Skip to content

API

Index¤

Reference - Exported functions¤

# TidierPlots.aes — Method.

aes(args...; kwargs...)

Details

TBD

source

# TidierPlots.facet_grid — Method.

facet_grid(;rows, cols, scales = "fixed", switch = "none")

facetgrid() forms a matrix of panels defined by row and column faceting variables. It is most useful when you have two discrete variables, and all combinations of the variables exist in the data. If you have only one variable with many levels, try facetwrap().

Arguments

  • rows (required): Variable to use for the rows of the matrix
  • cols (required): Variable to use for the columns of the matrix
  • scales (optional): Should the scales be fixed or free? Options: "free", "freex", "freey"
  • switch (optional): Flip the labels from their default "top and right". Options: "x", "y", "both".

source

# TidierPlots.geom_bar — Function.

geom_bar(aes(...), ...)
geom_bar(plot::GGPlot, aes(...), ...)

Represent the counts of a grouping variable as columns.

Details

The columns are stacked by default, and the behavior can be changed with the "position" argument. The position can either be "stack" or "dodge". If the argument is "dodge", then a a grouping variable will also need to be supplied to aes. Alternatively you can supply the grouping variable to dodge within the aesthetic.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.BarPlot)

Required Aesthetics

  • x OR y (not both)

Optional Aesthetics (see aes)

  • color / colour
  • strokecolor / strokecolour
  • dodge
  • group

Optional Arguments

  • position: "stack" (default) or "dodge"
  • stroke / strokewidth
  • strokecolor / strokecolour
  • direction: :y (default) or :x
  • dodge_gap
  • gap

Examples

# vertical bar plot
ggplot(penguins) + geom_bar(@aes(x = species))

# horizontal bar plot
ggplot(penguins) + geom_bar(@aes(y = species))

# stacked
ggplot(penguins, @aes(x = species, fill=sex)) + geom_bar()

# dodged
ggplot(penguins, @aes(x = species, fill=sex, dodge = sex)) + geom_bar()

source

# TidierPlots.geom_boxplot — Function.

geom_boxplot(aes(...), ...)
geom_boxplot(plot::GGPlot, aes(...), ...)

Compactly displays the distribution of continuous data.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.BoxPlot)

Required Aesthetics

  • x (integer or categorical)
  • y (numeric)

Optional Aesthetics (see aes)

  • color / colour (used in conjunction with dodge)
  • dodge

Optional Arguments

  • orientation=:vertical: orientation of box (:vertical or :horizontal)
  • width=1
  • gap=0.2
  • show_notch=false
  • nothchwidth=0.5
  • show_median=true
  • dodge_gap=0.03

Examples

ggplot(penguins, @aes(x=species, y=bill_length_mm)) +
    geom_boxplot()

ggplot(penguins, @aes(y=species, x=bill_length_mm)) +
    geom_boxplot()

ggplot(penguins, @aes(x=species, y=bill_length_mm, fill=sex)) +
    geom_boxplot()

source

# TidierPlots.geom_col — Function.

geom_col(aes(...), ...)
geom_col(plot::GGPlot, aes(...), ...)

Represent data as columns.

Details

The columns are stacked by default, and the behavior can be changed with the "position" argument. The position can either be "stack" or "dodge". If the argument is "dodge", then a a grouping variable will also need to be supplied to aes. Alternatively you can supply the grouping variable to dodge within the aesthetic.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.BarPlot)

Required Aesthetics

  • x
  • y

Optional Aesthetics (see aes)

  • color / colour
  • strokecolor / strokecolour
  • dodge
  • group

Optional Arguments

  • position: "stack" (default) or "dodge"
  • stroke / strokewidth
  • strokecolor / strokecolour
  • direction: :y (default) or :x
  • dodge_gap
  • gap

Examples

df = @chain penguins begin
    @group_by(species, sex)
    @summarize(mean_bill_length_mm = mean(bill_length_mm))
    @ungroup()
end

ggplot(df) +
    geom_col(@aes(x = species, y = mean_bill_length_mm))

# dodge using the group and position arguments
ggplot(df) +
    geom_col(@aes(x = species, y = mean_bill_length_mm, group = sex),
             position="dodge")

# dodge using the dodge aesthetic
ggplot(df) +
    geom_col(@aes(x = species, y = mean_bill_length_mm, dodge = sex))

# color based on grouping variable
ggplot(df) +
    geom_col(@aes(x = species, y = mean_bill_length_mm, color = sex))

source

# TidierPlots.geom_density — Function.

geom_density(aes(...), ...)
geom_density(plot::GGPlot, aes(...), ...)

Represent data as a smooth density curve.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Density)

Required Aesthetics

  • x

Optional Aesthetics (see aes)

  • NA

Optional Arguments

  • color / colour
  • colormap / palette
  • strokecolor / strokecolour
  • strokewidth / stroke
  • linestyle / linetype
  • direction=:x
  • npoints=200

Examples

ggplot(penguins, @aes(x=bill_length_mm)) + geom_density()

ggplot(penguins, @aes(x=bill_length_mm)) +
    geom_density(color = :black, stroke = 2)

source

# TidierPlots.geom_errorbar — Method.

geom_errorbar(aes(...), ...)
geom_errorbar(plot::GGPlot, aes(...), ...)

Represents data as a vertical interval.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Rangebars)

Required Aesthetics

  • x
  • ymin
  • ymax

Optional Aesthetics (see aes)

  • NA

Optional Arguments

  • color / colour
  • direction=:y
  • linewidth
  • whiskerwidth / width

Examples

df = DataFrame(
    trt   = [1, 1, 2, 2],
    resp  = [1, 5, 3, 4],
    group = [1, 2, 1, 2],
    lower = [0.8, 4.6, 2.4, 3.6],
    upper = [1.1, 5.3, 3.3, 4.2],
)

ggplot(df, @aes(x = trt, ymin = lower, ymax = upper)) +
    geom_errorbar(width=20, linewidth=2)

source

# TidierPlots.geom_errorbarh — Method.

geom_errorbarh(aes(...), ...)
geom_errorbarh(plot::GGPlot, aes(...), ...)

Represents data as a horizontal interval.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Rangebars)

Required Aesthetics

  • y
  • xmin
  • xmax

Optional Aesthetics (see aes)

  • NA

Optional Arguments

  • color / colour
  • direction=:x
  • linewidth
  • whiskerwidth / width

Examples

df = DataFrame(
    trt   = [1, 1, 2, 2],
    resp  = [1, 5, 3, 4],
    group = [1, 2, 1, 2],
    lower = [0.8, 4.6, 2.4, 3.6],
    upper = [1.1, 5.3, 3.3, 4.2],
)

ggplot(df, @aes(y = trt, xmin = lower, xmax = upper)) +
    geom_errorbarh(linewidth=2)

source

# TidierPlots.geom_histogram — Function.

geom_histogram(aes(...), ...)
geom_histogram(plot::GGPlot, aes(...), ...)

Represents data as a histogram.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Hist)

Required Aesthetics

  • x

Optional Aesthetics (see aes)

  • NA

Optional Arguments

  • bins
  • normalization
  • color / colour
  • stroke / strokewidth
  • strokecolor / strokecolour

Examples

ggplot(penguins, @aes(x = bill_length_mm)) +
    geom_histogram()

ggplot(penguins, @aes(x = bill_length_mm)) +
    geom_histogram(normalization=:probability, bins=20)

source

# TidierPlots.geom_hline — Method.

geom_hline(aes(...), ...)
geom_hline(plot::GGPlot, aes(...), ...)

Plot a horizontal line at the given y-intercept(s).

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.HLines)

Required Aesthetics

  • NA

Optional Aesthetics (see aes)

  • yintercept(s)
  • color / colour

Optional Arguments

  • yintercept(s)
  • color / colour
  • linewidth
  • linestyle / linetype
  • colormap / palette
  • alpha

Examples

# Plot only a single y-intercept
ggplot() + geom_hline(yintercept = 3)

# Plot multiple y-intercepts
ggplot() + geom_hline(yintercept = [-1, 4])

# Plot multiple y-intercepts mapped to a column
df = DataFrame(y = rand(4))
ggplot(df, @aes(yintercept = y)) + geom_hline()

source

# TidierPlots.geom_label — Function.

geom_label(aes(...), ...)
geom_label(plot::GGPlot, aes(...), ...)

Plot text on a graph.

ggplot2 deviation

Currently this method is the same as geom_text, and does not put the text in a rectangle like in ggplot2.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Text)

Required Aesthetics

  • x
  • y
  • text

Optional Aesthetics (see aes)

  • color / colour

Optional Arguments

  • color / colour
  • colormap / palette
  • align: tuple of positions (e.g. (:left, :bottom))
  • font
  • justification
  • rotation
  • fontsize
  • strokewidth
  • strokecolor
  • glowwidth / glow
  • glowcolor / glowcolour
  • word_wrap_width
  • alpha

Examples

df = DataFrame(
    x = [1,1,2,2],
    y = [1,2,1,2],
    t = ["A", "B", "C", "D"]
)

ggplot(df, @aes(x=x, y=y, text=t, color=t)) + geom_label()

ggplot(df, @aes(x=x, y=y, text=t, color=t)) +
    geom_label(fontsize=24, align=(:left, :bottom), font=:bold) +
    geom_point() +
    lims(x = (0, 3), y = (0, 3))

source

# TidierPlots.geom_line — Function.

geom_line(aes(...), ...)
geom_line(plot::GGPlot, aes(...), ...)

Represents data as connected points in the order of the variable on the x-axis.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Lines)

Required Aesthetics

  • x
  • y

Optional Aesthetics (see aes)

  • color / colour

Optional Arguments

  • color / colour
  • colormap / palette
  • linestyle / linetype
  • linewidth
  • alpha

Examples

xs = range(0, 2pi, length=30)
df = DataFrame(x = xs, y = sin.(xs))

ggplot(df, @aes(x = x, y = y)) + geom_line()

source

# TidierPlots.geom_path — Function.

geom_path(aes(...), ...)
geom_path(plot::GGPlot, aes(...), ...)

Represents data as connected points in the order in which they appear in the data.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Lines)

Required Aesthetics

  • x
  • y

Optional Aesthetics (see aes)

  • color / colour

Optional Arguments

  • color / colour
  • colormap / palette
  • linestyle / linetype
  • linewidth
  • alpha

Examples

ggplot(penguins, @aes(x = bill_length_mm, y = bill_depth_mm)) +
    geom_path()

source

# TidierPlots.geom_point — Function.

geom_point(aes(...), ...)
geom_point(plot::GGPlot, aes(...), ...)

The point geom is used to create scatterplots. The scatterplot is most useful for displaying the relationship between two continuous variables. It can be used to compare one continuous and one categorical variable, or two categorical variables, but other charts are usually more appropriate. A bubblechart is a scatterplot with a third variable mapped to the size of points.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to. This is typically used to facilitate creating your ggplot as part of a @chain.
  • data (DataFrame): Data to use for this geom. If not provided, the geom will inherit the data from ggplot.
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • inherit_aes: should the geom inherit aes from the ggplot?
  • ...: options that are not mapped to a column (passed to Makie.Scatter)

Overplotting

The biggest potential problem with a scatterplot is overplotting: whenever you have more than a few points, points may be plotted on top of one another. This can severely distort the visual appearance of the plot. There is no one solution to this problem, but there are some techniques that can help. You can add additional information with geomsmooth(), or if you have few unique x values, geomboxplot() may also be useful. Another technique is to make the points transparent (e.g. geompoint(alpha = 0.05)) or very small (e.g. geompoint(shape = '.')).

Required Aesthetics

  • x
  • y

Optional Aesthetics (see aes)

  • color / colour
  • fill
  • shape
  • size
  • stroke

Optional Arguments

  • color / colour
  • colormap / palette
  • marker / shape
  • markersize / size
  • strokewidth / stroke
  • strokecolor / strokecolour
  • glowwidth / glow
  • glowcolor / glowcolour
  • alpha

Examples

p = ggplot(penguins, @aes(bill_length_mm, bill_depth_mm))
p + geom_point()

# add aesthetic mappings
p + geom_point(aes(colour = :sex))

source

# TidierPlots.geom_smooth — Method.

geom_smooth(aes(...), ...)
geom_smooth(plot::GGPlot, aes(...), ...)

Represent data as a smoothed or linear fit.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Lines)

Required Aesthetics

  • x
  • y

Optional Aesthetics (see aes)

  • NA

Optional Arguments

  • method: either "smooth" (default, loess fit) or "lm" (linear fit)
  • color / colour
  • linewidth
  • alpha
  • linestyle / linetype

Examples

xs = range(0, 2pi, length=30)
ys = sin.(xs) .+ randn(length(xs)) * 0.5
df = DataFrame(x = xs, y = ys)

ggplot(df, @aes(x = x, y = y)) + geom_smooth() + geom_point()

ggplot(penguins, @aes(x = bill_length_mm, y = bill_depth_mm)) +
    geom_smooth(color=:red, linewidth=10, alpha=0.5)

source

# TidierPlots.geom_step — Function.

geom_step(aes(...), ...)
geom_step(plot::GGPlot, aes(...), ...)

Represents data as a stairstep plot.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Stairs)

Required Aesthetics

  • x
  • y

Optional Aesthetics (see aes)

  • color / colour

Optional Arguments

  • color / colour
  • colormap / palette
  • linestyle / linetype
  • linewidth
  • alpha

Examples

xs = range(0, 2pi, length=30)
df = DataFrame(x = xs, y = sin.(xs))

ggplot(df, @aes(x = x, y = y)) + geom_step()

source

# TidierPlots.geom_text — Function.

geom_text(aes(...), ...)
geom_text(plot::GGPlot, aes(...), ...)

Plot text on a graph.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Text)

Required Aesthetics

  • x
  • y
  • text

Optional Aesthetics (see aes)

  • color / colour

Optional Arguments

  • color / colour
  • colormap / palette
  • align: tuple of positions (e.g. (:left, :bottom))
  • font
  • justification
  • rotation
  • fontsize
  • strokewidth
  • strokecolor
  • glowwidth / glow
  • glowcolor / glowcolour
  • word_wrap_width
  • alpha

Examples

df = DataFrame(
    x = [1,1,2,2],
    y = [1,2,1,2],
    t = ["A", "B", "C", "D"]
)

ggplot(df, @aes(x=x, y=y, text=t, color=t)) + geom_text()

ggplot(df, @aes(x=x, y=y, text=t, color=t)) +
    geom_text(fontsize=24, align=(:left, :bottom), font=:bold) +
    geom_point() +
    lims(x = (0, 3), y = (0, 3))

source

# TidierPlots.geom_tile — Function.

geom_tile(aes(...), ...)
geom_tile(plot::GGPlot, aes(...), ...)

Plots a heatmap as a collection of rectangles.

Details

x, y, and z must all be the same length, and there must be no duplicate (x, y) pairs. You can think of x, y, and z as triples of the form (x, y, f(x, y)).

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Heatmap)

Required Aesthetics

  • x
  • y
  • z

Optional Aesthetics (see aes)

  • NA

Optional Arguments

  • interpolate=false
  • colormap / palette
  • alpha

Examples

function mandelbrot(x, y)
    z = c = x + y*im
    for i in 1:30.0; abs(z) > 2 && return i; z = z^2 + c; end; 0
end

xs = -2:0.01:1
ys = -1.1:0.01:1.1
xys = Iterators.product(xs, ys) |> collect |> vec
zs = map(xy -> mandelbrot(xy[1], xy[2]), xys)

df = DataFrame(
    x = first.(xys),
    y = last.(xys),
    z = zs
)

ggplot(df, @aes(x = x, y = y, z = z)) + geom_tile()

source

# TidierPlots.geom_violin — Function.

geom_(aes(...), ...)
geom_(plot::GGPlot, aes(...), ...)

Represents data as a violin plot.

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.Violin)

Required Aesthetics

  • x (integer or categorical)
  • y (numeric)

Optional Aesthetics (see aes)

  • color / colour (used in conjunction with dodge)
  • dodge

Optional Arguments

  • orientation=:vertical: orientation of box (:vertical or :horizontal)
  • width=1
  • gap=0.2
  • show_notch=false
  • nothchwidth=0.5
  • show_median=true
  • dodge_gap=0.03

Examples

ggplot(penguins, @aes(x=species, y=bill_length_mm)) +
    geom_violin()

ggplot(penguins, @aes(x=species, y=bill_length_mm)) +
    geom_violin(orientation=:horizontal)

ggplot(penguins, @aes(x=species, y=bill_length_mm, fill=sex, dodge=sex)) +
    geom_violin()

source

# TidierPlots.geom_vline — Method.

geom_vline(aes(...), ...)
geom_vline(plot::GGPlot, aes(...), ...)

Plot a horizontal line at the given y-intercept(s).

Arguments

  • plot::GGPlot (optional): a plot object to add this geom to
  • aes(...): the names of the columns in the DataFrame that will be used in the mapping
  • ...: options that are not mapped to a column (passed to Makie.VLines)

Required Aesthetics

  • NA

Optional Aesthetics (see aes)

  • xintercept(s)
  • color / colour

Optional Arguments

  • xintercept(s)
  • color / colour
  • linewidth
  • linestyle / linetype
  • colormap / palette
  • alpha

Examples

# Plot only a single x-intercept
ggplot() + geom_vline(xintercept = 3)

# Plot multiple x-intercepts
ggplot() + geom_vline(xintercept = [-1, 4])

# Plot multiple x-intercepts mapped to a column
df = DataFrame(x = rand(4))
ggplot(df, @aes(xintercept = x)) + geom_vline()

source

# TidierPlots.guides — Method.

Sets which scales will get legends or colorbars. Use the name of the scale and either the string "legend" or "colorbar" to add a guide.

source

# TidierPlots.label_bytes — Method.

label_bytes(;units=:si, kwargs...)

Convert numeric values into human-readable strings representing byte sizes.

Arguments

  • units: Can be :si for SI units (powers of 10), :binary for binary units (powers of 1024), or a specific unit string (e.g., MB, GiB).
  • kwargs...: Additional keyword arguments passed to label_number.

Examples

labeler = label_bytes(units=:si)
labeler([1024, 2048]) # ["1.02 KB", "2.05 KB"]

source

# TidierPlots.label_currency — Method.

label_currency(;kwargs...)

Convert numeric values into currency strings.

Arguments

  • kwargs: Additional keyword arguments passed to label_number.

Examples

labeler = label_currency()
labeler([100, 200.75]) # ["$100", "$200.75"]

source

# TidierPlots.label_date — Method.

label_date(;format="m/d/Y")

Convert Date or DateTime values into formatted strings.

Arguments

  • format: A date format string.

Examples

labeler = label_date(format="Y-m-d")
labeler([Date(2020, 1, 1)]) # ["2020-1-1"]

source

# TidierPlots.label_log — Method.

label_log(;base=10, kwargs...)

Convert numeric values into logarithmic strings with a specified base.

Arguments

  • base: The logarithmic base.
  • kwargs: Additional keyword arguments passed to label_number.

Examples

labeler = label_log(base=10)
labeler([10, 100]) # ["10^1", "10^2"]

source

# TidierPlots.label_number — Method.

label_number(;precision=2, scale=1, prefix="", suffix="", decimal_mark=".", comma_mark=",", style_positive=:none, style_negative=:hyphen, kwargs...)

Format numeric values as strings with various styling options.

Arguments

  • precision: Number of decimal places.
  • scale: Scaling factor applied to the numbers before formatting.
  • prefix: String to prepend to the number.
  • suffix: String to append to the number.
  • decimal_mark: Character to use as the decimal point.
  • comma_mark: Character to use as the thousands separator.
  • style_positive: Style for positive numbers (:none, :plus, :space).
  • style_negative: Style for negative numbers (:hyphen, :parens).
  • kwargs: Additional keyword arguments passed on to format from Format.jl

Examples

labeler = label_number(precision=0, suffix="kg")
labeler([1500.12, -2000.12]) # ["1,500kg", "-2,000kg"]

source

# TidierPlots.label_ordinal — Method.

label_ordinal(;kwargs...)

Convert numeric values into ordinal strings (e.g., 1st, 2nd, 3rd).

Arguments

  • kwargs: Additional keyword arguments passed to label_number.

Examples

labeler = label_ordinal()
labeler([1, 2, 3]) # ["1st", "2nd", "3rd"]

source

# TidierPlots.label_percent — Method.

label_percent(;kwargs...)

Convert numeric values into percentage strings.

Arguments

  • kwargs: Additional keyword arguments passed to label_number.

Examples

labeler = label_percent()
labeler([0.1, 0.256]) # ["10%", "25.6%"]

source

# TidierPlots.label_pvalue — Method.

label_pvalue(;precision=2, kwargs...)

Format p-values, handling very small or large values with special notation.

Arguments

  • precision: Number of decimal places for thresholding small values.
  • kwargs: Additional keyword arguments passed to label_number.

Examples

labeler = label_pvalue()
labeler([0.0001, 0.05, 0.9999]) # ["<0.01", "0.05", ">0.99"]

source

# TidierPlots.label_scientific — Method.

label_scientific(;kwargs...)

Convert numeric values into scientific notation strings.

Arguments

  • kwargs: Additional keyword arguments passed to label_number.

Examples

labeler = label_scientific()
labeler([1000, 2000000]) # ["1e+03", "2e+06"]

source

# TidierPlots.label_wrap — Method.

label_wrap(width)

Wrap text strings to a specified width, breaking at spaces.

Arguments

  • width: The maximum number of characters in a line before wrapping.

Examples

labeler = label_wrap(10)
labeler(["This is a long sentence."]) # ["This is a
long
sentence."]

source

Reference - Internal functions¤

# TidierPlots.alpha_scale_to_ggoptions — Method.

Internal function. Converts args_dict to AxisOptions.

source

# TidierPlots.handle_point_color_and_fill — Method.

Color and fill work slightly strangely in geom_point in ggplot2. Replicates behaviour.

source

# TidierPlots.linewidth_scale_to_ggoptions — Method.

Internal function. Converts args_dict to AxisOptions.

source

# TidierPlots.make_alpha_lookup_continuous — Method.

Internal function. Takes a Dict and makes a function that maps a numeric vector to the range specfied in the Dict's :range key, or to [0.1, 1.0] if not specified.

source

# TidierPlots.make_linewidth_lookup_continuous — Method.

Internal function. Takes a Dict and makes a function that maps a numeric vector to the range specfied in the Dict's :range key, or to [0.1, 1.0] if not specified.

source

# TidierPlots.position_facets — Function.

Internal function. Given a list of names and (optionally) some constraints, return the relative position of the facets and their labels.

source

# TidierPlots.scale_custom — Function.

scale_custom(aes, function, legend_options = Dict())
scale_custom(plot, aes, function, legend_options = Dict())

Create a custom scale for any aes.

Arguments

  • plot: Optional. GGPlot to apply this scale to
  • aes: The aes that the scale is for
  • f: Function to apply to the raw data. Should accept one argument (vector of data as it appears in your DataFrame) and return a vector in the format expected by Makie.
  • legend_options: Used to generate the guides for the plot

source

# TidierPlots.shape_scale_to_ggoptions — Method.

Internal function. Converts args_dict to AxisOptions.

source

# TidierPlots.size_scale_to_ggoptions — Method.

Internal function. Converts args_dict to AxisOptions.

source