Posts Tagged ‘ r-project ’

R has a JSON package

November 5, 2009
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R has a JSON package

Named rjson, appropriately. It’s quite basic just now, but contains methods for interconversion between R objects and JSON. Something like this:> library(rjson) > data <- list(a=1,b=2,c=3) > json <- toJSON(data) > json "{\"a\":1,\"b\":2,\"c\":3}" > cat(json, file="data.json")Use cases? I wonder if RApache could be used to build an API that serves R data in JSON format? Posted in

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R Tutorial Series: Summary and Descriptive Statistics

November 1, 2009
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R Tutorial Series: Summary and Descriptive Statistics

Summary (or descriptive) statistics are the first figures used to represent nearly every dataset. They also form the foundation for much more complicated computations and analyses. Thus, in spite of being composed of simple methods, they are essential ...

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R Tutorial Series: Introduction to The R Project for Statistical Computing (Part 2)

October 15, 2009
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R Tutorial Series: Introduction to The R Project for Statistical Computing (Part 2)

Welcome to part two of the Introduction to The R Project for Statistical Computing tutorial. If you missed part one, it can be found here. In this segment, we will explore the following topics.Importing DataVariablesWorkspace FilesConsole FilesFinding ...

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R Tutorial Series: Introduction to The R Project for Statistical Computing (Part 1)

October 11, 2009
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R Tutorial Series: Introduction to The R Project for Statistical Computing (Part 1)

R is a free, cross-platform, open-source statistical analysis language and program. It is also an alternative to expensive commercial statistics software such as SPSS. The environment for R differs from the typical point and click interface found in mo...

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Delete rows from R data frame

October 8, 2009
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Delete rows from R data frame

Deleting rows from a data frame in R is easy by combining simple operations. Let’s say you are working with the built-in data set airquality and need to remove rows where the ozona is NA (also called null, blank or missing). The method is a conce...

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Introducing Influence.ME: Tools for detecting influential data in mixed models

April 29, 2009
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I’m highly excited to announce that influence.ME is now available. Influence.ME is a new software package for R, providing statistical tools for detecting influential data in mixed models. It has been developed by Rense Nieuwenhuis, Ben Pelzer, a...

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useR! 2009 acceptance: presenting influence.ME

April 23, 2009
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The organizing committee of the useR! 2009 conference just informed me, that my submission for presenting my extension package influence.ME, has been accepted! Influence.ME is a new R package that I’m currently developing, with the indispensable ...

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R-Sessions 32: Forward.lmer: Basic stepwise function for mixed effects in R

February 13, 2009
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Intended to be a customized solution, it may have grown to be a little more. forward.lmer is an early installment of a full stepwise function for mixed effects regression models in R-Project. I may put in some work to extend it, or I may not. Neverthel...

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R-Sessions 31: Combining lmer output in a single table (UPDATED)

February 5, 2009
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There are various ways of getting your output from R to your publication draft. Most of them are highly efficient, but unfortunately I couldn’t find a function that combines the output from several (lmer) models and presents it in a single table....

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R-Sessions 30: Visualizing missing values

January 8, 2009
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R-Sessions 30: Visualizing missing values

It always takes some time to get a grip on a new dataset, especially large ones. The code-books are often as indispensable as they are massive, and not always as clear as one would want. Routings, and resulting and strange patterns of missing values are at times difficult to find.I found a...

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