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Machine Learning Essentials

Sat, 10 Mar 2018 15:37:00 +0100

Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques.

This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring data sets, as well as, for building predictive models.

The main parts of the book include:

Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods.
Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies.
Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines.
Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting).
Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables.
Model validation and evaluation techniques for measuring the performance of a predictive model.
Model diagnostics for detecting and fixing a potential problems in a predictive model.

The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers.

Key features:

Covers machine learning algorithm and implementation
Key mathematical concepts are presented
Short, self-contained chapters with practical examples.

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.

Where to find the book?:

Download the PDF through payhip
Read the ebook on google play
Order a physical copy from amazon
(Download the book preview)

Network Analysis and Visualization in R

Mon, 27 Nov 2017 08:17:00 +0100

Social network analysis is used to investigate the inter-relationship between entities. Examples of network structures, include: social media networks, friendship networks and collaboration networks.

This book provides a quick start guide to network analysis and visualization in R.

You'll learn, how to:

- Create static and interactive network graphs using modern R packages.
- Change the layout of network graphs.
- Detect important or central entities in a network graph.
- Detect community (or cluster) in a network.

R Graphics Essentials for Great Data Visualization: 200 Practical Examples You Want to Know for Data Science

Thu, 16 Nov 2017 07:43:00 +0100

Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data.

In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems.

Next, we provide practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics.

With this book, you 'll learn:

- How to quickly create beautiful graphics using ggplot2 packages

- How to properly customize and annotate the plots

- Type of graphics for visualizing categorical and continuous variables

- How to add automatically p-values to box plots, bar plots and alternatives

- How to add marginal density plots and correlation coefficients to scatter plots

- Key methods for analyzing and visualizing multivariate data

- R functions and packages for plotting time series data

- How to combine multiple plots on one page to create production-quality figures.

Exploratory Multivariate Analysis by Example Using R

Thu, 24 Aug 2017 18:53:00 +0200

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications.

It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.

The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods.

The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical.

They also illustrate the methods using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors.

The book has been written using minimal mathematics so as to appeal to applied statisticians, as well as researchers from various disciplines, including medical research and the social sciences. Readers can use the theory, examples, and software presented in this book in order to be fully equipped to tackle real-life multivariate data.

Practical Guide to Principal Component Methods in R

Wed, 23 Aug 2017 11:12:00 +0200

Introduction

Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced.

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.

Where to find the book:

Download the PDF through payhip
Read the ebook on google play
Order a physical copy from amazon
(Download the book preview)

The following figure illustrates the type of analysis to be performed depending on the type of variables contained in the data set.

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 depending on the used package.

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 factoextra (official online documentation: https://www.sthda.com/english/rpkgs/factoextra).

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.

Methods, which outputs can be visualized using the factoextra package are shown in the figure below:

In this book, we’ll use mainly:

the FactoMineR package to compute principal component methods;
and the factoextra package for extracting, visualizing and interpreting the results.

The other packages - ade4, ExPosition, etc - will be also presented briefly.

How this book is organized

This book contains 4 parts.

Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra.

Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include:

Principal Component Analysis (PCA, for continuous variables),
Simple correspondence analysis (CA, for large contingency tables formed by two categorical variables)
Multiple correspondence analysis (MCA, for a data set with more than 2 categorical variables).

In Part III, you’ll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups:

Factor Analysis of Mixed Data (FAMD) and,
Multiple Factor Analysis (MFA).

Part IV 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

Key features of this book

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.

Key features:

Covers principal component methods and implementation in R
Highlights the most important information in your data set using ggplot2-based elegant visualization
Short, self-contained chapters with tested examples that allow for flexibility in designing a course and for easy reference

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.

Examples of plots

Some examples of plots generated in this book are shown hereafter. You’ll learn how to create, customize and interpret these plots.

Eigenvalues/variances of principal components. Proportion of information retained by each principal component.

PCA - Graph of variables:

Control variable colors using their contributions to the principal components.

Highlight the most contributing variables to each principal dimension:

PCA - Graph of individuals:

Control automatically the color of individuals using the cos2 (the quality of the individuals on the factor map)

Change the point size according to the cos2 of the corresponding individuals:

PCA - Biplot of individuals and variables

Correspondence analysis. Association between categorical variables.

FAMD/MFA - Analyzing mixed and structured data

Clustering on principal components

Book preview

Download the preview of the book at: Principal Component Methods in R (Book preview)

Order now

About the author

Alboukadel Kassambara is a PhD in Bioinformatics and Cancer Biology. He works since many years on genomic data analysis and visualization (read more: http://www.alboukadel.com/).

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.

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.

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.

He is the author of many popular R packages for:

multivariate data analysis (factoextra, https://www.sthda.com/english/rpkgs/factoextra),
survival analysis (survminer, https://www.sthda.com/english/rpkgs/survminer/),
correlation analysis (ggcorrplot, https://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2),
creating publication ready plots in R (ggpubr, https://www.sthda.com/english/rpkgs/ggpubr).

Recently, he published three books on data analysis and visualization:

Practical Guide to Cluster Analysis in R (https://goo.gl/DmJ5y5)
Guide to Create Beautiful Graphics in R (https://goo.gl/vJ0OYb).
Complete Guide to 3D Plots in R (https://goo.gl/v5gwl0).

R Markdown for the Enterprise

Sat, 12 Aug 2017 11:02:00 +0200

This blog post shows how helpful can be R Markdown for the enterprise needs. R Markdown combines the creation and sharing steps.

Three requests can be satisfied using the following features of R Markdown:

1) Break out the reports - Using R Markdown’s Parameterized Reports feature, we can easily create documents for each required segment.

2) Automate the file creation - R Markdown can be run from code, so a separate R script can iteratively run the R Markdown and pass a different parameter for each iteration.

3) Create the slides inside R - Take advantage of R Markdown Presentation output to create a slide deck. Without having to learn a new scripting language, we can code the slide deck and use the same Parameter feature to automate its creation.

4) Keep the interactivity - In many cases, the end user needs a level of interactivity with the report. This interactivity can be achieved by using htmlwidgets inside the R Markdown document. For example, the Leaflet widget can be used for interactive maps, the Data Table widget for interactive tables, and the dygraphs widget for interactive time series charting.

Integrating dplyr with Remote databases

Sat, 12 Aug 2017 10:41:00 +0200

This blog post provides a practical example to connect to a remote databases from R using the dplyr/dbplyr package.

sparklyr: R Interface to Apache Spark

Sat, 12 Aug 2017 10:31:00 +0200

The sparklyr R package makes it possible to:

- Connect to Spark from R. The sparklyr package provides a complete dplyr backend.

- Filter and aggregate Spark datasets then bring them into R for analysis and visualization.

- Use Spark’s distributed machine learning library from R.

- Create extensions that call the full Spark API and provide interfaces to Spark packages.

Introducing an R interface for Apache Spark

Sat, 12 Aug 2017 10:21:00 +0200

This video tutorial presents the sparklyr R package. In this four part series, the author discusses how to leverage Spark’s capabilities in a modern R environment.

The Sparklyr Series:

- Introducing an R interface for Apache Spark

- Extending Spark using sparklyr and R

- Advanced Features of sparklyr

- Understanding Spark and sparklyr deployment modes

Using databases with R

Sat, 12 Aug 2017 10:16:00 +0200

This website is meant to document, over time, all database best practices and tools for R, so you can find everything you need in one place.