factoextra:提取和可视化多元数据分析的结果

[](https://travis-ci.org/kassambara/factoextra) [](https://cran.r-project.org/package=factoextra) [](https://cran.r-project.org/package=factoextra) [](http://cranlogs.r-pkg.org/badges/grand-total/factoextra) [](http://www.repostatus.org/#active) [](https://github.com/kassambara/factoextra/pulls) [](https://github.com/kassambara/factoextra/issues) factoextra : Extract and Visualize the Results of Multivariate Data Analyses ============================================================================ [**factoextra**](http://www.sthda.com/english/rpkgs/factoextra) is an R package making easy to *extract* and *visualize* the output of exploratory **multivariate data analyses**, including: 1. [Principal Component Analysis (PCA)](http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/112-pca-principal-component-analysis-essentials/), 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. 2. [Correspondence Analysis (CA)](http://www.sthda.com/english/wiki/correspondence-analysis-in-r-the-ultimate-guide-for-the-analysis-the-visualization-and-the-interpretation-r-software-and-data-mining), which is an extension of the principal component analysis suited to analyse a large contingency table formed by two *qualitative variables* (or categorical data). 3. [Multiple Correspondence Analysis (MCA)](http://www.sthda.com/english/wiki/multiple-correspondence-analysis-essentials-interpretation-and-application-to-investigate-the-associations-between-categories-of-multiple-qualitative-variables-r-software-and-data-mining), which is an adaptation of CA to a data table containing more than two categorical variables. 4. [Multiple Factor Analysis (MFA)](http://www.sthda.com/english/rpkgs/factoextra/reference/fviz_mfa.html) dedicated to datasets where variables are organized into groups (qualitative and/or quantitative variables). 5. [Hierarchical Multiple Factor Analysis (HMFA)](http://www.sthda.com/english/rpkgs/factoextra/reference/fviz_hmfa.html): An extension of MFA in a situation where the data are organized into a hierarchical structure. 6. [Factor Analysis of Mixed Data (FAMD)](http://www.sthda.com/english/rpkgs/factoextra/reference/fviz_famd.html), 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](http://www.sthda.com/english/rpkgs/factoextra). - The R package **factoextra** has flexible and easy-to-use methods to extract quickly, in a human readable standard data format, the analysis results from the different packages mentioned above. - It produces a **ggplot2**-based **elegant data visualization** with less typing. - It contains also many functions facilitating clustering analysis and visualization. > 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>.  Why using factoextra? --------------------- 1. 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. 2. *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 - their contributions to the definition of the principal dimensions. <span class="success">If you want to do this, the factoextra package provides a convenient solution.</span> 1. *PCA and (M)CA are used sometimes for prediction problems* : one can predict the coordinates of new supplementary variables (quantitative and qualitative) and supplementary individuals using the information provided by the previously performed PCA or (M)CA. This can be done easily using the [FactoMineR](http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/112-pca-principal-component-analysis-essentials/) package. <span class="success">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...</span> 1. *Several functions from different packages - FactoMineR, ade4, ExPosition, stats - are available in R for performing PCA, CA or MCA*. However, The components of the output vary from package to package. <span class="success">No matter the package you decided to use, factoextra can give you a human understandable output.</span> Installing FactoMineR --------------------- The FactoMineR package can be installed and loaded as follow: ``` r # Install install.packages("FactoMineR") # Load library("FactoMineR") ``` Installing and loading factoextra --------------------------------- - factoextra can be installed from [CRAN](https://cran.r-project.org/package=factoextra) as follow: ``` r install.packages("factoextra") ``` - Or, install the latest version from [Github](https://github.com/kassambara/factoextra) ``` r if(!require(devtools)) install.packages("devtools") devtools::install_github("kassambara/factoextra") ``` - Load factoextra as follow : ``` r library("factoextra") #> Loading required package: ggplot2 #> Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa ``` Main functions in the factoextra package ---------------------------------------- <span class="warning">See the online documentation (<http://www.sthda.com/english/rpkgs/factoextra>) for a complete list.</span> ### Visualizing dimension reduction analysis outputs <table style="width:97%;"> <colgroup> <col width="13%" /> <col width="83%" /> </colgroup> <thead> <tr class="header"> <th>Functions</th> <th>Description</th> </tr> </thead> <tbody> <tr class="odd"> <td><em>fviz_eig (or fviz_eigenvalue)</em></td> <td>Extract and visualize the eigenvalues/variances of dimensions.</td> </tr> <tr class="even"> <td><em>fviz_pca</em></td> <td>Graph of individuals/variables from the output of <em>Principal Component Analysis</em> (PCA).</td> </tr> <tr class="odd"> <td><em>fviz_ca</em></td> <td>Graph of column/row variables from the output of <em>Correspondence Analysis</em> (CA).</td> </tr> <tr class="even"> <td><em>fviz_mca</em></td> <td>Graph of individuals/variables from the output of <em>Multiple Correspondence Analysis</em> (MCA).</td> </tr> <tr class="odd"> <td><em>fviz_mfa</em></td> <td>Graph of individuals/variable