### Install factoextra from GitHub

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Install the latest version of factoextra directly from GitHub. Ensure the 'remotes' package is installed first.

```r
if(!require(remotes)) install.packages("remotes")
remotes::install_github("kassambara/factoextra")
```

--------------------------------

### Install factoextra from CRAN

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Install the factoextra package from CRAN using this command.

```r
install.packages("factoextra")
```

--------------------------------

### Install FactoMineR Package

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Use this command to install the FactoMineR package from CRAN.

```r
# Install
install.packages("FactoMineR")
```

--------------------------------

### Load factoextra Library

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Load the installed factoextra package into your R session.

```r
library("factoextra")
```

--------------------------------

### Load factoextra and decathlon2 dataset

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Loads the factoextra package and the decathlon2 dataset for PCA analysis. Ensure the factoextra package is installed.

```r
library("factoextra")
#> Loading required package: ggplot2
#> Welcome to factoextra!
#> Want to learn more? See two factoextra-related books at https://www.datanovia.com/en/product/practical-guide-to-principal-component-methods-in-r/
data("decathlon2")
df <- decathlon2[1:23, 1:10]
```

--------------------------------

### Load FactoMineR Library

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Load the installed FactoMineR package into your R session.

```r
# Load
library("FactoMineR")
```

--------------------------------

### MCA with Supplementary Quantitative Variables

Source: https://github.com/kassambara/factoextra/blob/master/README.md

This example shows how to perform a Multiple Correspondence Analysis (MCA) using FactoMineR's MCA function, specifying quantitative supplementary variables. It then extracts these supplementary variables using get_mca.

```r
# FactoMineR MCA quantitative supplementary variables are supported directly
library(FactoMineR)
data(poison)
res.mca <- MCA(poison, quanti.sup = 1:2, graph = FALSE)
get_mca(res.mca, "quanti.sup")
```

--------------------------------

### Phylogenic Dendrogram Visualization

Source: https://github.com/kassambara/factoextra/blob/master/README.md

This snippet visualizes a hierarchical clustering result as a phylogenic tree using fviz_dend with a specified layout. It requires the igraph package to be installed.

```r
if (requireNamespace("igraph", quietly = TRUE)) {
  # The default compatibility alias "layout.auto" still works, and
  # modern igraph layout names are accepted as well.
  fviz_dend(res, type = "phylogenic", phylo_layout = "layout_nicely",
            show_labels = FALSE)
}
```

--------------------------------

### Partitioning Clustering with K-means

Source: https://github.com/kassambara/factoextra/blob/master/README.md

This snippet demonstrates how to perform k-means clustering on scaled data and visualize the clusters using factoextra's fviz_cluster function. Ensure the factoextra package is loaded.

```r
library("factoextra")
# 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"
             )
```

--------------------------------

### Visualize PCA variables by contribution

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Visualizes PCA variables, coloring them by their contribution to the principal axes using a gradient. Uses repel=TRUE to prevent text overlap.

```r
# 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
             )
```

--------------------------------

### Compute and Visualize Elbow Method for Clustering

Source: https://github.com/kassambara/factoextra/blob/master/README.md

This snippet demonstrates computing the Within-Sum of Squares (WSS) for different numbers of clusters (k) using the 'hcut' method on scaled iris data. It visualizes the results using fviz_nbclust, showing the k=1 baseline internally.

```r
data(iris)
iris.scaled <- scale(iris[, -5])
res.pca <- prcomp(iris[, -5], scale = TRUE)

# WSS now computes the k = 1 baseline internally
fviz_nbclust(iris.scaled, hcut, method = "wss", hc_method = "complete")
```

--------------------------------

### Hierarchical Clustering Visualization

Source: https://github.com/kassambara/factoextra/blob/master/README.md

This snippet shows how to compute hierarchical clustering using hcut and visualize the dendrogram with factoextra's fviz_dend function. The data is scaled before clustering.

```r
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"))
```

--------------------------------

### Generate PCA Biplot

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Creates a biplot visualizing both individuals and variables on the principal component space. Use `repel = TRUE` to prevent text overlap.

```r
# Biplot of individuals and variables
fviz_pca_biplot(res.pca, repel = TRUE)
```

--------------------------------

### Visualize Eigenvalues using Parallel Analysis

Source: https://github.com/kassambara/factoextra/blob/master/README.md

This code snippet visualizes the eigenvalues from a PCA result using fviz_eig. It explicitly enables and configures parallel analysis for validation, ensuring reproducibility with a specified seed.

```r
# Parallel analysis validation is explicit and reproducible
fviz_eig(res.pca, choice = "eigenvalue", parallel = TRUE,
         parallel.iter = 10, parallel.seed = 123)
```

--------------------------------

### Generate Correspondence Analysis Biplot

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Visualizes both row and column points and their relationships in the correspondence analysis space. `repel = TRUE` helps to avoid overlapping labels.

```r
fviz_ca_biplot(res.ca, repel = TRUE)
```

--------------------------------

### Extract and visualize eigenvalues/variances

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Extracts eigenvalues and their percentage of variance from the PCA results using get_eig(). Visualizes these values using fviz_screeplot() to help determine the number of dimensions to retain.

```r
# 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))
```

--------------------------------

### Determine Optimal Clusters using Gap Statistic

Source: https://github.com/kassambara/factoextra/blob/master/README.md

This snippet uses the fviz_nbclust function from factoextra to determine the optimal number of clusters for k-means using the gap statistic method on scaled data.

```r
# Optimal number of clusters for k-means
library("factoextra")
my_data <- scale(USArrests)
fviz_nbclust(my_data, kmeans, method = "gap_stat")
```

--------------------------------

### Extract and visualize PCA results for variables

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Extracts variable-specific results like coordinates and contributions using get_pca_var(). Visualizes these using fviz_pca_var() for a default plot.

```r
# 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")
```

--------------------------------

### Color Individuals by Groups in PCA

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Visualizes PCA results with individuals colored according to their group membership. `habillage` specifies the grouping variable, and `addEllipses` draws concentration ellipses for each group.

```r
# 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
             )
```

--------------------------------

### Perform Principal Component Analysis

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Computes the PCA on the prepared data frame using the FactoMineR::PCA function. Set graph = FALSE to prevent automatic plotting.

```r
library("FactoMineR")
res.pca <- PCA(df,  graph = FALSE)
```

--------------------------------

### Graph of Variable Categories in MCA

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Generates a graph of variable categories from an MCA result. The `repel = TRUE` argument is used to avoid overplotting of category labels.

```r
fviz_mca_var(res.mca, repel = TRUE)
```

--------------------------------

### Extract and Visualize Individual Results

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Extracts and displays coordinates, cos2 values, and contributions for individuals from PCA results. Useful for understanding individual positions and their quality on the factor map.

```r
# 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)
             )
```

--------------------------------

### Visualize Row or Column Points in CA

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Generates separate plots for row points or column points in correspondence analysis. Use `fviz_ca_row` for rows and `fviz_ca_col` for columns.

```r
# Graph of row points
fviz_ca_row(res.ca, repel = TRUE)

# Graph of column points
fviz_ca_col(res.ca)
```

--------------------------------

### Biplot of Individuals and Variables in MCA

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Generates a biplot that displays both individuals and variables from an MCA result. The `repel = TRUE` argument helps in avoiding label overlaps.

```r
fviz_mca_biplot(res.mca, repel = TRUE)
```

--------------------------------

### Visualize Contributions of Variables and Individuals

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Visualizes the contribution of variable categories and individuals to the principal axes of an MCA. Allows specifying the choice of elements ('var' or 'ind') and the axes to plot.

```r
# 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)
```

--------------------------------

### Compute Correspondence Analysis

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Performs correspondence analysis on a contingency table. The `CA` function from FactoMineR is used, and results can be visualized with factoextra.

```r
# Loading data
data("housetasks")

# Computing CA
library("FactoMineR")
res.ca <- CA(housetasks, graph = FALSE)
```

--------------------------------

### Graph of Individuals in MCA

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Generates a graph of individuals from an MCA result. This function can color individuals by groups, add concentration ellipses, and use text repulsion to avoid overplotting.

```r
# 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)
```

--------------------------------

### Compute MCA using FactoMineR

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Computes the Multiple Correspondence Analysis using the MCA function from the FactoMineR package. Requires the 'poison' dataset and specifies supplementary quantitative and qualitative variables.

```r
library(FactoMineR)
data(poison)
res.mca <- MCA(poison, quanti.sup = 1:2,
              quali.sup = 3:4, graph=FALSE)
```

--------------------------------

### Visualize CA Contributions

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Plots the contributions of row or column variables to a specified axis in correspondence analysis. Use `choice = "row"` or `choice = "col"` to specify the variable type.

```r
# 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)
```

--------------------------------

### Extract Correspondence Analysis Results

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Retrieves the results for row and column variables from a correspondence analysis object. Use `get_ca_row` for row data and `get_ca_col` for column data.

```r
# Result for row variables
get_ca_row(res.ca)
  
# Result for column variables
get_ca_col(res.ca)
```

--------------------------------

### Visualize Variable Contributions to Principal Axes

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Plots the top contributing variables to a specified principal axis. Use this to identify variables that most influence the principal components.

```r
# 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 MCA Results for Variables and Individuals

Source: https://github.com/kassambara/factoextra/blob/master/README.md

Extracts the results for variable categories and individuals from a computed MCA object. These functions are useful for further analysis and visualization.

```r
# Extract the results for variable categories
get_mca_var(res.mca)

# Extract the results for individuals
get_mca_ind(res.mca)
```

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