fviz_ca: Quick Correspondence Analysis data visualization using factoextra - R software and data mining


Description

Graph of column/row variables from the output of Correspondence Analysis (CA).

The following functions, from factoextra package are use:

  • 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()

These functions are included in factoextra package.

Install and load factoextra

The package devtools is required for the installation as factoextra is hosted on github.

# install.packages("devtools")
library("devtools")
install_github("kassambara/factoextra")

Load factoextra :

library("factoextra")

Usage

# Graph of row variables
fviz_ca_row(X, axes = c(1, 2), shape.row = 19,
  geom = c("point", "text"), label = "all", 
  invisible = "none", labelsize = 4, pointsize = 2,
  col.row = "blue", col.row.sup = "darkblue", alpha.row = 1,
  select.row = list(name = NULL, cos2 = NULL, contrib = NULL),
  map = "symmetric",
  jitter = list(what = "label", width = NULL, height = NULL), ...)
# Graph of column variables
fviz_ca_col(X, axes = c(1, 2), shape.col = 17,
  geom = c("point", "text"), label = "all",
  invisible = "none", labelsize = 4, pointsize = 2,
  col.col = "red", col.col.sup = "darkred", alpha.col = 1,
  select.col = list(name = NULL, cos2 = NULL, contrib = NULL),
  map = "symmetric",
 jitter = list(what = "label", width = NULL, height = NULL), ...)
# Biplot of row and column  variables
fviz_ca_biplot(X, axes = c(1, 2), shape.row = 19, shape.col = 17,
  geom = c("point", "text"), label = "all", invisible = "none",
  labelsize = 4, pointsize = 2, col.col = "red",
  col.col.sup = "darkred", alpha.col = 1, col.row = "blue",
  col.row.sup = "darkblue", alpha.row = 1,
  select.col = list(name = NULL, cos2 = NULL, contrib = NULL),
  select.row = list(name = NULL, cos2 = NULL, contrib = NULL),
  map = "symmetric", arrows = c(FALSE, FALSE),
  jitter = list(what = "label", width = NULL, height = NULL), ...)
# An alias of fviz_ca_biplot()
fviz_ca(X, ...)

Arguments

Argument Description
X an object of class CA [FactoMineR], ca [ca], coa [ade4]; correspondence [MASS].
axes a numeric vector of length 2 specifying the dimensions to be plotted.
shape.row,shape.col the point shapes to be used for row/column variables. Default values are 19 for rows and 17 for columns.
geom a text 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.
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“).
labelsize font size for the labels.
pointsize the size of points.
map character string specifying the map type. Allowed options include: “symmetric”, “rowprincipal”, “colprincipal”, “symbiplot”, “rowgab”, “colgab”, “rowgreen” and “colgreen”. See details
**jitter**a parameter used to jitter the points in order to reduce overplotting. It's a list containing the objects *what, width and height* (Ex.; jitter = list(what, width, height)). **what**: the element to be jittered. Possible values are "point" or "p"; "label" or "l"; "both" or "b". **width**: degree of jitter in x direction (ex: 0.2). **height**: degree of jitter in y direction (ex: 0.2).
col.col,col.row color for column/row points. The default values are “red” and “blue”, respectively. 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:

  • name: is a character vector containing columns/rows to be drawn
  • cos2: if cos2 is in [0, 1], ex: 0.6, then columns/rows with a cos2 > 0.6 are drawn. if cos2 > 1, ex: 5, then the top 5 columns/rows with the highest cos2 are drawn.
  • contrib: if contrib > 1, ex: 5, then the top 5 columns/rows with the highest cos2 are drawn
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.
Optional arguments.

Details

The default plot of 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 vice-versa. 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 so-called 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 ggplot2 plot

Examples

Correspondence Analysis

Correspondence Analysis (CA) is performed using the function CA() [in FactoMineR] and housetasks data [in factoextra]:

# Install and load FactoMineR to compute CA
# install.packages("FactoMineR")
library("FactoMineR")
data(housetasks)
head(housetasks)
           Wife Alternating Husband Jointly
Laundry     156          14       2       4
Main_meal   124          20       5       4
Dinner       77          11       7      13
Breakfeast   82          36      15       7
Tidying      53          11       1      57
Dishes       32          24       4      53
res.ca <- CA(housetasks, graph=FALSE)

fviz_ca_row(): Graph of row variables

# Default plot
fviz_ca_row(res.ca)

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Change title and axis labels
fviz_ca_row(res.ca) +
 labs(title = "CA", x = "Dim.1", y ="Dim.2" )

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Change axis limits by specifying the min and max
fviz_ca_row(res.ca) +
   xlim(-1.3, 1.7) + ylim (-1.5, 1)

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Use text only
fviz_ca_row(res.ca, geom = "text")

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Use points only
fviz_ca_row(res.ca, geom="point")

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Change the size of points
fviz_ca_row(res.ca, geom="point", pointsize = 4)

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Change point color and theme
fviz_ca_row(res.ca, col.row = "violet")+
   theme_minimal()

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Control automatically the color of row points
# using the cos2 or the contributions
# cos2 = the quality of the rows on the factor map
fviz_ca_row(res.ca, col.row="cos2")

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Gradient color
fviz_ca_row(res.ca, col.row="cos2") +
      scale_color_gradient2(low="white", mid="blue",
      high="red", midpoint=0.5)

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Change the theme and use only points
fviz_ca_row(res.ca, col.row="cos2", geom = "point") +
      scale_color_gradient2(low="white", mid="blue",
      high="red", midpoint=0.4)+ theme_minimal()

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Color by the contributions
fviz_ca_row(res.ca, col.row="contrib") +
      scale_color_gradient2(low="white", mid="blue",
      high="red", midpoint=10)

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Control the transparency of the color by the
# contributions
fviz_ca_row(res.ca, alpha.row="contrib") +
     theme_minimal()

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Select and visualize rows with cos2 > 0.5
fviz_ca_row(res.ca, select.row = list(cos2 = 0.5))

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Select the top 7 according to the cos2
fviz_ca_row(res.ca, select.row = list(cos2 = 7))

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Select the top 7 contributing rows
fviz_ca_row(res.ca, select.row = list(contrib = 7))

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Select by names
fviz_ca_row(res.ca,
select.row = list(name = c("Breakfeast", "Repairs", "Holidays")))

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

fviz_ca_col(): Graph of column categories

# Default plot
fviz_ca_col(res.ca)

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Change color and theme
fviz_ca_col(res.ca, col.col="steelblue")+
 theme_minimal()

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Control colors using their contributions
fviz_ca_col(res.ca, col.col = "contrib")+
 scale_color_gradient2(low = "white", mid = "blue",
           high = "red", midpoint = 25) +
 theme_minimal()

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Control the transparency of variables using their contributions
fviz_ca_col(res.ca, alpha.col = "contrib") +
   theme_minimal()

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Select and visualize columns with cos2 >= 0.4
fviz_ca_col(res.ca, select.col = list(cos2 = 0.4))

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Select the top 3 contributing columns
fviz_ca_col(res.ca, select.col = list(contrib = 3))

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Select by names
fviz_ca_col(res.ca,
 select.col= list(name = c("Wife", "Husband", "Jointly")))

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

fviz_ca_biplot(): Biplot of rows and columns

# Symetric Biplot of rows and columns
fviz_ca_biplot(res.ca)

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Asymetric biplot, use arrows for columns
fviz_ca_biplot(res.ca, map ="rowprincipal",
 arrow = c(FALSE, TRUE))

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Keep only the labels for row points
fviz_ca_biplot(res.ca, label ="row")

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Keep only labels for column points
fviz_ca_biplot(res.ca, label ="col")

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Hide row points
fviz_ca_biplot(res.ca, invisible ="row")

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Hide column points
fviz_ca_biplot(res.ca, invisible ="col")

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# Control automatically the color of rows using the cos2
fviz_ca_biplot(res.ca, col.row="cos2") +
       theme_minimal()

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

# 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))

Quick Correspondence Analysis data visualization using factoextra - R software and data mining

Infos

This analysis has been performed using R software (ver. 3.2.1) and factoextra (ver. 1.0.3)


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