In a scatter plot, each row of `data_frame` is represented by a symbol
mark in 2D space.

In a bar plot, each row of `data_frame` is represented as a rectangular
mark.

In a histogram, rows of `data_frame` are grouped together into a
rectangular mark to visualize the 1D distribution of an aggregate
function `histfunc` (e.g. the count or sum) of the value `y` (or `x` if
`orientation` is `'h'`).

In a violin plot, rows of `data_frame` are grouped together into a
curved mark to visualize their distribution.

In a box plot, rows of `data_frame` are grouped together into a
box-and-whisker mark to visualize their distribution.

Each box spans from quartile 1 (Q1) to quartile 3 (Q3). The second
quartile (Q2) is marked by a line inside the box. By default, the
whiskers correspond to the box' edges +/- 1.5 times the interquartile
range (IQR: Q3-Q1), see "points" for other options.

**Principal component analysis (2 dimensions)**
Given a collection of points in two, three, or higher dimensional space,
a "best fitting" line can be defined as one that minimizes the average squared distance
from a point to the line. The next best-fitting line can be similarly chosen from
directions perpendicular to the first. Repeating this process yields an orthogonal
basis in which different individual dimensions of the data are uncorrelated.
These basis vectors are called principal components, and several related procedures
principal component analysis (PCA).

In a 3D scatter plot, each row of `data_frame` is represented by a
symbol mark in 3D space.

In a 2D line plot, each row of `data_frame` is represented as vertex of
a polyline mark in 2D space.

In a density heatmap, rows of `data_frame` are grouped together into
colored rectangular tiles to visualize the 2D distribution of an
aggregate function `histfunc` (e.g. the count or sum) of the value `z`.

In a density contour plot, rows of `data_frame` are grouped together
into contour marks to visualize the 2D distribution of an aggregate
function `histfunc` (e.g. the count or sum) of the value `z`.

In a parallel categories (or parallel sets) plot, each row of
`data_frame` is grouped with other rows that share the same values of
`dimensions` and then plotted as a polyline mark through a set of
parallel axes, one for each of the `dimensions`.

In a parallel coordinates plot, each row of `data_frame` is represented
by a polyline mark which traverses a set of parallel axes, one for each
of the `dimensions`.

Plot a scatter mattrix for all selected columns

**Principal component analysis (3 dimensions)**
Given a collection of points in two, three, or higher dimensional space,
a "best fitting" line can be defined as one that minimizes the average squared distance
from a point to the line. The next best-fitting line can be similarly chosen from
directions perpendicular to the first. Repeating this process yields an orthogonal
basis in which different individual dimensions of the data are uncorrelated.
These basis vectors are called principal components, and several related procedures
principal component analysis (PCA).

A generalization of Fisher's linear discriminant, a method used in statistics,
pattern recognition, and machine learning to find a linear combination of features
that characterizes or separates two or more classes of objects or events. The resulting
combination may be used as a linear classifier, or, more commonly, for dimensionality
reduction before later classification.

A supervised learning method for classifying multivariate data into distinct classes
according to a given distance metric over the data. Functionally, it serves the same
purposes as the K-nearest neighbors algorithm, and makes direct use of a related concept
termed stochastic nearest neighbors.

Plot correlation matrix