), or free in one dimension ("free_x", "free_y")? Defaults to "free" when providing one splitting variable via by. Defaults to "fixed" when providing two splitting variables via rows and cols.
...

Arguments passed on to the geom function.

Value

A tidyplot object.

Examples

# Before splitting
energy |>
  dplyr::filter(year %in% c(2005, 2010, 2015, 2020)) |>
  tidyplot(y = energy, color = energy_source) |>
  add_donut() |>
  adjust_size(width = 25, height = 25)

# Split by year
energy |>
  dplyr::filter(year %in% c(2005, 2010, 2015, 2020)) |>
  tidyplot(y = energy, color = energy_source) |>
  add_donut() |>
  adjust_size(width = 25, height = 25) |>
  split_plot(by = year)

# Change dimensions of subplots
energy |>
  dplyr::filter(year %in% c(2005, 2010, 2015, 2020)) |>
  tidyplot(y = energy, color = energy_source) |>
  add_donut() |>
  adjust_size(width = 15, height = 15) |>
  split_plot(by = year)

# Spread plots across multiple pages
energy |>
  dplyr::filter(year %in% c(2005, 2010, 2015, 2020)) |>
  tidyplot(y = energy, color = energy_source) |>
  add_donut() |>
  adjust_size(width = 25, height = 25) |>
  split_plot(by = year, ncol = 2, nrow = 1)

# Split by two variables
energy |>
  dplyr::mutate(decade = paste0(floor(year / 10) * 10, "s")) |>
  tidyplot(y = energy, color = energy_source) |>
  add_donut() |>
  adjust_size(14,14) |>
  split_plot(rows = decade, cols = energy_type)

Study data

Description

Study data

Usage

study

Format

A data frame.

Source

tidyplots package

Examples

dplyr::glimpse(study)

Themes

Description

Themes

Usage

theme_tidyplot(plot, fontsize = 7)

theme_ggplot2(plot, fontsize = 7)

theme_minimal_xy(plot, fontsize = 7)

theme_minimal_x(plot, fontsize = 7)

theme_minimal_y(plot, fontsize = 7)

Arguments

plot

A tidyplot generated with the function tidyplot().
fontsize

Font size in points. Defaults to 7.

Value

A tidyplot object.

Examples

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_sem_errorbar() |>
  add_mean_dash() |>
  theme_tidyplot()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_sem_errorbar() |>
  add_mean_dash() |>
  theme_ggplot2()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_sem_errorbar() |>
  add_mean_dash() |>
  theme_minimal_xy()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_sem_errorbar() |>
  add_mean_dash() |>
  theme_minimal_x()

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points() |>
  add_sem_errorbar() |>
  add_mean_dash() |>
  theme_minimal_y()

Create a new tidyplot

Description

Create a new tidyplot

Usage

tidyplot(
  data,
  ...,
  width = NULL,
  height = NULL,
  unit = NULL,
  dodge_width = NULL,
  my_style = NULL,
  paper = NULL,
  ink = NULL
)

Arguments

data

A tidy data.frame to use for plotting.
...

Mappings for the x axis, y axis and color, see examples. Additional argument are passed to ggplot2::aes().
width, height

Dimensions of the plot area. The default (NULL) retrieves the setting from the tidyplots options, which defaults to 50. NA results in taking all available space (ggplot2 default).
unit

Unit of the plot area width and height. The default (NULL) retrieves the setting from the tidyplots options, which defaults to "mm".
dodge_width

For adjusting the distance between grouped objects. The default (NULL) retrieves the setting from the tidyplots options, which defaults to 0.8 for plots with at least one discrete axis and to 0 for plots with two continuous axes.
my_style

Styling function to apply to the plot. The default (NULL) retrieves the setting from the tidyplots options, which default to no additional styling.
paper

Background color. The default (NULL) retrieves the setting from the tidyplots options, which defaults to "#FFFFFF".
ink

Foreground color. The default (NULL) retrieves the setting from the tidyplots options, which defaults to "#000000".

Examples

study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_data_points_beeswarm()

study |>
  tidyplot(x = group, y = score, color = dose) |>
  add_mean_bar()

# Change plot area size
study |>
  tidyplot(x = treatment, y = score, color = treatment,
    width = 25, height = 25) |>
  add_data_points_beeswarm()

# Change dodge_width
study |>
  tidyplot(x = group, y = score, color = dose, dodge_width = 0.3) |>
  add_mean_bar()

Tidyplots options

Description

Control the settings for formatting tidyplots globally.

Usage

tidyplots_options(
  width = NULL,
  height = NULL,
  unit = NULL,
  dodge_width = NULL,
  my_style = NULL,
  paper = NULL,
  ink = NULL
)

Arguments

width, height

Dimensions of the plot area. The default (NULL) retrieves the setting from the tidyplots options, which defaults to 50. NA results in taking all available space (ggplot2 default).
unit

Unit of the plot area width and height. The default (NULL) retrieves the setting from the tidyplots options, which defaults to "mm".
dodge_width

For adjusting the distance between grouped objects. The default (NULL) retrieves the setting from the tidyplots options, which defaults to 0.8 for plots with at least one discrete axis and to 0 for plots with two continuous axes.
my_style

Styling function to apply to the plot. The default (NULL) retrieves the setting from the tidyplots options, which default to no additional styling.
paper

Background color. The default (NULL) retrieves the setting from the tidyplots options, which defaults to "#FFFFFF".
ink

Foreground color. The default (NULL) retrieves the setting from the tidyplots options, which defaults to "#000000".

Value

The old options invisibly

Examples

# Define custom style
my_style <- function(x) x |>
  adjust_colors(colors_discrete_candy) |>
  adjust_font(family = "mono")

# Set tidyplots options
tidyplots_options(
  width = 3,
  height = 4,
  unit = "cm",
  dodge_width = 1,
  my_style = my_style
  )

# Plot
study |>
  tidyplot(x = group, y = score, color = dose) |>
  add_mean_bar()

# Reset tidyplots options
tidyplots_options()

# Same plot
study |>
  tidyplot(x = group, y = score, color = dose) |>
  add_mean_bar()

Time course data

Description

Time course data

Usage

time_course

Format

A data frame.

Source

tidyplots package

Examples

dplyr::glimpse(time_course)

View plot on screen

Description

View plot on screen

Usage

view_plot(plot, data = all_rows(), title = ggplot2::waiver(), ...)

Arguments

plot

A tidyplot generated with the function tidyplot().
data

The data to be displayed in this layer. There are three options:

  • If all_rows() (the default) the complete dataset is displayed.

  • A function to subset the plot data. See filter_rows() and friends.

  • A data.frame to override the plot data.

title

Plot title.
...

Arguments passed on to print().

Details

Value

A tidyplot object.

Examples

# View intermediate stages on screen
study |>
  tidyplot(x = treatment, y = score, color = treatment) |>
  add_mean_bar(alpha = 0.4) |>
  add_sem_errorbar() |>
  add_data_points_beeswarm() |>
  view_plot(title = "Before changing color scheme") |>
  adjust_colors(colors_discrete_seaside) |>
  view_plot(title = "After changing color scheme")

# View data subsets on screen
gene_expression |>
  tidyplot(x = condition, y = expression, color = sample_type) |>
  add_mean_dash() |>
  add_sem_errorbar() |>
  add_data_points_beeswarm() |>
  view_plot(data = filter_rows(external_gene_name == "Apol6"),
    title = "Apol6") |>
  view_plot(data = filter_rows(external_gene_name == "Bsn"),
    title = "Bsn")