In January 2016, I was honored to receive an “Honorable Mention” of the John Chambers Award 2016. This article was written for R-bloggers, whose builder, Tal Galili, kindly invited me to write an introduction to the rARPACK package. A Short Story of rARPACK Eigenvalue decomposition is a commonly used technique in numerous statistical problems. For example, principal component analysis (PCA) basically conducts eigenvalue decomposition on the sample covariance of a data matrix: the eigenvalues are the component variances, and eigenvectors are the variable loadings.

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This semester I’m taking a course in big data computing using Scala/Spark, and we are asked to finish a course project related to big data analysis. Since statistical modeling heavily relies on linear algebra, I investigated some existing libraries in Scala/Java that deal with matrix and linear algebra algorithms. 1. Set-up Scala/Java libraries are usually distributed as *.jar files. To use them in Scala, we can create a directory to hold them and set up the environment variable to let Scala know about this path.

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Dr. Hadley Wickham is the Chief Scientist of RStudio and Assistant Professor of Statistics at Rice University. He is the developer of the famous R package ggplot2 for data visualization and the author of many other widely used packages like plyr and reshape2. On Sep 13, 2013 he gave a talk at Department of Statistics, Purdue University, and later I (Yixuan) had a conversation with him (Hadley), talking about his own experience and interest on data visualization, data tidying, R programming and other related topics.

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