{"id":7178,"date":"2018-09-21T22:15:37","date_gmt":"2018-09-21T20:15:37","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?post_type=product&#038;p=7178"},"modified":"2023-12-31T08:29:57","modified_gmt":"2023-12-31T07:29:57","slug":"practical-guide-to-principal-component-methods-in-r","status":"publish","type":"product","link":"https:\/\/www.datanovia.com\/en\/product\/practical-guide-to-principal-component-methods-in-r\/","title":{"rendered":"Practical Guide To Principal Component Methods in R"},"content":{"rendered":"<p>&nbsp;<\/p>\n<div id=\"rdoc\">\n<p>Although there are several good books on <strong>principal component methods<\/strong> (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced.<\/p>\n<p>This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R.<\/p>\n<p>The following figure illustrates the type of analysis to be performed depending on the type of variables contained in the data set.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/images\/multivariate-analysis-factoextra.png\" alt=\"Principal component methods\" \/><\/p>\n<p>There are a number of R packages implementing principal component methods. These packages include: <em>FactoMineR<\/em>, <em>ade4<\/em>, <em>stats<\/em>, <em>ca<\/em>, <em>MASS<\/em> and <em>ExPosition<\/em>.<\/p>\n<p>However, the result is presented differently depending on the used package.<\/p>\n<p>To help in the interpretation and in the visualization of multivariate analysis - such as cluster analysis and principal component methods - we developed an easy-to-use R package named <a href=\"https:\/\/rpkgs.datanovia.com\/factoextra\"><strong>factoextra<\/strong><\/a>.<\/p>\n<div class=\"block\">\n<p>No matter which package you decide to use for computing principal component methods, the factoextra R package can help to extract easily, in a human readable data format, the analysis results from the different packages mentioned above. factoextra provides also convenient solutions to create ggplot2-based beautiful graphs.<\/p>\n<\/div>\n<p>Methods, which outputs can be visualized using the factoextra package are shown in the figure below:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/images\/factoextra-r-package.png\" alt=\"Principal component methods and clustering methods supported by the factoextra R package\" \/><\/p>\n<p>In this book, we\u2019ll use mainly:<\/p>\n<div class=\"success\">\n<ul>\n<li>the <strong>FactoMineR<\/strong> package to compute principal component methods;<\/li>\n<li>and the <strong>factoextra<\/strong> package for extracting, visualizing and interpreting the results.<\/li>\n<\/ul>\n<p>The other packages - ade4, ExPosition, etc - will be also presented briefly.<\/p>\n<\/div>\n<div id=\"how-this-book-is-organized\" class=\"section level2\">\n<h2>How this book is organized<\/h2>\n<p>This book contains 4 parts.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/images\/principal-component-methods-book-structure.png\" alt=\"Principal Component Methods book structure\" \/><\/p>\n<p><strong>Part I<\/strong> provides a quick introduction to R and presents the key features of FactoMineR and factoextra.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/images\/r-packages-multivariate-analysis.png\" alt=\"Key features of FactoMineR and factoextra for multivariate analysis\" \/><\/p>\n<p><strong>Part II<\/strong> describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include:<\/p>\n<ul>\n<li>Principal Component Analysis (PCA, for continuous variables),<\/li>\n<li>Simple correspondence analysis (CA, for large contingency tables formed by two categorical variables)<\/li>\n<li>Multiple correspondence analysis (MCA, for a data set with more than 2 categorical variables).<\/li>\n<\/ul>\n<p>In <strong>Part III<\/strong>, you\u2019ll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups:<\/p>\n<ul>\n<li>Factor Analysis of Mixed Data (FAMD) and,<\/li>\n<li>Multiple Factor Analysis (MFA).<\/li>\n<\/ul>\n<p><strong>Part IV<\/strong> covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables<\/p>\n<\/div>\n<div id=\"key-features-of-this-book\" class=\"section level2\">\n<h2>Key features of this book<\/h2>\n<p>This book presents the basic principles of the different methods and provide many examples in R. This book offers solid guidance in data mining for students and researchers.<\/p>\n<p>Key features:<\/p>\n<ul>\n<li>Covers principal component methods and implementation in R<\/li>\n<li>Highlights the most important information in your data set using ggplot2-based elegant visualization<\/li>\n<li>Short, self-contained chapters with tested examples that allow for flexibility in designing a course and for easy reference<\/li>\n<\/ul>\n<div class=\"block\">\n<p>At the end of each chapter, we present R lab sections in which we systematically work through applications of the various methods discussed in that chapter. Additionally, we provide links to other resources and to our hand-curated list of videos on principal component methods for further learning.<\/p>\n<\/div>\n<\/div>\n<div id=\"examples-of-plots\" class=\"section level2\">\n<h2>Examples of plots<\/h2>\n<p>Some examples of plots generated in this book are shown hereafter. You\u2019ll learn how to create, customize and interpret these plots.<\/p>\n<ol style=\"list-style-type: decimal;\">\n<li><strong>Eigenvalues\/variances of principal components<\/strong>. Proportion of information retained by each principal component.<\/li>\n<\/ol>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/figures\/012-principal-component-methods-book-intro-pca-eigenvalue-1.png\" width=\"432\" \/><\/p>\n<ol style=\"list-style-type: decimal;\" start=\"2\">\n<li><strong>PCA - Graph of variables<\/strong>:<\/li>\n<\/ol>\n<ul>\n<li>Control variable colors using their contributions to the principal components.<\/li>\n<\/ul>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/figures\/012-principal-component-methods-book-intro-pca-variable-colors-by-contributions-1.png\" width=\"480\" \/><\/p>\n<ul>\n<li>Highlight the most contributing variables to each principal dimension:<\/li>\n<\/ul>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/figures\/012-principal-component-methods-book-intro-pca-variable-contributions-1.png\" width=\"288\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/figures\/012-principal-component-methods-book-intro-pca-variable-contributions-2.png\" width=\"288\" \/><\/p>\n<ol style=\"list-style-type: decimal;\" start=\"3\">\n<li><strong>PCA - Graph of individuals<\/strong>:<\/li>\n<\/ol>\n<ul>\n<li>Control automatically the color of individuals using the cos2 (the quality of the individuals on the factor map)<\/li>\n<\/ul>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/figures\/012-principal-component-methods-book-intro-pca-individuals-1.png\" width=\"528\" \/><\/p>\n<ul>\n<li>Change the point size according to the cos2 of the corresponding individuals:<\/li>\n<\/ul>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/figures\/012-principal-component-methods-book-intro-pca-graph-individuals-point-size-by-cos2-1.png\" width=\"528\" \/><\/p>\n<ol style=\"list-style-type: decimal;\" start=\"4\">\n<li><strong>PCA - Biplot of individuals and variables<\/strong><\/li>\n<\/ol>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/figures\/012-principal-component-methods-book-intro-pca-color-individuals-and-variables-by-groups-1.png\" width=\"528\" \/><\/p>\n<ol style=\"list-style-type: decimal;\" start=\"5\">\n<li><strong>Correspondence analysis<\/strong>. Association between categorical variables.<\/li>\n<\/ol>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/figures\/012-principal-component-methods-book-intro-correspondence-analysis-1.png\" width=\"528\" \/><\/p>\n<ol style=\"list-style-type: decimal;\" start=\"6\">\n<li><strong>FAMD\/MFA<\/strong> - Analyzing mixed and structured data<\/li>\n<\/ol>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/figures\/012-principal-component-methods-book-intro-famd-plot-ellipse-1.png\" width=\"499.2\" \/><\/p>\n<ol style=\"list-style-type: decimal;\" start=\"7\">\n<li><strong>Clustering on principal components<\/strong><\/li>\n<\/ol>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/principal-component-methods\/figures\/012-principal-component-methods-book-intro-hierarchical-clustering-on-principal-component-1.png\" width=\"528\" \/><\/p>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using\u00a0<strong>principal component methods<\/strong>\u00a0in R.<\/p>\n<p>You will learn:<\/p>\n<ul>\n<li>Principal Component Analysis (PCA) for summarizing a large dataset of continuous variables<\/li>\n<li>Simple Correspondence Analysis (CA)\u00a0for large contingency tables formed by two categorical variables<\/li>\n<li>Multiple Correspondence Analysis (MCA) for a data set with more than 2 categorical variables<\/li>\n<li>Methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups:\u00a0Factor Analysis of Mixed Data\u00a0(FAMD) and\u00a0Multiple Factor Analysis\u00a0(MFA).<\/li>\n<li>Hierarchical Clustering on Principal Components\u00a0(HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables<\/li>\n<\/ul>\n<div class=\"aff-ordering-info\"><b>Order a Physical Copy<\/b> on <a href=\"https:\/\/www.datanovia.com\/en\/m53t\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Amazon<\/a>:<br \/>\n<a href=\"https:\/\/www.datanovia.com\/en\/m53t\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/affiliate-marketing\/images\/amazon.png\" alt=\"Amazon\" \/><\/a><\/p>\n<p><b>Or, Buy and Download Now a PDF Copy<\/b> by clicking on the <b>&#8220;ADD TO CART&#8221;<\/b> button down below. You will receive a link to download a <b>PDF copy<\/b> (click to see the <a href=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/book-preview\/principal-component-methods-in-r-preview.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">book preview<\/a>)<\/p>\n<\/div>\n","protected":false},"featured_media":7179,"comment_status":"open","ping_status":"closed","template":"","meta":{"rating_form_position":"","rating_results_position":"","mr_structured_data_type":""},"product_brand":[],"product_cat":[110],"product_tag":[112,113],"class_list":{"0":"post-7178","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-book","7":"product_tag-multivariate-analysis","8":"product_tag-unsupervised-learning","10":"first","11":"instock","12":"sale","13":"downloadable","14":"virtual","15":"purchasable","16":"product-type-simple"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Practical Guide To Principal Component Methods in R - Datanovia<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.datanovia.com\/en\/product\/practical-guide-to-principal-component-methods-in-r\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Practical Guide To Principal Component Methods in R - Datanovia\" \/>\n<meta property=\"og:description\" content=\"This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using\u00a0principal component methods\u00a0in R.  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You will learn:   Principal Component Analysis (PCA) for summarizing a large dataset of continuous variables  Simple Correspondence Analysis (CA)\u00a0for large contingency tables formed by two categorical variables  Multiple Correspondence Analysis (MCA) for a data set with more than 2 categorical variables  Methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups:\u00a0Factor Analysis of Mixed Data\u00a0(FAMD) and\u00a0Multiple Factor Analysis\u00a0(MFA).  Hierarchical Clustering on Principal Components\u00a0(HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables  Order a Physical Copy on Amazon:   Or, Buy and Download Now a PDF Copy by clicking on the \"ADD TO CART\" button down below. 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