fviz_contrib - Quick visualization of row/column contributions - R software and data mining


Description

This function can be used to visualize the contributions of rows/columns from the results of Principal Component Analysis (PCA), Correspondence Analysis (CA) and Multiple Correspondence Analysis (MCA) functions.

The function fviz_contrib() [in factoextra package] is used.

Install and load factoextra

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

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

Load factoextra :

library("factoextra")

Usage

fviz_contrib(X, choice = c("row", "col", "var", "ind"), 
             axes = 1, fill = "steelblue", color = "steelblue",
             sort.val = c("desc", "asc", "none"), top = Inf)

Arguments

Argument Description
X an object of class PCA, CA and MCA [FactoMineR]; prcomp and princomp [stats]; dudi, pca, coa and acm [ade4]; ca [ca package].
choice allowed values are “row” and “col” for CA; “var” and “ind” for PCA or MCA.
axes a numeric vector specifying the dimension(s) of interest.
fill a fill color for the bar plot.
color an outline color for the bar plot.
sort.val a string specifying whether the value should be sorted. Allowed values are “none” (no sorting), “asc” (for ascending) or “desc” (for descending).
top a numeric value specifying the number of top elements to be shown.
… not used.

Details

The function fviz_contrib() creates a barplot of row/column contributions. A reference dashed line is also shown on the barplot. This reference line corresponds to the expected value if the contribution where uniform.

For a given dimension, any row/column with a contribution above the reference line could be considered as important in contributing to the dimension.

Value

A ggplot2 plot

Examples

Principal component analysis

A principal component analysis (PCA) is performed using the built-in R function prcomp() and the decathlon2 [in factoextra] data

data(decathlon2)
decathlon2.active <- decathlon2[1:23, 1:10]
res.pca <- prcomp(decathlon2.active,  scale = TRUE)
# variable contributions on axis 1
fviz_contrib(res.pca, choice="var", axes = 1 )

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# sorting
fviz_contrib(res.pca, choice="var", axes = 1,
           sort.val ="asc")

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# select the top 7 contributing variables
fviz_contrib(res.pca, choice="var", axes = 1, top = 7 )

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Change theme and color
fviz_contrib(res.pca, choice="var", axes = 1,
         fill = "lightgray", color = "black") +
         theme_minimal() +
         theme(axis.text.x = element_text(angle=45))

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Variable contributions on axis 2
fviz_contrib(res.pca, choice="var", axes = 2)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Variable contributions on axes 1 + 2
fviz_contrib(res.pca, choice="var", axes = 1:2)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Contributions of individuals on axis 1
fviz_contrib(res.pca, choice="ind", axes = 1)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

Correspondence Analysis

The function CA() in FactoMineR package is used:

# Install and load FactoMineR to compute CA
# install.packages("FactoMineR")
library("FactoMineR")
data("housetasks")
res.ca <- CA(housetasks, graph = FALSE)
# Visualize row contributions on axes 1
fviz_contrib(res.ca, choice ="row", axes = 1)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Visualize row contributions on axes 1 + 2
fviz_contrib(res.ca, choice ="row", axes = 1:2)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Visualize column contributions on axes 1
fviz_contrib(res.ca, choice ="col", axes = 1)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

Multiple Correspondence Analysis

The function MCA() in FactoMineR package is used:

library(FactoMineR)
data(poison)
res.mca <- MCA(poison, quanti.sup = 1:2,
              quali.sup = 3:4, graph=FALSE)
# Visualize individual contributions on axes 1
fviz_contrib(res.mca, choice ="ind", axes = 1)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Select the top 20
fviz_contrib(res.mca, choice ="ind", axes = 1, top = 20)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

# Visualize variable categorie contributions on axes 1
fviz_contrib(res.mca, choice ="var", axes = 1)

fviz_contrib - Quick visualization of row/column contributions - R software and data mining

Infos

This analysis has been performed using R software (ver. 3.1.2) and factoextra (ver. 1.0.2)


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