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R 3.0.0 is released! (what's new, and how to upgrade) - R-Statistics blog

R 3.0.0 is released! (what's new, and how to upgrade) - R-Statistics blog | Statistics with R | Scoop.it
A few hours ago Peter Dalgaard (of R Core Team) announced the release of R 3.0.0!  Bellow you can read the changes in this release. One of the features
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Some useful information about changes in new version 3.0 by R Statistics Blog

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R & Bioconductor - Manuals

R & Bioconductor - Manuals | Statistics with R | Scoop.it
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Very impressive and a very good overview - everything about R with and without relation to Bioconductor on a glance!

 

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Handling_and_Processing_Strings_in_R.pdf

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David Smith from Revolution recently reported in a blog post (http://blog.revolutionanalytics.com/2014/03/strings-in-r-free-ebook.html) about this wonderful free ebook from Gaston Sanchez (http://gastonsanchez.com/Handling_and_Processing_Strings_in_R.pdf) about handling and processing strings in R.

 

Thanks David for posting that and a big thank you to Gaston for sharing this with us!

 

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Making Maps with R | The Molecular Ecologist

Making Maps with R | The Molecular Ecologist | Statistics with R | Scoop.it
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A nice overview - the comments are also very helpful!

 

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RTextTools

A core focus of the RTextTools project has been to make the package as accessible and user-friendly as possible. In its early iterations, the package contained dependencies such as RWeka , openNLP ,...
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RTextTools is an interesting package because it avoids using java

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Revolutions | R skills attract the highest salaries in the US

Revolutions | R skills attract the highest salaries in the US | Statistics with R | Scoop.it
Two recent salary surveys have shown that R language skills attract median salaries in excess of $110,000 in the United States. In the 2014 Dice Tech Salary Survey of over 17,000 technology professionals, the highest-paid IT skill was R programming. While big-data skills in general featured strongly in the top tier, having R at the top of the list reflects the strong demand for skills to make sense of, and extract value from big data. Similarly, the recent O'Reilly Data Scientist Survey also found R skills amongst those that pay in the $110,000-$125,000 range (albeit amongst a much smaller and...
Dr. Guido Möser's insight:

It is never too late to start learning R!

 

Thanks Revolution team!

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Flavio Barros's curator insight, February 24, 9:59 PM

Pelo menos no mercado americano é assim. Como será no Brasil daqui a alguns poucos anos?

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R - Generalized linear Models - Short Introduction | Princeton

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Interesting overview about glm in R

 

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R snippets: Tuning optim with parscale

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Interesting example using optim function and explaining parameter parscale. 

 

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Xianjun Dong | One Tip Per Day: How to see source code of a function/method in R?

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Also shows helpful S4 functions - showMethods() and getMethod() Thanks!
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Tips on Computing with Big Data in R | Revolution - Joseph Rickert

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Helpful Tipps from Revolution how to improve the speed of computations with Big Data in R

 

1. Upgrade your hardware

2. Upgrade your software

3. Minimize copies of the data

4. Process data in chunks 

5. Compute in parallel across cores or nodes

6. Take advantage of integers

7. Store data efficiently

8. Only read the data needed

9. Avoid loops when transforming data

10. Use C, C++, or Fortran for critical functions

11. Process data transformations in batches

12. Use row-oriented data transformations where possible

13. Handle categorical variables efficiently and with care

14. Be aware of output with the same number of rows as your input

15. Think Twice Before Sorting

 

Thanks Joseph and the Revolution Team!!

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OpenCPU - New package: jsonlite. A smart(er) JSON encoder/decoder.

This week we released a new package on CRAN: jsonlite. This package is a fork of RJSONIO by Duncan Temple Lang and builds on the same parser, but uses a different mapping between R objects and JSON data.
Dr. Guido Möser's insight:

Jeroen Ooms is presenting some interesting features in new jsonlite package, especially convert to and from json!

 

Thanks Jeroen!

 

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R snippets: Simulation speed: GNU R vs Julia

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R vs Julia - another speed comparison could be found directly on the official Julia page: http://julialang.org/

 

Seems promising to test it a little more in depth.

 

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Introduction to R Shiny: Building web apps in R Shiny for learning and visualization

Slides: http://files.meetup.com/1685538/IntroductionRShiny.pptx R Shiny, from the people behind R Studio, allows you to quickly and easily build basic web ap...
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Some very helpful information about building Shiny Apps! 

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Econometrics by Simulation: A Shiny App for Experimenting with Dynamic Programming

Econometrics by Simulation: A Shiny App for Experimenting with Dynamic Programming | Statistics with R | Scoop.it
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The SIR model in a Shiny app - code on GitHub:  https://github.com/EconometricsBySimulation/SIR-shiny/

 

The app can be found here: http://econometricsbysimulation.shinyapps.io/Dynamic-Pro/

 

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OpenIntro | Statistics Textbook

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OpenIntro is a free Statistics Textbook using R for examples

 

Download 2nd edition here: https://dl.dropboxusercontent.com/u/8602054/os2.pdf

 

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Read and write ESRI Shapefiles with R | NCEAS

Read and write ESRI Shapefiles with R | NCEAS | Statistics with R | Scoop.it
Dr. Guido Möser's insight:

A helpful introduction with R examples how to read in shapefiles in R -  the following libraries are discussed (with R examples):

 

rgdal

mapstools

PBSmapping

 

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R-python Integration | herrfz

Click here to edit the title

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An interesting ipython notebook showing R-Python integration. A short but interesting one.

 

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Jeromy Anglim's Blog | Getting Started with R Markdown, knitr, and Rstudio 0.96

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Short and excellent introduction in R Markdown, knitr, and RStudio 

 

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Noam Ross | Using Dates and Times in R

Noam Ross | Using Dates and Times in R | Statistics with R | Scoop.it
Dr. Guido Möser's insight:

Very hlupful overview over dealing with dates and times in R

 

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Cathy's curator insight, February 12, 12:11 PM

Great insight in R statistics

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Econometrics by Simulation: Using MongoHQ to build a Shiny Hit Counter

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Especially the integration of MongoDB in a Shiny Application is very interesting!

 

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Felix Schönbrodt | A short taxonomy of Bayes factors

Felix Schönbrodt | A short taxonomy of Bayes factors | Statistics with R | Scoop.it
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This is a very well written explanation with examples etc. of Bayesian Factors! 

 

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Stan: Project Home Page

Stan: Project Home Page | Statistics with R | Scoop.it
Stan modeling language and C++ library for Bayesian inference.
NUTS adaptive HMC (MCMC) sampling, automatic differentiation,
R, shell interfaces.
Dr. Guido Möser's insight:

Stan Home: Information, Interfaces, Manual & Examples

 

Interfaces:

RStan (R)

PyStan (Python)

CmdStan (Command-Line Terminal)

 

Thank you for this excellent software package!

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statsR4us | Murtaza Haider

Introduction to analytic intelligence with data analysis,such as logistic regression, logit models, discrete choice using SPSS, Stata, SAS, and R.
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A wide array of interesting statistical topics using R and other software packages. 

 

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Jared Knowles: Getting Started with Mixed Effect Models in R

Getting Started with Multilevel Modeling in R Getting Started with
Multilevel Modeling in R Jared E.
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lme4-package and arm ans some nice examples

 

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R-statistics blog: data.frames

R-statistics blog: data.frames | Statistics with R | Scoop.it
Writing about statistics with R, and open source stuff (software, data, community)
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Some useful blog entries for handling data.frames!

 

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"R" you ready? - Sending data from client to server and back using shiny

"R" you ready? - Sending data from client to server and back using shiny | Statistics with R | Scoop.it
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/s...
Dr. Guido Möser's insight:

Very helpful tipps to improve your Shiny applications. 

 

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