2327 search results for "Time series"

Best practices for handling packages in R projects

November 11, 2015
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Best practices for handling packages in R projects

by Andrie de Vries For much of my data science work, I want to have the very latest package from CRAN or github. However, once any work finds it way into production server (where it runs on a regular schedule), I want my environment to be stable. Most importantly, for these projects I want to ensure I have reproducible...

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More on stepped wedge

November 7, 2015
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A couple of months back I talked at the launch of the Trial series on the Stepped Wedge Designed, on which I have worked together with a number of colleagues at UCL and LSHTM. Jennifer, who's one of the authors of the series and is doing her ...

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updating the GISS dataset

November 6, 2015
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updating the GISS dataset

Introduction I was watching a video of David Suzuki being interviewed on Australian TV, and there were some questions about the “pause” in temperature in the GISS dataset. I thought I’d like to check for myself, and reasoned that I may as well update the giss dataset in the ocedata R package. As always seems to be the case, the data format is changed...

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Visualizing Bikeshare Data

November 6, 2015
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Visualizing Bikeshare Data

This entry is part 18 of 18 in the series Using RSeattle’s Pronto bikeshare system recently announced a Data Challenge for data visualization using their first year of trip data. As avid cyclists and data analysis junkies, we of course took …   read more ...

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‘inegiR’ – an R package for Mexican official statistics

November 4, 2015
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‘inegiR’ – an R package for Mexican official statistics

(guest post by Eduardo Flores) Introduction For anyone interested in using data from INEGI (the official statistics agency of Mexico), it was sometimes a hasle to look-up all the information in their BIE data base. Of course, their interface is useful for users who need some data series fast because of the export to excel functions. But for users...

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Reverse Geocode using Google API and XML package in R – PART 2

November 3, 2015
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As promised in my previous post this post will dive deeper into understanding how to create links in R and further execute them to generate a list of XML output. In the third and final post we will use this list to filter the data and extract the infor...

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Understanding the Bayesian approach to false discovery rates (using baseball statistics)

November 2, 2015
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Understanding the Bayesian approach to false discovery rates (using baseball statistics)

Previously in this series Understanding the beta distribution (using baseball statistics) Understanding empirical Bayes estimation (using baseball statistics) Understanding credible intervals (using baseball statistics) In my last few posts, I’ve been exploring how to perform estimation of batting averages, as a way to demonstrate empirical Bayesian methods. We’ve been able to construct both point estimates and...

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Applied Statistical Theory: Belief Networks

October 21, 2015
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Applied Statistical Theory: Belief Networks

Applied statistical theory is a new series that will cover the basic methodology and framework behind various statistical procedures. As analysts, we need to know enough about what we’re doing to be dangerous and explain approaches to others. It’s not enough to say “I used X because the misclassification rate was low.” At the same

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How well can you scale your strategy?

October 21, 2015
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How well can you scale your strategy?

This post will deal with a quick, finger in the air way of seeing how well a strategy scales–namely, how … Continue reading →

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Understanding credible intervals (using baseball statistics)

October 20, 2015
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Understanding credible intervals (using baseball statistics)

Previously in this series Understanding the beta distribution (using baseball statistics) Understanding empirical Bayes estimation (using baseball statistics) In my last post, I explained the method of empirical Bayes estimation, a way to calculate useful proportions out of many pairs of success/total counts (e.g. 0/1, 3/10, 235/1000). I used the example of estimating baseball batting averages based...

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