API

AxisEntries(axis::Union{Axis, Nothing}, entries::Vector{Entry}, labels, scales)

Define all ingredients to make plots on an axis. Each scale can be either a CategoricalScale (for discrete collections), such as CategoricalScale(["a", "b"], ["red", "blue"]), or a function, such as log10. Other scales may be supported in the future.

density(; datalimits, npoints, kernel, bandwidth)

Fit a kernel density estimation of data. Here, datalimits specifies the range for which the density should be calculated, npoints is the number of points used by Makie to draw the line and kernel and bandwidth are forwarded to KernelDensity.kde.

expectation(args...)

Compute the expected value of the last argument conditioned on the preceding ones.

frequency()

Compute a frequency table of the arguments.

histogram(; bins=automatic, weights=automatic, normalization=:none)

Compute a histogram. bins can be an Int to create that number of equal-width bins over the range of values. Alternatively, it can be a sorted iterable of bin edges. The histogram can be normalized by setting normalization. Possible values are:

• :pdf: Normalize by sum of weights and bin sizes. Resulting histogram has norm 1 and represents a PDF.
• :density: Normalize by bin sizes only. Resulting histogram represents count density of input and does not have norm 1.
• :probability: Normalize by sum of weights only. Resulting histogram represents the fraction of probability mass for each bin and does not have norm 1.
• :none: Do not normalize.

Weighted data is supported via the keyword weights.

iscontinuous(v)

Determine whether v should be treated as a continuous or categorical vector.

linear(; interval)

Compute a linear fit of y ~ 1 + x. An optional named mapping weights determines the weights. Use interval to specify what type of interval the shaded band should represent. Valid values of interval are :confidence delimiting the uncertainty of the predicted relationship, and :prediction delimiting estimated bounds for new data points.

linesfill(xs, ys; lower, upper, kwargs...)

Line plot with a shaded area between lower and upper. If lower and upper are not given, shaded area is between 0 and ys.

Attributes

Available attributes and their defaults for Combined{AlgebraOfGraphics.linesfill!, T} where T are:

linesfill(xs, ys; lower, upper, kwargs...)

Line plot with a shaded area between lower and upper. If lower and upper are not given, shaded area is between 0 and ys.

Attributes

Available attributes and their defaults for Combined{AlgebraOfGraphics.linesfill, T} where T are:

  color       :black
  colormap    :viridis
  colorrange  MakieCore.Automatic()
  fillalpha   0.15
  linestyle   "nothing"
  linewidth   1.5
  lower       MakieCore.Automatic()
  upper       MakieCore.Automatic()

nonnumeric(x)

Transform x into a non numeric type that is printed and sorted in the same way.

plottypes_attributes(entries)

Return plottypes and relative attributes, as two vectors of the same length, for the given entries.

renamer(arr::Union{AbstractArray, Tuple})

Utility to rename a categorical variable, as in renamer([value1 => label1, value2 => label2]). The keys of all pairs should be all the unique values of the categorical variable and the values should be the corresponding labels. The order of arr is respected in the legend.

Examples

julia> r = renamer(["class 1" => "Class One", "class 2" => "Class Two"])
AlgebraOfGraphics.Renamer{Vector{String}, Vector{String}}(["class 1", "class 2"], ["Class One", "Class Two"])

julia> println(r("class 1"))
Class One

Alternatively, a sequence of pair arguments may be passed.

julia> r = renamer("class 1" => "Class One", "class 2" => "Class Two")
AlgebraOfGraphics.Renamer{Tuple{String, String}, Tuple{String, String}}(("class 1", "class 2"), ("Class One", "Class Two"))

julia> println(r("class 1"))
Class One

If arr does not contain Pairs, elements of arr are assumed to be labels, and the unique values of the categorical variable are taken to be the indices of the array. This is particularly useful for dims mappings.

Examples

julia> r = renamer(["Class One", "Class Two"])
AlgebraOfGraphics.Renamer{Nothing, Vector{String}}(nothing, ["Class One", "Class Two"])

julia> println(r(2))
Class Two

smooth(span=0.75, degree=2)

Fit a loess model. span is the degree of smoothing, typically in [0,1]. Smaller values result in smaller local context in fitting. degree is the polynomial degree used in the loess model.

sorter(ks...)

Utility to reorder a categorical variable, as in sorter("low", "medium", "high"). ks should include all the unique values of the categorical variable. The order of ks is respected in the legend.

to_entry(layer::Layer)

Convert layer to equivalent entry, excluding transformations.