# Monthly Archives: March 2014

## Analysis of experiments using ASReml-R, Chicago, 24/25th April

March 27, 2014
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This workshop is aimed at scientist/practitioners that are interested in analyzing complex datasets by fitting linear mixed models, particularly users with experience in breeding programs and design and analysis of experiments. After the workshop the participants should be able to understand the use of ASReml and to analyze most biological experiment and particularly, study breeding trials for single or...

## Data Preparation – Part II

March 27, 2014
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This time i will talk about how to deal with large text files in chuncks with R. Just to provide The post Data Preparation – Part II appeared first on Flavio Barros .

## More fun with %.% and %>%

March 27, 2014
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The %.% operator in dplyr allows one to put functions together without lots of nested parentheses. The flanking percent signs are R’s way of denoting infix operators; you might have used %in% which corresponds to the match function or %*% which is matrix multiplication. The %.% operator is also called chain, and what it does

## Blurry Fractals

March 27, 2014
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Beauty is the first test; there is no permanent place in the world for ugly mathematics (G. H. Hardy) Newton basin fractals are the result of iterating Newton’s method to find roots of a polynomial over the complex plane. It maybe sound a bit complicated but is actually quite simple to understand. Those who would

## Visualizing principal components with R and Sochi Olympic Athletes

March 27, 2014
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Principal Components Analysis (PCA) is used as a dimensionality reduction method. Here we simply explain PCA step-by-step using data about Sochi Olympic Curlers. It is hard to visualize a high dimensional space. When I took linear algebra, the book and teachers spoke about it as if were easy to visualize a hyperspace, but...

## Alluvial diagrams

March 27, 2014
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Parallel coordinates plot is one of the tools for visualizing multivariate data. Every observation in a dataset is represented with a polyline that crosses a set of parallel axes corresponding to variables in the dataset. You can create such plots in R using a function parcoord in package MASS. For example, we can create such

## New Shiny website launched; Shiny 0.9 released

March 27, 2014
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We’re excited to introduce to you our new website for Shiny: shiny.rstudio.com! We’ve included articles on many Shiny-related topics, dozens of example applications, and an all-new tutorial for getting started. Whether you’re a beginner or expert at Shiny, we hope that having these resources available in one place will help you find the information you need.

## R User Group Activity for Q1 2014

March 27, 2014
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by Joseph Rickert Worldwide R user group activity for the first Quarter of 2014 appears to be way up compared to previous years as the following plot shows. The plot was built by counting the meetings on Revolution Analytics R Community Calendar. R users continue to value the live, in person events and face-to-face meetings with their peers. Moreover,...

## GIS in R: Part 1

March 27, 2014
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I messed around with R for years without really learning how to use it properly. I think it’s because I could always throw my hands up when the going got tough and run back and cling the skirts of Excel or JMP or Systat. I finally learned how to use R when I needed to

## Filtering Data with L1 Regularisation

March 27, 2014
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$Filtering Data with L1 Regularisation$

A few days ago I posted about Filtering Data with L2 Regularisation. Today I am going to explore the other filtering technique described in the paper by Tung-Lam Dao. This is similar to the filter discussed in my previous post, but uses a slightly different objective function: where the regularisation term now employs the L1