plot_prcomp: Visualize principal component analysis

In DataExplorer: Automate Data Exploration and Treatment

Visualize principal component analysis

Description

Visualize output of prcomp.

Usage

plot_prcomp(
  data,
  variance_cap = 0.8,
  maxcat = 50L,
  prcomp_args = list(scale. = TRUE),
  geom_label_args = list(),
  title = NULL,
  ggtheme = theme_gray(),
  theme_config = list(),
  nrow = 3L,
  ncol = 3L,
  parallel = FALSE
)

Arguments

data	input data
variance_cap	maximum cumulative explained variance allowed for all principal components. Default is 80%.
maxcat	maximum categories allowed for each discrete feature. The default is 50.
prcomp_args	a list of other arguments to prcomp
geom_label_args	a list of other arguments to geom_label
title	plot title starting from page 2.
ggtheme	complete ggplot2 themes. The default is theme_gray.
theme_config	a list of configurations to be passed to theme.
nrow	number of rows per page
ncol	number of columns per page
parallel	enable parallel? Default is FALSE.

Details

When cumulative explained variance exceeds variance_cap, remaining principal components will be ignored. Set variance_cap to 1 for all principal components.

Discrete features containing more categories than maxcat specifies will be ignored.

Value

invisibly return the named list of ggplot objects

Note

Discrete features will be dummify-ed first before passing to prcomp.

Missing values may create issues in prcomp. Consider na.omit your input data first.

Features with zero variance are dropped.

Examples

plot_prcomp(na.omit(airquality), nrow = 2L, ncol = 2L)

DataExplorer documentation built on May 29, 2024, 12:19 p.m.