factoextra 1.0.5

Bug fixes

Minor changes

  • New argument mean.point in the function fviz(). logical value. If TRUE, group mean points are added to the plot.
  • Now, PCA correlation circles have fixed coordinates so they don’t appear as ellipses (@scoavoux, #38.
  • New argument fill.ind and fill.var added in fviz_pca() (@ginolhac, #27 and @Confurious, #42).
  • New arguments geom.ind and geom.var in fviz_pca_xxx() and fviz_mca_xxx() functions to have more controls on the individuals/variables geometry in the functions fviz_pca_biplot() and fviz_mca_biplot() (@Confurious, #42).
  • New arguments geom.row and geom.col in fviz_ca_xxx() functions to have more controls on the individuals/variables geometry in the function fviz_ca_biplot() (@Confurious, #42).
  • New argument gradient.cols in fviz_pca_biplot()
  • New argument àxes in fviz_cluster() to specify the dimension to plot.
  • New argument circlesize in the function fviz() to change the size of the variable correlation circle size.

  • It’s now possible to color individuals using a custom continuous variable (#29). This is done using the argument col.ind.

library(factoextra)
data(iris)
res.pca <- prcomp(iris[, -5],  scale = TRUE)

# Visualize and color by a custom continuous variable
fviz_pca_ind(res.pca, col.ind = iris$Sepal.Length,
             legend.title = "Sepal.Length")
  • factoextra can now handle Japanese characters by using the argument font.family = “HiraKakuProN-W3”` (#31). For example:
library(FactoMineR)
library(factoextra)

.tbl2.1 <- matrix(c(395, 2456,1758,
                    147, 153, 916, 
                    694, 327, 1347),byrow=T,3,3)
dimnames(.tbl2.1) <- list(地域=c("オスロ","中部地域","北部地域"),
                            犯罪=c("強盗", "詐欺","破壊") )


res.CA <- CA(.tbl2.1,graph=FALSE)

fviz_ca_biplot(res.CA,map="simbiplot",title="simbiplot",
               font.family = "HiraKakuProN-W3")

factoextra 1.0.4

New features

  • New function fviz_mclust() for plotting model-based clustering using ggplot2.

  • New function fviz(): Generic function to create a scatter plot of multivariate analyse outputs, including PCA, CA and MCA, MFA, …

  • New functions fviz_mfa_var() and fviz_hmfa_var() for plotting MFA and HMFA variables, respectively.

  • New function get_mfa_var(): Extract the results for variables (quantitatives, qualitatives and groups). Deprecated functions: get_mfa_var_quanti(), get_mfa_var_quali() and get_mfa_group().

  • New functions added for extracting and visualizing the results of FAMD (factor analysis of mixed data): get_famd_ind(), get_famd_var(), fviz_famd_ind() and fviz_famd_var().

  • Now fviz_dend() returns a ggplot. It can be used to plot circular dendrograms and phylogenic-like trees. Additionnally, it supports an object of class HCPC (from FactoMineR).

  • New arguments in fviz_cluster():
    • main, xlab, ylab in fviz_cluster(): to change the plot main title and axis labels.
    • ellipse, ellipse.type, ellipse.level and ellipse.alpha
    • choose.vars: a character vector containing variables to be considered for plotting.
  • New argument pointshape in fviz_pca(). When you use habillage, point shapes change automatically by groups. To avoid this behaviour use for example pointshape = 19 in combination with habillage (@raynamharris, #15).
  • New argument repel in fviz_add().
  • New argument gradient.cols in fviz_*() functions.

  • Support for the ExPosition package added (epCA, epPCA, epMCA) (#23)

Minor changing

  • Check point added in the function fviz_nbclust() to make sure that x is an object of class data.frame or matrix (Jakub Nowosad, #15).
  • The following arguments are deprecated in fviz_cluster(): title, frame, frame.type, frame.level, frame.alpha. Now, use main, ellipse, ellipse.type, ellipse.level and ellipse.alpha instead.

  • Now, by default, the function fviz_cluster() doesn’t show cluster mean points for an object of class PAM and CLARA, when the argument show.clust.cent is missing . This is because cluster centers are medoids in the case of PAM and CLARA but not means. However, user can force the function to display the mean points by using the argument show.clust.cent = TRUE.

  • The argument jitter is deprecated; use repel = TRUE instead, to avoid overlapping of labels.

  • New argument “sub” in fviz_dend() for adding a subtitle to the dendrogram. If NULL, the method used hierarchical clustering is shown. To remove the subtitle use sub = “”.

Bug fixes

factoextra 1.0.3

NEW FEATURES

  • New fviz_mfa function to plot MFA individuals, partial individuals, quantitive variables, categorical variables, groups relationship square and partial axes (@inventionate, #4).

  • New fviz_hmfa function to plot HMFA individuals, quantitive variables, categorical variables and groups relationship square (@inventionate, #4).

  • New get_mfa and get_hmfa function (@inventionate, #4).

  • fviz_ca, fviz_pca, fviz_mca, fviz_mfa and fviz_hmfa ggrepel support (@inventionate, #4).

  • Updated fviz_summarize, eigenvalue, fviz_contrib and fviz_cos2 functions, to compute FactoMineR MFA and HMFA results (@inventionate, #4).

  • fviz_cluster() added. This function can be used to visualize the outputs of clustering methods including: kmeans() [stats package]; pam(), clara(), fanny() [cluster package]; dbscan() [fpc package]; Mclust() [mclust package]; HCPC() [FactoMineR package]; hkmeans() [factoextra].

  • fviz_silhouette() added. Draws the result of cluster silhouette analyses computed using the function silhouette()[cluster package]

  • fviz_nbclust(): Dertemines and visualize the optimal number of clusters

  • fviz_gap_stat(): Visualize the gap statistic generated by the function clusGap() [in cluster package]

  • hcut(): Computes hierarchical clustering and cut the tree into k clusters.

  • hkmeans(): Hierarchical k-means clustering. Hybrid approach to avoid the initial random selection of cluster centers.

  • get_clust_tendency(): Assessing clustering tendency

  • fviz_dend(): Enhanced visualization of dendrogram

  • eclust(): Visual enhancement of clustering analysis

  • get_dist() and fviz_dist(): Enhanced Distance Matrix Computation and Visualization

  • eclust(): Visual enhancement of clustering analysis

MINOR CHANGING

  • Require R >= 3.1.0
  • A dataset named “multishapes” has been added. It contains clusters of multiple shapes. Useful for comparing density-based clustering and partitioning methods such as k-means
  • The argument jitter is added to the functions fviz_pca(), fviz_mca() and fviz_ca() and fviz_cluster() in order to reduce overplotting of points and texts
  • The functions fviz_*() now use ggplot2::stat_ellipse() for drawing ellipses.

BUG FIXES

  • Unknown parameters “shape” removed from geom_text (@bdboy, #5)

factoextra 1.0.2

NEW FEATURES

  • Visualization of Correspondence Analysis outputs from different R packages (FactoMineR, ca, ade4, MASS)
  • fviz_ca_row()
  • fviz_ca_col()
  • fviz_ca_biplot()

  • Extract results from CA output
  • get_ca_row()
  • get_ca_col()
  • get_ca()

  • Visualize the cos2 and the contributions of rows/columns. The functions can handle the output of PCA, CA and MCA
  • fviz_cos2()
  • fviz_contrib()

  • Sumarize the results of PCA, CA, MCA
  • facto_summarize()

DEPRECATED FUNCTION

  • fviz_pca_contrib() is dreprecated -> use fviz_contrib()

MINOR CHANGING

  • fviz_add: “text” are included in the allowed values for the argument geom
  • fviz_screeplot: the X parameter can be also an object of class ca [ca], coa [ade4], correspondence [MASS]
  • get_eigenvalue: X parameters and description changed
  • get_pca_ind: the argument data are no longer required

factoextra 1.0.1

FEATURES

  • Easy to use functions to extract and visualize the output of principal component analysis.