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(This article was first published on <strong><a href="http://www.sthda.com/english/wiki/practical-guide-to-principal-component-methods-in-r"> Easy Guides</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<div>
<h2>Introduction</h2>
<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>
<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>
<p>Where to find the book:</p>
<ul>
<li>Download the <strong>PDF</strong> through <a rel="nofollow" href="https://payhip.com/b/shrk" target="_blank">payhip</a></li>
<li>Read the <strong>ebook</strong> on <a rel="nofollow" href="https://play.google.com/store/books/author?id=Alboukadel+Kassambara" target="_blank">google play</a></li>
<li>Order a <strong>physical copy</strong> from <a rel="nofollow" href="https://www.amazon.com/dp/1975721136" target="_blank">amazon</a></li>
<li>(Download the <a rel="nofollow" href="http://www.sthda.com/english/upload/principal_component_methods_in_r_preview.pdf" target="_blank">book preview</a>)</li>
</ul>
<p><a rel="nofollow" href="https://payhip.com/b/shrk" title="Download now the PDF on Payhip" target="_blank"><img src="https://www.r-bloggers.com/english/sthda-upload/images/principal-component-methods-cover-200px.png"></a></p>
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<p>The following figure illustrates the type of analysis to be performed depending on the type of variables contained in the data set.</p>
<p><img alt="Principal component methods" src="https://i2.wp.com/www.sthda.com/english/english/sthda-upload/images/multivariate-analysis-factoextra.png?w=456" data-recalc-dims="1"></p>
<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>
<p>However, the result is presented differently depending on the used package.</p>
<p>To help in the interpretation and in the visualization of multivariate analysis – such as <a rel="nofollow" href="http://www.sthda.com/english/web/5-bookadvisor/17-practical-guide-to-cluster-analysis-in-r/" target="_blank">cluster analysis</a> and principal component methods – we developed an easy-to-use R package named <a rel="nofollow" href="http://www.sthda.com/english/rpkgs/factoextra" target="_blank"><strong>factoextra</strong></a> (official online documentation: <a rel="nofollow" href="http://www.sthda.com/english/rpkgs/factoextra" target="_blank">http://www.sthda.com/english/rpkgs/factoextra</a>).</p>
<div>
<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>
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<p>Methods, which outputs can be visualized using the factoextra package are shown in the figure below:</p>
<p><img alt="Principal component methods and clustering methods supported by the factoextra R package" src="https://i1.wp.com/www.sthda.com/english/english/sthda-upload/images/factoextra-r-package.png?w=456" data-recalc-dims="1"></p>
<p>In this book, we’ll use mainly:</p>
<div>
<ul>
<li>
the <strong>FactoMineR</strong> package to compute principal component methods;
</li>
<li>
and the <strong>factoextra</strong> package for extracting, visualizing and interpreting the results.
</li>
</ul>
<p>
The other packages – ade4, ExPosition, etc – will be also presented briefly.
</p>
</div>
</div>
<div>
<h2>How this book is organized</h2>
<p>This book contains 4 parts.</p>
<p><img alt="Principal Component Methods book structure" src="https://i0.wp.com/www.sthda.com/english/english/sthda-upload/images/principal-component-methods-book-structure.png?w=456" data-recalc-dims="1"></p>
<p><strong>Part I</strong> provides a quick introduction to R and presents the key features of FactoMineR and factoextra.</p>
<p><img alt="Key features of FactoMineR and factoextra for multivariate analysis" src="https://i0.wp.com/www.sthda.com/english/english/sthda-upload/images/r-packages-multivariate-analysis.png?w=456" data-recalc-dims="1"></p>
<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>
<ul>
<li>Principal Component Analysis (PCA, for continuous variables),</li>
<li>Simple correspondence analysis (CA, for large contingency tables formed by two categorical variables)</li>
<li>Multiple correspondence analysis (MCA, for a data set with more than 2 categorical variables).</li>
</ul>
<p>In <strong>Part III</strong>, you’ll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups:</p>
<ul>
<li>Factor Analysis of Mixed Data (FAMD) and,</li>
<li>Multiple Factor Analysis (MFA).</li>
</ul>
<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>
</div>
<div>
<h2>Key features of this book</h2>
<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>
<p>Key features:</p>
<ul>
<li>Covers principal component methods and implementation in R</li>
<li>Highlights the most important information in your data set using ggplot2-based elegant visualization</li>
<li>Short, self-contained chapters with tested examples that allow for flexibility in designing a course and for easy reference</li>
</ul>
<div>
<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>
</div>
</div>
<div>
<h2>Examples of plots</h2>
<p>Some examples of plots generated in this book are shown hereafter. You’ll learn how to create, customize and interpret these plots.</p>
<ol>
<li><strong>Eigenvalues/variances of principal components</strong>. Proportion of information retained by each principal component.</li>
</ol>
<p><img src="https://i0.wp.com/www.sthda.com/english/english/sthda-upload/figures/principal-component-methods/principal-component-methods-book-intro-pca-eigenvalue-1.png?w=432" data-recalc-dims="1"></p>
<ol start="2">
<li><strong>PCA – Graph of variables</strong>:</li>
</ol>
<ul>
<li>Control variable colors using their contributions to the principal components.</li>
</ul>
<p><img src="https://i0.wp.com/www.sthda.com/english/english/sthda-upload/figures/principal-component-methods/principal-component-methods-book-intro-pca-variable-colors-by-contributions-1.png?w=450" data-recalc-dims="1"></p>
<ul>
<li>Highlight the most contributing variables to each principal dimension:</li>
</ul>
<p><img src="https://i2.wp.com/www.sthda.com/english/english/sthda-upload/figures/principal-component-methods/principal-component-methods-book-intro-pca-variable-contributions-1.png?w=288" data-recalc-dims="1"><img src="https://i2.wp.com/www.sthda.com/english/english/sthda-upload/figures/principal-component-methods/principal-component-methods-book-intro-pca-variable-contributions-2.png?w=288" data-recalc-dims="1"></p>
<ol start="3">
<li><strong>PCA – Graph of individuals</strong>:</li>
</ol>
<ul>
<li>Control automatically the color of individuals using the cos2 (the quality of the individuals on the factor map)</li>
</ul>
<p><img src="https://i2.wp.com/www.sthda.com/english/english/sthda-upload/figures/principal-component-methods/principal-component-methods-book-intro-pca-individuals-1.png?w=450" data-recalc-dims="1"></p>
<ul>
<li>Change the point size according to the cos2 of the corresponding individuals:</li>
</ul>
<p><img src="https://i2.wp.com/www.sthda.com/english/english/sthda-upload/figures/principal-component-methods/principal-component-methods-book-intro-pca-graph-individuals-point-size-by-cos2-1.png?w=450" data-recalc-dims="1"></p>
<ol start="4">
<li><strong>PCA – Biplot of individuals and variables</strong></li>
</ol>
<p><img src="https://i1.wp.com/www.sthda.com/english/english/sthda-upload/figures/principal-component-methods/principal-component-methods-book-intro-pca-color-individuals-and-variables-by-groups-1.png?w=450" data-recalc-dims="1"></p>
<ol start="5">
<li><strong>Correspondence analysis</strong>. Association between categorical variables.</li>
</ol>
<p><img src="https://i2.wp.com/www.sthda.com/english/english/sthda-upload/figures/principal-component-methods/principal-component-methods-book-intro-correspondence-analysis-1.png?w=450" data-recalc-dims="1"></p>
<ol start="6">
<li><strong>FAMD/MFA</strong> – Analyzing mixed and structured data</li>
</ol>
<p><img src="https://i2.wp.com/www.sthda.com/english/english/sthda-upload/figures/principal-component-methods/principal-component-methods-book-intro-famd-plot-ellipse-1.png?w=450" data-recalc-dims="1"></p>
<ol start="7">
<li><strong>Clustering on principal components</strong></li>
</ol>
<p><img src="https://i2.wp.com/www.sthda.com/english/english/sthda-upload/figures/principal-component-methods/principal-component-methods-book-intro-hierarchical-clustering-on-principal-component-1.png?w=450" data-recalc-dims="1"></p>
</div>
<div>
<h2>Book preview</h2>
<p>Download the preview of the book at: <a rel="nofollow" href="http://www.sthda.com/english/upload/principal_component_methods_in_r_preview.pdf" target="_blank">Principal Component Methods in R (Book preview)</a></p>
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<h2>Order now</h2>
<p><a rel="nofollow" href="https://payhip.com/b/shrk" title="Download now the PDF on Payhip" target="_blank"><img src="https://www.r-bloggers.com/english/sthda-upload/images/principal-component-methods-cover-200px.png"></a></p>
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<h2>About the author</h2>
<p>Alboukadel Kassambara is a PhD in Bioinformatics and Cancer Biology. He works since many years on genomic data analysis and visualization (read more: <a rel="nofollow" href="http://www.alboukadel.com/" target="_blank">http://www.alboukadel.com/</a>).</p>
<p>He has work experiences in statistical and computational methods to identify prognostic and predictive biomarker signatures through integrative analysis of large-scale genomic and clinical data sets.</p>
<p>He created a bioinformatics web-tool named GenomicScape (www.genomicscape.com) which is an easy-to-use web tool for gene expression data analysis and visualization.</p>
<p>He developed also a training website on data science, named STHDA (Statistical Tools for High-throughput Data Analysis, www.sthda.com/english), which contains many tutorials on data analysis and visualization using R software and packages.</p>
<p>He is the author of many popular R packages for:</p>
<ul>
<li>multivariate data analysis (<strong>factoextra</strong>, <a rel="nofollow" href="http://www.sthda.com/english/rpkgs/factoextra" target="_blank">http://www.sthda.com/english/rpkgs/factoextra</a>),</li>
<li>survival analysis (<strong>survminer</strong>, <a rel="nofollow" href="http://www.sthda.com/english/rpkgs/survminer/" target="_blank">http://www.sthda.com/english/rpkgs/survminer/</a>),</li>
<li>correlation analysis (<strong>ggcorrplot</strong>, <a rel="nofollow" href="http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2" target="_blank">http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2</a>),</li>
<li>creating publication ready plots in R (<strong>ggpubr</strong>, <a rel="nofollow" href="http://www.sthda.com/english/rpkgs/ggpubr" target="_blank">http://www.sthda.com/english/rpkgs/ggpubr</a>).</li>
</ul>
<p>Recently, he published three books on data analysis and visualization:</p>
<ol>
<li>Practical Guide to Cluster Analysis in R (<a rel="nofollow" href="https://goo.gl/DmJ5y5" target="_blank">https://goo.gl/DmJ5y5</a>)</li>
<li>Guide to Create Beautiful Graphics in R (<a rel="nofollow" href="https://goo.gl/vJ0OYb" target="_blank">https://goo.gl/vJ0OYb</a>).</li>
<li>Complete Guide to 3D Plots in R (<a rel="nofollow" href="https://goo.gl/v5gwl0" target="_blank">https://goo.gl/v5gwl0</a>).</li>
</ol>
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