1617 search results for "Excel"

Hurricanes and Reproducible Research

November 8, 2013
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Hurricanes and Reproducible Research

On vacation with my family this week and that means I have a few minutes now and again to read. One of the books I brought along is Christopher Gandrud’s excellent “Reproducible Research with R and RStudio”. Looking for some data as a test project, I latched onto Hurricane data. Folly Beach was hit pretty

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The R Backpages

November 7, 2013
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The R Backpages

by Joseph Rickert As an avid newspaper reader (I still get the print edition of the New York Times delivered every Sunday morning) I have always thought that some of the most interesting news is to be found in the back pages. So, in that spirit here are some things that I thought might be fit to print. Plotly...

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Unsupervised data pre-processing: individual predictors

November 7, 2013
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Unsupervised data pre-processing: individual predictors

I just got the excellent book Applied Predictive Modeling, by Max Kuhn and Kjell Johnson . The book is designed for a broad audience and focus on the construction and application of predictive models. Besides going through the necessary theory in a not-so-technical way, the book provides R code at the end of each chapter.

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NYC R Programming Classes – starting this coming Sunday

November 5, 2013
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NYC R Programming Classes – starting this coming Sunday

Guest post by Vivian Zhang, original post. You can sign up for our Sunday Intensive beginner level R classes at NYC Data Science Academy meetup page or [email protected] more info. Brief: The course (which will meet five Sundays) will start from the basics, introducing the building blocks used for programming in R and building intuition for writing clean and robust code....

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Archival and analysis of #GI2013 Tweets

November 4, 2013
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Archival and analysis of #GI2013 Tweets

I archived and analyzed all Tweets containing #GI2013 from the recent Cold Spring Harbor Genome Informatics meeting, using my previously described code.Friday was the most Tweeted day. Perhaps this was due to Lior Pachter's excellent keynote, "Stories ...

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Dream Team – combining Tableau and R

November 3, 2013
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Dream Team – combining Tableau and R

Last quarter was a bit too busy to write some new blog post because of a new job. And changing the job often come along with changing the tools you work with. That was my way to Tableau. Tableau is one of the new stars in the BI/Analytics world and definitely worth a look. The people at Tableau...

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Data Preparation – Part I

October 31, 2013
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Data Preparation – Part I

The R language provides tools for modeling and visualization, but is still an excellent tool for handling/preparing data. As C++ or python, there is some tricks that bring performance, make the code clean or both, but especially with R these choices can have a huge impact on performance and the “size” of your code. A The post Data...

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R and my divorce from Word

October 30, 2013
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R and my divorce from Word

Being in grad school, I do a lot of scholarly writing that requires associated or embedded R analyses, figures, and tables, plus bibliographies. Microsoft Word makes this unnecessarily difficult. Many tools are now available to break free from the tyranny of Word. The ones I like involve writing an article in markdown format, integrating all data preparation,...

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Poisson regression fitted by glm(), maximum likelihood, and MCMC

October 29, 2013
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Poisson regression fitted by glm(), maximum likelihood, and MCMC

The goal of this post is to demonstrate how a simple statistical model (Poisson log-linear regression) can be fitted using three different approaches. I want to demonstrate that both frequentists and Bayesians use the same models, and that it is the fitting procedure and the inference that differs. This is … Continue reading →

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Scaling up text processing and Shutting up R: Topic modelling and MALLET

October 29, 2013
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Scaling up text processing and Shutting up R: Topic modelling and MALLET

In this post I show how a combination of MALLET, Python, and data.table means we can analyse quite Big data in R, even though R itself buckles when confronted by textual data.  Topic modelling is great fun. Using topic modelling I have been able to separate articles about the 'Kremlin' as a) a building, b) an international actor c) the...

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