Data Visualization with R

by Rob Kabacoff

Data Visualization with R

A guide to creating modern data visualizations with R. Starting with data preparation, topics include how to create effective univariate, bivariate, and multivariate graphs. In addition specialized graphs including geographic maps, the display of change over time, flow diagrams, interactive graphs, and graphs that help with the interpret statistical models are included. Focus is on the 45 most popular graph types. The guide also includes detailed instructions on how to customizing graphs, and ends with a chapter on graphing best practices. Although strongly based on the ggplot2 package, other approaches are included as well. Read more →


Lab notes for Statistics for Social Sciences II: Multivariate Techniques

by Eduardo García Portugués


Lab notes for Statistics for Social Sciences II: Multivariate Techniques […] Welcome to the lab notes for Statistics for Social Sciences II: Multivariate Techniques. Along these notes we will see how to effectively implement the statistical methods presented in the lectures. The exposition we will follow is based on learning by analyzing datasets and real-case studies, always with the help of statistical software. While doing so, we will illustrate the key insights of some multivariate techniques and the adequate use of advanced statistical software. Be advised that these notes are neither … Read more →


Applications of Multivariate Analysis in Business

by Ed Anderson


This document describes the concept of Mass Customisation as it applies to Business Analytics and provides case study implementations of R Studio […] It has been great being part of the Analytical Community the last few years. The excitement is everywhere about “big-data”,“data-science”,“MOOCs”. The talent being attracted into Analytics is awe inspiring.One current trend is ‘a shift from a desire to work for bigger name brand companies like Facebook or Google, to more mission-driven organizations attempting to make an impact on society. Whether it is curing cancer, conserving energy, … Read more →


Multivariate Analysis with Optimal Scaling

by Jan de Leeuw, Patrick Mair, Patrick Groenen

Multivariate Analysis with Optimal Scaling

In 1980 members of the Department of Data Theory at the University of Leiden taught a post-doctoral course in Nonlinear Multivariate Analysis. The course content was sort-of-published, in Dutch, as Gifi (1980). The course was repeated in 1981, and this time the sort-of-published version (Gifi (1981)) was in English. The preface gives some details about the author. The text is the joint product of the members of the Department of Data Theory of the Faculty of Social Sciences, University of Leiden. ‘Albert Gifi’ is their ‘nom de plume’. The portrait, however, of Albert Gifi shown here, is that … Read more →


Exploratory Data Analysis with R

by Roger D. Peng

Exploratory Data Analysis with R

This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data you have. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing informative data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data. Read more →


Block Relaxation Methods in Statistics

by Jan de Leeuw


The book discusses block relaxation, alternating least squares, augmentation, and majorization algorithms to minimize loss functions, with applications in statistics, multivariate analysis, and multidimensional scaling. […] Many recent algorithms in computational statistics are variations on a common theme. In this book we discuss four such classes of algorithms. Or, more precisely, we discuss a single large class of algorithms, and we show how various well-known classes of statistical algorithms fit into this common framework. The types of algorithms we consider are, in logical order, … Read more →