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R factoextra package for cluster visualization. Use for visualizing clustering results and PCA.
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Basado en la clasificación ocupacional SOC
R language data analysis and visualization skill. Use when user asks to (1) run R scripts or code, (2) install/update R packages, (3) perform data analysis with R, (4) create visualizations with ggplot2/plotly, (5) statistical analysis, (6) data manipulation with tidyverse/dplyr/data.table. Triggers on keywords like "R语言", "R脚本", "ggplot", "tidyverse", "数据分析", "可视化".
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| name | factoextra |
| description | R factoextra package for cluster visualization. Use for visualizing clustering results and PCA. |
Extract and visualize multivariate analyses.
library(factoextra)
# Elbow method
fviz_nbclust(data, kmeans, method = "wss")
# Silhouette method
fviz_nbclust(data, kmeans, method = "silhouette")
# Gap statistic
fviz_nbclust(data, kmeans, method = "gap_stat")
# Perform k-means
km <- kmeans(data, centers = 3)
# Visualize clusters
fviz_cluster(km, data = data)
# With options
fviz_cluster(km, data = data,
palette = "jco",
ellipse.type = "convex",
repel = TRUE,
ggtheme = theme_minimal())
# Compute distances
d <- dist(data)
# Hierarchical clustering
hc <- hclust(d, method = "ward.D2")
# Dendrogram
fviz_dend(hc, k = 3,
cex = 0.5,
color_labels_by_k = TRUE,
rect = TRUE)
# Circular dendrogram
fviz_dend(hc, k = 3, type = "circular")
# Phylogenic
fviz_dend(hc, k = 3, type = "phylogenic")
# Silhouette
sil <- silhouette(km$cluster, dist(data))
# Visualize
fviz_silhouette(sil)
# PCA
pca <- prcomp(data, scale = TRUE)
# Scree plot
fviz_eig(pca)
# Individuals
fviz_pca_ind(pca,
col.ind = "cos2",
gradient.cols = c("blue", "yellow", "red"))
# Variables
fviz_pca_var(pca,
col.var = "contrib",
gradient.cols = c("blue", "yellow", "red"))
# Biplot
fviz_pca_biplot(pca)
# Variable contributions
fviz_contrib(pca, choice = "var", axes = 1)
# Individual contributions
fviz_contrib(pca, choice = "ind", axes = 1:2)
# PAM
pam_result <- pam(data, k = 3)
fviz_cluster(pam_result)
# CLARA
clara_result <- clara(data, k = 3)
fviz_cluster(clara_result)
# FANNY
fanny_result <- fanny(data, k = 3)
fviz_cluster(fanny_result)