Returns optimal histogram edge positions in accordance to Wand (1995)'s criterion'

add_label(argnames, f, args...; kwargs...)

This function ensures that labels are passed to the plotting command, if it accepts them.

If f does not accept keyword arguments, and kwargs is empty, it will only forward args....

If the user has provided keyword arguments, but f does not accept them, then it will error.

andrewsplot(args...; kw...)

Shows each row of an array (or table) as a line. The x argument specifies a grouping variable. This is a way to visualize structure in high-dimensional data. https://en.wikipedia.org/wiki/Andrews_plot #Examples

using RDatasets, StatsPlots
iris = dataset("datasets", "iris")
@df iris andrewsplot(:Species, cols(1:4))

binned kernel function estimator

corrplot

This plot type shows the correlation among input variables. A correlation plot may be produced by a matrix.

A correlation matrix can also be created from the columns of a DataFrame using the @df macro like so:

@df iris corrplot([:SepalLength :SepalWidth :PetalLength :PetalWidth])

The marker color in scatter plots reveals the degree of correlation. Pass the desired colorgradient to markercolor.

With the default gradient positive correlations are blue, neutral are yellow and negative are red. In the 2d-histograms, the color gradient shows the frequency of points in that bin (as usual, controlled by seriescolor).

corrplot

This plot type shows the correlation among input variables. A correlation plot may be produced by a matrix.

A correlation matrix can also be created from the columns of a DataFrame using the @df macro like so:

@df iris corrplot([:SepalLength :SepalWidth :PetalLength :PetalWidth])

The marker color in scatter plots reveals the degree of correlation. Pass the desired colorgradient to markercolor.

With the default gradient positive correlations are blue, neutral are yellow and negative are red. In the 2d-histograms, the color gradient shows the frequency of points in that bin (as usual, controlled by seriescolor).

corrplot

This plot type shows the correlation among input variables. A correlation plot may be produced by a matrix.

A correlation matrix can also be created from the columns of a DataFrame using the @df macro like so:

@df iris corrplot([:SepalLength :SepalWidth :PetalLength :PetalWidth])

The marker color in scatter plots reveals the degree of correlation. Pass the desired colorgradient to markercolor.

With the default gradient positive correlations are blue, neutral are yellow and negative are red. In the 2d-histograms, the color gradient shows the frequency of points in that bin (as usual, controlled by seriescolor).

covellipse(μ, Σ; showaxes=false, n_std=1, n_ellipse_vertices=100)

Plot a confidence ellipse of the 2×2 covariance matrix Σ, centered at μ. The ellipse is the contour line of a Gaussian density function with mean μ and variance Σ at n_std standard deviations. If showaxes is true, the two axes of the ellipse are also plotted.

covellipse(μ, Σ; showaxes=false, n_std=1, n_ellipse_vertices=100)

Plot a confidence ellipse of the 2×2 covariance matrix Σ, centered at μ. The ellipse is the contour line of a Gaussian density function with mean μ and variance Σ at n_std standard deviations. If showaxes is true, the two axes of the ellipse are also plotted.

covellipse(μ, Σ; showaxes=false, n_std=1, n_ellipse_vertices=100)

Plot a confidence ellipse of the 2×2 covariance matrix Σ, centered at μ. The ellipse is the contour line of a Gaussian density function with mean μ and variance Σ at n_std standard deviations. If showaxes is true, the two axes of the ellipse are also plotted.

extract_columns_and_names(df, syms...)

Extracts columns and their names (if the column number is an integer) into a slightly complex Tuple.

The structure goes as ((columndata...), names). This is unpacked by the @df macro into gensym'ed variables, which are passed to the plotting function.

to_corrplot_matrix(mat)

Transforms the input into a correlation plot matrix. Meant to be overloaded by other types!

Returns optimal histogram bin widths in accordance to Wand (1995)'s criterion'

`@df d x`

Convert every symbol in the expression x with the respective column in d if it exists.

If you want to avoid replacing the symbol, escape it with ^.

NA values are replaced with NaN for columns of Float64 and "" or Symbol() for strings and symbols respectively.

x can be either a plot command or a block of plot commands.

`@df x`

Curried version of @df d x. Outputs an anonymous function d -> @df d x.