ggfacto: The Graph of an Analysis

In ggfacto: Graphs for Correspondence Analysis

The Graph of an Analysis

Description

The graph of a multiple correspondence analysis, a correspondence analysis or a principal component analysis, in the plane of two axes — one verb for the three, as interpret is their one table:

• a multiple correspondence analysis draws its active levels, and behind them the cloud of its individuals as answer profiles (see ggmca);

• a correspondence analysis draws the levels of its two variables, and of the supplementary variables its table holds (see ggca);

• a principal component analysis draws its biplot: the cloud of the individuals with the variables' arrows rescaled onto it (see ggpca); 'profiles = FALSE' draws its circle of correlations (see ggpca_cor_circle).

Hovering a point shows the data behind it: a level's crosstabs, coloured by their deviations from the mean, an individual's answers or values, a supplementary level's percentages or means. Supplementary variables and clusters are added from the data frame, for an MCA or a PCA, and from the table, for a CA.

Usage

ggfacto(
  res,
  data,
  sup_vars,
  clust,
  axes = c(1, 2),
  axes_reverse = NULL,
  type,
  profiles = TRUE,
  active_tables,
  ellipses = NULL,
  title,
  xlim,
  ylim,
  text_size = 3.5,
  size_scale_max = NULL,
  lang = NULL,
  interactive = FALSE,
  ...
)

Arguments

res	An analysis made with multiple_correspondence_analysis, correspondence_analysis or principal_component_analysis (or with FactoMineR::MCA(), CA() or PCA(), or GDAtools::speMCA() or csMCA()).
data	The data frame the analysis was made on, in which to find the supplementary variables and the clusters, for an MCA or a PCA: the whole data frame, even when the analysis was made on a subset of it. A CA reads its table instead.
sup_vars	<tidy-select> The supplementary variables, as in ‘tab()': 'sup_vars = c(SEX, AGE)'. For a CA, they are the table’s other variables: 'tab(data, c(relig, marital), c(partyid, race))'.
clust	The clusters, made with hierarchical_clust: for an MCA or a PCA, the column of 'data' that holds them ('clust = cah'); for a CA, the clusters of the levels of one margin ('clust = hierarchical_clust(res, ncp = 2, nb_clust = 4)').
axes	The axes to draw, as a numeric vector of length 2.
axes_reverse	'1' to invert left and right, '2' to invert up and down, '1:2' for both.
type	How the levels are drawn: "text", "labels" or "points", and "facets" for one graph per level of the first supplementary variable (MCA, PCA). By default "text", and "points" for a CA.
profiles	Should the cloud of the individuals be drawn? As answer profiles for an MCA, as a biplot for a PCA. By default, yes; ‘FALSE' draws the levels alone, and a PCA’s circle of correlations when nothing else is asked for.
active_tables	The crosstabs in the tooltips of an MCA: see ggmca.
ellipses	A number between 0 and 1 draws a concentration ellipse around the individuals of each level of the first supplementary variable: 0.5 holds half of them.
title	The title of the graph.
xlim, ylim	Horizontal and vertical limits, as numeric vectors of length 2.
text_size	Size of text.
size_scale_max	The size of the largest point. By default, computed from the spread of the weights of the points drawn.
lang	NULL (the session's language), "en" or "fr": the language of the tooltips and of the axis titles.
interactive	Set to TRUE to get the interactive graph at once, as ggi makes it. By default, a ggplot2 graph, to which elements can be added with '+', before passing it to ggi.
...	Further arguments of the analysis's own graph function, which document them: ggmca (such as 'tooltip_vars', 'max_profiles', 'color_groups'), ggca ('show_sup', 'uppercase', 'tooltips') or ggpca.

Details

An argument that an analysis does not take stops with an explanation: 'data', 'sup_vars', 'profiles', 'active_tables' and 'ellipses' for a CA, 'active_tables' for a PCA.

Value

A ggplot object, or an html widget with 'interactive = TRUE'.

Examples

data(tea, package = "FactoMineR")
res.mca <- multiple_correspondence_analysis(tea, 1:18)
tea <- tea |>
  dplyr::mutate(clust = hierarchical_clust(res.mca, ncp = 3, nb_clust = 5))
ggfacto(res.mca, tea, sup_vars = SPC, clust = clust, interactive = TRUE)

gss <- forcats::gss_cat |>
  dplyr::filter(!relig %in% c("No answer", "Don't know", "Not applicable"),
                !partyid %in% c("No answer", "Don't know"))
res.ca <- correspondence_analysis(tabxplor::tab(gss, c(relig, marital), partyid))
ggfacto(res.ca, interactive = TRUE)

cars <- mtcars
cars$cyl <- factor(cars$cyl)
res.pca <- principal_component_analysis(cars, c(mpg, disp, hp, drat, wt, qsec))
ggfacto(res.pca, cars, sup_vars = cyl, ellipses = 0.5)

ggfacto documentation built on Sept. 23, 2026, 1:08 a.m.