factoextra is an R package making easy to extract and visualize the output of exploratory multivariate data analyses, including:
Principal Component Analysis (PCA), which is used to summarize the information contained in a continuous (i.e, quantitative) multivariate data by reducing the dimensionality of the data without loosing important information.
Correspondence Analysis (CA), which is an extension of the principal component analysis suited to analyse a large contingency table formed by two qualitative variables (or categorical data).
Multiple Correspondence Analysis (MCA), which is an adaptation of CA to a data table containing more than two categorical variables.
Multiple Factor Analysis (MFA) dedicated to datasets where variables are organized into groups (qualitative and/or quantitative variables).
Hierarchical Multiple Factor Analysis (HMFA): An extension of MFA in a situation where the data are organized into a hierarchical structure.
Factor Analysis of Mixed Data (FAMD), a particular case of the MFA, dedicated to analyze a data set containing both quantitative and qualitative variables.
There are a number of R packages implementing principal component methods. These packages include: FactoMineR, ade4, stats, ca, MASS and ExPosition.
However, the result is presented differently according to the used packages. To help in the interpretation and in the visualization of multivariate analysis - such as cluster analysis and dimensionality reduction analysis - we developed an easy-to-use R package named factoextra.
We'll use i) the FactoMineR package (Sebastien Le, et al., 2008) to compute PCA, (M)CA, FAMD, MFA and HCPC; ii) and the factoextra package for extracting and visualizing the results.
The figure below shows methods, which outputs can be visualized using the factoextra package. The official online documentation is available at: http://www.sthda.com/english/rpkgs/factoextra.
The factoextra R package can handle the results of PCA, CA, MCA, MFA, FAMD and HMFA from several packages, for extracting and visualizing the most important information contained in your data.
After PCA, CA, MCA, MFA, FAMD and HMFA, the most important row/column elements can be highlighted using :
their cos2 values corresponding to their quality of representation on the factor map
If you want to do this, the factoextra package provides a convenient solution.
If you want to make predictions with PCA/MCA and to visualize the position of the supplementary variables/individuals on the factor map using ggplot2: then factoextra can help you. It's quick, write less and do more...
No matter the package you decided to use, factoextra can give you a human understandable output.
The FactoMineR package can be installed and loaded as follow:
# Install
install.packages("FactoMineR")
# Load
library("FactoMineR")
install.packages("factoextra")
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/factoextra")
library("factoextra")
#> Loading required package: ggplot2
#> Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
See the online documentation (http://www.sthda.com/english/rpkgs/factoextra) for a complete list.
As depicted in the figure below, the type of analysis to be performed depends on the data set formats and structures.
In this section we start by illustrating classical methods - such as PCA, CA and MCA - for analyzing a data set containing continuous variables, contingency table and qualitative variables, respectively.
We continue by discussing advanced methods - such as FAMD, MFA and HMFA - for analyzing a data set containing a mix of variables (qualitatives & quantitatives) organized or not into groups.
Finally, we show how to perform hierarchical clustering on principal components (HCPC), which useful for performing clustering with a data set containing only qualitative variables or with a mixed data of qualitative and quantitative variables.
Read more about computing and interpreting principal component analysis at: Principal Component Analysis (PCA).
library("factoextra")
data("decathlon2")
df <- decathlon2[1:23, 1:10]
library("FactoMineR")
res.pca <- PCA(df, graph = FALSE)
# Extract eigenvalues/variances
get_eig(res.pca)
#> eigenvalue variance.percent cumulative.variance.percent
#> Dim.1 4.1242133 41.242133 41.24213
#> Dim.2 1.8385309 18.385309 59.62744
#> Dim.3 1.2391403 12.391403 72.01885
#> Dim.4 0.8194402 8.194402 80.21325
#> Dim.5 0.7015528 7.015528 87.22878
#> Dim.6 0.4228828 4.228828 91.45760
#> Dim.7 0.3025817 3.025817 94.48342
#> Dim.8 0.2744700 2.744700 97.22812
#> Dim.9 0.1552169 1.552169 98.78029
#> Dim.10 0.1219710 1.219710 100.00000
# Visualize eigenvalues/variances
fviz_screeplot(res.pca, addlabels = TRUE, ylim = c(0, 50))
4.Extract and visualize results for variables:
# Extract the results for variables
var <- get_pca_var(res.pca)
var
#> Principal Component Analysis Results for variables
#> ===================================================
#> Name Description
#> 1 "$coord" "Coordinates for the variables"
#> 2 "$cor" "Correlations between variables and dimensions"
#> 3 "$cos2" "Cos2 for the variables"
#> 4 "$contrib" "contributions of the variables"
# Coordinates of variables
head(var$coord)
#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
#> X100m -0.8506257 -0.17939806 0.3015564 0.03357320 -0.1944440
#> Long.jump 0.7941806 0.28085695 -0.1905465 -0.11538956 0.2331567
#> Shot.put 0.7339127 0.08540412 0.5175978 0.12846837 -0.2488129
#> High.jump 0.6100840 -0.46521415 0.3300852 0.14455012 0.4027002
#> X400m -0.7016034 0.29017826 0.2835329 0.43082552 0.1039085
#> X110m.hurdle -0.7641252 -0.02474081 0.4488873 -0.01689589 0.2242200
# Contribution of variables
head(var$contrib)
#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
#> X100m 17.544293 1.7505098 7.338659 0.13755240 5.389252
#> Long.jump 15.293168 4.2904162 2.930094 1.62485936 7.748815
#> Shot.put 13.060137 0.3967224 21.620432 2.01407269 8.824401
#> High.jump 9.024811 11.7715838 8.792888 2.54987951 23.115504
#> X400m 11.935544 4.5799296 6.487636 22.65090599 1.539012
#> X110m.hurdle 14.157544 0.0332933 16.261261 0.03483735 7.166193
# Graph of variables: default plot
fviz_pca_var(res.pca, col.var = "black")
It's possible to control variable colors using their contributions ("contrib") to the principal axes:
# Control variable colors using their contributions
fviz_pca_var(res.pca, col.var="contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Avoid text overlapping
)
# Contributions of variables to PC1
fviz_contrib(res.pca, choice = "var", axes = 1, top = 10)
# Contributions of variables to PC2
fviz_contrib(res.pca, choice = "var", axes = 2, top = 10)
# Extract the results for individuals
ind <- get_pca_ind(res.pca)
ind
#> Principal Component Analysis Results for individuals
#> ===================================================
#> Name Description
#> 1 "$coord" "Coordinates for the individuals"
#> 2 "$cos2" "Cos2 for the individuals"
#> 3 "$contrib" "contributions of the individuals"
# Coordinates of individuals
head(ind$coord)
#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
#> SEBRLE 0.1955047 1.5890567 0.6424912 0.08389652 1.16829387
#> CLAY 0.8078795 2.4748137 -1.3873827 1.29838232 -0.82498206
#> BERNARD -1.3591340 1.6480950 0.2005584 -1.96409420 0.08419345
#> YURKOV -0.8889532 -0.4426067 2.5295843 0.71290837 0.40782264
#> ZSIVOCZKY -0.1081216 -2.0688377 -1.3342591 -0.10152796 -0.20145217
#> McMULLEN 0.1212195 -1.0139102 -0.8625170 1.34164291 1.62151286
# Graph of individuals
# 1. Use repel = TRUE to avoid overplotting
# 2. Control automatically the color of individuals using the cos2
# cos2 = the quality of the individuals on the factor map
# Use points only
# 3. Use gradient color
fviz_pca_ind(res.pca, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Avoid text overlapping (slow if many points)
)
# Biplot of individuals and variables
fviz_pca_biplot(res.pca, repel = TRUE)
# Compute PCA on the iris data set
# The variable Species (index = 5) is removed
# before PCA analysis
iris.pca <- PCA(iris[,-5], graph = FALSE)
# Visualize
# Use habillage to specify groups for coloring
fviz_pca_ind(iris.pca,
label = "none", # hide individual labels
habillage = iris$Species, # color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
addEllipses = TRUE # Concentration ellipses
)
Read more about computing and interpreting correspondence analysis at: Correspondence Analysis (CA).
# Loading data
data("housetasks")
# Computing CA
library("FactoMineR")
res.ca <- CA(housetasks, graph = FALSE)
# Result for row variables
get_ca_row(res.ca)
# Result for column variables
get_ca_col(res.ca)
fviz_ca_biplot(res.ca, repel = TRUE)
To visualize only row points or column points, type this:
# Graph of row points
fviz_ca_row(res.ca, repel = TRUE)
# Graph of column points
fviz_ca_col(res.ca)
# Visualize row contributions on axes 1
fviz_contrib(res.ca, choice ="row", axes = 1)
# Visualize column contributions on axes 1
fviz_contrib(res.ca, choice ="col", axes = 1)
Read more about computing and interpreting multiple correspondence analysis at: Multiple Correspondence Analysis (MCA).
library(FactoMineR)
data(poison)
res.mca <- MCA(poison, quanti.sup = 1:2,
quali.sup = 3:4, graph=FALSE)
# Extract the results for variable categories
get_mca_var(res.mca)
# Extract the results for individuals
get_mca_ind(res.mca)
# Visualize variable categorie contributions on axes 1
fviz_contrib(res.mca, choice ="var", axes = 1)
# Visualize individual contributions on axes 1
# select the top 20
fviz_contrib(res.mca, choice ="ind", axes = 1, top = 20)
# Color by groups
# Add concentration ellipses
# Use repel = TRUE to avoid overplotting
grp <- as.factor(poison[, "Vomiting"])
fviz_mca_ind(res.mca, habillage = grp,
addEllipses = TRUE, repel = TRUE)
fviz_mca_var(res.mca, repel = TRUE)
fviz_mca_biplot(res.mca, repel = TRUE)
The factoextra R package has also functions that support the visualization of advanced methods such:
To learn more about cluster analysis, you can refer to the book available at: Practical Guide to Cluster Analysis in R
The main parts of the book include:
The book presents the basic principles of these tasks and provide many examples in R. It offers solid guidance in data mining for students and researchers.
# 1. Loading and preparing data
data("USArrests")
df <- scale(USArrests)
# 2. Compute k-means
set.seed(123)
km.res <- kmeans(scale(USArrests), 4, nstart = 25)
# 3. Visualize
library("factoextra")
fviz_cluster(km.res, data = df,
palette = c("#00AFBB","#2E9FDF", "#E7B800", "#FC4E07"),
ggtheme = theme_minimal(),
main = "Partitioning Clustering Plot"
)
Read more:
library("factoextra")
# Compute hierarchical clustering and cut into 4 clusters
res <- hcut(USArrests, k = 4, stand = TRUE)
# Visualize
fviz_dend(res, rect = TRUE, cex = 0.5,
k_colors = c("#00AFBB","#2E9FDF", "#E7B800", "#FC4E07"))
Read more:
# Optimal number of clusters for k-means
library("factoextra")
my_data <- scale(USArrests)
fviz_nbclust(my_data, kmeans, method = "gap_stat")
I would like to thank Fabian Mundt for his active contributions to factoextra.
We sincerely thank all developers for their efforts behind the packages that factoextra depends on, namely, ggplot2 (Hadley Wickham, Springer-Verlag New York, 2009), FactoMineR (Sebastien Le et al., Journal of Statistical Software, 2008), dendextend (Tal Galili, Bioinformatics, 2015), cluster (Martin Maechler et al., 2016) and more .....
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