1822 search results for "tutorial"

Getting Started with Mixed Effect Models in R

November 25, 2013
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Getting Started with Multilevel Modeling in R Getting Started with Multilevel Modeling in R Jared E. Knowles Introduction Analysts dealing with grouped data and complex hierarchical structures in their data ranging from measurements nested within participants, to counties nested within states or students nested within classrooms often find themselves...

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R and Bayesian Statistics

November 21, 2013
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R and Bayesian Statistics

by Joseph Rickert Drew Linzer, the Bayesian statistician who attracted considerable attention last year with his spot-on, R-based forecast of the 2012 presidential election, recently gave a tutorial on Bayesian statistics to the Bay Area useR Group (BARUG). Drew covered quite a bit of ground running R code that showed how to make use of WinBugs, JAGS and Stan,...

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rgbif changes in v0.4

November 21, 2013
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The Global Biodiversity Information Facility (GBIF) is a warehouse of species occurrence data - collecting data from a lot of different sources. Our package rgbif allows you to interact with GBIF from R. We interact with GBIF via their Application Programming Interface, or API. Our last version on CRAN (v0.3) interacted with the older version of their API -...

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Sending data from client to server and back using shiny

November 20, 2013
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Sending data from client to server and back using shiny

After some time of using shiny I got to the point where I needed to send some arbitrary data from the client to the server, process it with R and return some other data to the client. As a client/server programming newbie this was a challenge for me as I did not want to dive

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Historical Value at Risk versus historical Expected Shortfall

November 18, 2013
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Historical Value at Risk versus historical Expected Shortfall

Comparing the behavior of the two on the S&P 500. Previously There have been a few posts about Value at Risk (VaR) and Expected Shortfall (ES) including an introduction to Value at Risk and Expected Shortfall. Data and model The underlying data are daily returns for the S&P 500 from 1950 to the present. The VaR and … Continue reading...

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analyze the national survey of children’s health with r

November 18, 2013
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american children of the nineties might have had pogs, beanie babies, m.c. hammer, but we lacked a reliable source for state-level survey estimates on health.  then in 2003, the maternal and child health bureau of the health services and resources...

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Shiny 0.8.0 released; Yihui Xie joins RStudio

November 15, 2013
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Shiny 0.8.0 released; Yihui Xie joins RStudio

We’re very pleased to announce Shiny 0.8.0 (which actually went up on CRAN about two weeks ago). This release features a vastly better way to display tabular data, and new debugging tools that make it much easier to fix errors in your app. DataTables support We now support much more attractive and powerful displays of

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In case you missed it: October 2013 Roundup

November 11, 2013
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In case you missed them, here are some articles from October of particular interest to R users: Joe Rickert recounts the R presence at the Strata + Hadoop World conference, including slides from the R and Hadoop tutorial. Hadley Wickham's favorite tools, gadgets and software (including of course R). Revolution R Enterprise 7 is announced, with updated R engine...

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A slightly different introduction to R, part V: plotting and simulating linear models

November 11, 2013
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A slightly different introduction to R, part V: plotting and simulating linear models

In the last episode (which was quite some time ago) we looked into comparisons of means with linear models. This time, let’s visualise some linear models with ggplot2, and practice another useful R skill, namely how to simulate data from known models. While doing this, we’ll learn some more about the layered structure of a

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Translating between R and SQL: the basics

November 8, 2013
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An introductory comparison of using the two languages. Background R was made especially for data analysis and graphics.  SQL was made especially for databases.  They are allies. The data structure in R that most closely matches a SQL table is a data frame.  The terms rows and columns are used in both. A mashup There The post Translating...

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