fviz_ca {factoextra}  R Documentation 
Visualize Correspondence Analysis
Description
Correspondence analysis (CA) is an extension of Principal Component Analysis (PCA) suited to analyze frequencies formed by two categorical variables. fviz_ca() provides ggplot2based elegant visualization of CA outputs from the R functions: CA [in FactoMineR], ca [in ca], coa [in ade4], correspondence [in MASS] and expOutput/epCA [in ExPosition]. Read more: Correspondence Analysis
fviz_ca_row(): Graph of row variables
fviz_ca_col(): Graph of column variables
fviz_ca_biplot(): Biplot of row and column variables
fviz_ca(): An alias of fviz_ca_biplot()
Usage
fviz_ca_row(
X,
axes = c(1, 2),
geom = c("point", "text"),
geom.row = geom,
shape.row = 19,
col.row = "blue",
alpha.row = 1,
col.row.sup = "darkblue",
select.row = list(name = NULL, cos2 = NULL, contrib = NULL),
map = "symmetric",
repel = FALSE,
...
)
fviz_ca_col(
X,
axes = c(1, 2),
shape.col = 17,
geom = c("point", "text"),
geom.col = geom,
col.col = "red",
col.col.sup = "darkred",
alpha.col = 1,
select.col = list(name = NULL, cos2 = NULL, contrib = NULL),
map = "symmetric",
repel = FALSE,
...
)
fviz_ca_biplot(
X,
axes = c(1, 2),
geom = c("point", "text"),
geom.row = geom,
geom.col = geom,
label = "all",
invisible = "none",
arrows = c(FALSE, FALSE),
repel = FALSE,
title = "CA  Biplot",
...
)
fviz_ca(X, ...)
Arguments
X 
an object of class CA [FactoMineR], ca [ca], coa [ade4]; correspondence [MASS] and expOutput/epCA [ExPosition]. 
axes 
a numeric vector of length 2 specifying the dimensions to be plotted. 
geom 
a character specifying the geometry to be used for the graph. Allowed values are the combination of c("point", "arrow", "text"). Use "point" (to show only points); "text" to show only labels; c("point", "text") or c("arrow", "text") to show both types. 
geom.row , geom.col 
as 
shape.row , shape.col 
the point shapes to be used for row/column variables. Default values are 19 for rows and 17 for columns. 
map 
character string specifying the map type. Allowed options include: "symmetric", "rowprincipal", "colprincipal", "symbiplot", "rowgab", "colgab", "rowgreen" and "colgreen". See details 
repel 
a boolean, whether to use ggrepel to avoid overplotting text labels or not. 
... 
Additional arguments.

col.col , col.row 
color for column/row points. The default values are "red" and "blue", respectively. Can be a continuous variable or a factor variable. Allowed values include also : "cos2", "contrib", "coord", "x" or "y". In this case, the colors for row/column variables are automatically controlled by their qualities ("cos2"), contributions ("contrib"), coordinates (x^2 + y^2, "coord"), x values("x") or y values("y") 
col.col.sup , col.row.sup 
colors for the supplementary column and row points, respectively. 
alpha.col , alpha.row 
controls the transparency of colors. The value can variate from 0 (total transparency) to 1 (no transparency). Default value is 1. Allowed values include also : "cos2", "contrib", "coord", "x" or "y" as for the arguments col.col and col.row. 
select.col , select.row 
a selection of columns/rows to be drawn. Allowed values are NULL or a list containing the arguments name, cos2 or contrib:

label 
a character vector specifying the elements to be labelled. Default value is "all". Allowed values are "none" or the combination of c("row", "row.sup", "col", "col.sup"). Use "col" to label only active column variables; "col.sup" to label only supplementary columns; etc 
invisible 
a character value specifying the elements to be hidden on the plot. Default value is "none". Allowed values are the combination of c("row", "row.sup","col", "col.sup"). 
arrows 
Vector of two logicals specifying if the plot should contain points (FALSE, default) or arrows (TRUE). First value sets the rows and the second value sets the columns. 
title 
the title of the graph 
Details
The default plot of (M)CA is a "symmetric" plot in which both rows and columns are in principal coordinates. In this situation, it's not possible to interpret the distance between row points and column points. To overcome this problem, the simplest way is to make an asymmetric plot. This means that, the column profiles must be presented in row space or viceversa. The allowed options for the argument map are:

"rowprincipal" or "colprincipal": asymmetric plots with either rows in principal coordinates and columns in standard coordinates, or vice versa. These plots preserve row metric or column metric respectively.

"symbiplot": Both rows and columns are scaled to have variances equal to the singular values (square roots of eigenvalues), which gives a symmetric biplot but does not preserve row or column metrics.
"rowgab" or "colgab": Asymmetric maps, proposed by Gabriel & Odoroff (1990), with rows (respectively, columns) in principal coordinates and columns (respectively, rows) in standard coordinates multiplied by the mass of the corresponding point.
"rowgreen" or "colgreen": The socalled contribution biplots showing visually the most contributing points (Greenacre 2006b). These are similar to "rowgab" and "colgab" except that the points in standard coordinates are multiplied by the square root of the corresponding masses, giving reconstructions of the standardized residuals.
Value
a ggplot
Author(s)
Alboukadel Kassambara alboukadel.kassambara@gmail.com
References
http://www.sthda.com
See Also
Examples
# Correspondence Analysis
# ++++++++++++++++++++++++++++++
# Install and load FactoMineR to compute CA
# install.packages("FactoMineR")
library("FactoMineR")
data(housetasks)
head(housetasks)
res.ca < CA(housetasks, graph=FALSE)
# Biplot of rows and columns
# ++++++++++++++++++++++++++
# Symetric Biplot of rows and columns
fviz_ca_biplot(res.ca)
# Asymetric biplot, use arrows for columns
fviz_ca_biplot(res.ca, map ="rowprincipal",
arrow = c(FALSE, TRUE))
# Keep only the labels for row points
fviz_ca_biplot(res.ca, label ="row")
# Keep only labels for column points
fviz_ca_biplot(res.ca, label ="col")
# Select the top 7 contributing rows
# And the top 3 columns
fviz_ca_biplot(res.ca,
select.row = list(contrib = 7),
select.col = list(contrib = 3))
# Graph of row variables
# +++++++++++++++++++++
# Control automatically the color of row points
# using the "cos2" or the contributions "contrib"
# cos2 = the quality of the rows on the factor map
# Change gradient color
# Use repel = TRUE to avoid overplotting (slow if many points)
fviz_ca_row(res.ca, col.row = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE)
# You can also control the transparency
# of the color by the "cos2" or "contrib"
fviz_ca_row(res.ca, alpha.row="contrib")
# Select and visualize some rows with select.row argument.
#  Rows with cos2 >= 0.5: select.row = list(cos2 = 0.5)
#  Top 7 rows according to the cos2: select.row = list(cos2 = 7)
#  Top 7 contributing rows: select.row = list(contrib = 7)
#  Select rows by names: select.row = list(name = c("Breakfeast", "Repairs", "Holidays"))
# Example: Select the top 7 contributing rows
fviz_ca_row(res.ca, select.row = list(contrib = 7))
# Graph of column points
# ++++++++++++++++++++++++++++
# Control colors using their contributions
fviz_ca_col(res.ca, col.col = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"))
# Select columns with select.col argument
# You can select by contrib, cos2 and name
# as previously described for ind
# Select the top 3 contributing columns
fviz_ca_col(res.ca, select.col = list(contrib = 3))