2839 search results for "twitteR"

Using Text Mining to Find Out What @RDataMining Tweets are About

November 8, 2011
By
Using Text Mining to Find Out What @RDataMining Tweets are About

This post shows an example on text mining of Twitter data with R packages twitteR, tm and wordcloud. Package twitteR provides access to Twitter data, tm provides functions for text mining, and wordcloud visualizes the result with a word cloud. … Continue reading →

Read more »

Bridge and Torch problem in R

November 8, 2011
By
Bridge and Torch problem in R

A couple months ago I came across the bridge and torch problem at a careers fair in Oxford. A young tech company called QuBit used it as a brain teaser challenge for would be software engineers to solve before submitting … Continue reading →

Read more »

Doing away with “unknown timezone” warnings

November 8, 2011
By
Doing away with “unknown timezone” warnings

Timezone stuff can really drive you NUTS - at least if you’re sitting in front of a German Windows-Box This is what I used to do to set my tz: And I always wondered why R would throw “unknown timezone” warnings: Someday I found out that setting tz via `options()` was not enough as the … Continue reading...

Read more »

ABC on wordpress

November 7, 2011
By
ABC on wordpress

Erkan Buzbas sent me an email about his webpage (operated as a wordpress blog) on ABC. It contains different items of information on ABC research and an hopefully growing list of references. After Scott Sisson’s tweet on ABC_research (latest news: two ABC sessions in ISBA 20122, Kyoto),  here comes another way to keep posted about

Read more »

Web Scraping Google URLs

November 7, 2011
By
Web Scraping Google URLs

Google slightly changed the html code it uses for hyperlinks on search pages last Thursday, thus causing one of my scripts to stop working. Thankfully, this is easily solved in R thanks to the XML package and the power and simplicity of XPath expressions: Lovely jubbly! P.S. I know that there is an API of

Read more »

Code Optimization: One R Problem, Eleven Solutions – Now Thirteen!

November 7, 2011
By
Code Optimization: One R Problem, Eleven Solutions – Now Thirteen!

Following up from my previous post “Code Optimisation: One R Problem, Ten Solutions – Now Eleven!” I figured out a twelfth solution after writing that blog post. Furthermore, half way through writing this blog post I figured out a thirteenth solution too. As a recap, the problem is taken from rwiki where the goal is to find

Read more »

Bayesian modeling using WinBUGS

November 6, 2011
By
Bayesian modeling using WinBUGS

Yes, yet another Bayesian textbook: Ioannis Ntzoufras’ Bayesian modeling using WinBUGS was published in 2009 and it got an honourable mention at the 2009 PROSE Award. (Nice acronym for a book award! All the mathematics books awarded that year were actually statistics books.) Bayesian modeling using WinBUGS is rather similar to the more recent Bayesian

Read more »

The Joy of R: A Feline Guide

November 5, 2011
By
The Joy of R: A Feline Guide

Just because it’s caturday Images by Mario Pineda-Krch (CC BY-NC-SA 3.0) This is from the “Mario’s Entangled Bank” blog ( http://pineda-krch.com ) of Mario Pineda-Krch, a theoretical biologist at the University of Alberta. Filed under: cats, computing, humour, R, Sweave

Read more »

Data Referenced Journalism and the Media – Still a Long Way to Go Yet?

November 4, 2011
By
Data Referenced Journalism and the Media – Still a Long Way to Go Yet?

Reading our local weekly press this evening (the Isle of Wight County Press), I noticed a page 5 headline declaring “Alarm over death rates at St Mary’s”, St Mary’s being the local general hospital. It seems a Department of Health report on hospital mortality rates came out earlier this week, and the Island’s hospital, it

Read more »

Confidence interval for predictions with GLMs

November 4, 2011
By
Confidence interval for predictions with GLMs

Consider a (simple) Poisson regression . Given a sample where , the goal is to derive a 95% confidence interval for given , where is the prediction. Hence, we want to derive a confidence interval for the prediction, not the potential observation, i.e. the dot on the graph below > r=glm(dist~speed,data=cars,family=poisson) > P=predict(r,type="response", + newdata=data.frame(speed=seq(-1,35,by=.2))) > plot(cars,xlim=c(0,31),ylim=c(0,170)) > abline(v=30,lty=2)...

Read more »

Sponsors

Mango solutions



RStudio homepage



Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training



http://www.eoda.de









ODSC

CRC R books series













Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)