Noam Ross recently shared a very useful guide to speeding up your R code. Get a bigger computer (for example, renting an instance on the Amazon cloud for a few cents an hour) Use parallel programming techniques Using the R byte-compiler Profiling and benchmarking your code Using high-performance packages (like xts, for time series) And lastly, rewriting your code...
R posts in other languages
- 适合初学者的R语言教程
- SAS hat einen neuen Vergleich von SAS und R veröffentlicht
- 实现可重复的统计slides
- О связи между “утечкой мозгов” и объемом пассажирских перевозок
- Новая книга по основам прогнозирования (с использованием R)
- Двухфакторный дисперсионный анализ
- Revolution R Enterprise 6.2 mit neuen Funktionen für hoch performante Analysen auf Big Data
- Third Milano R net meeting: leave a comment
- Photos of the third Milano R net meeting
- Presentations of the third Milano R net meeting
Tutorials on git/github and GNU make
If you’re not using version control, you should be. Learn git. If you’re not on github, you should be. That’s real open source. To help some colleagues get started with git and github, I wrote a minimal tutorial. There are lots of git and github resources available, but I thought I’d give just the bare 
Using Interactive D3.js charts from within Tiki with rNVD3
I've recently managed to reproduce my first charts using the nice package rNVD3 from Ramnath Vaidyanathan:
https://github.com/ramnathv/rNVD3
This rNVD3 package uses NVD3, which provides re-usable charts with d3.js, without taking away the power that d3.js brings with itself. But let's make a "Hello world" type of example, with the minimum complexity in it.
For this, I...
Bubble sort implemented in pure R
Please note that this is programming I purely did for the learning experience. The pure R bubble sort implemented in this post is veeeeery slow for two reasons: Interpreted code with lots of iteration is very slow. Bubble sort is… See more ›
Package intergraph goes 2.0
Yesterday I submitted a new version (marked 2.0-0) of package ‘intergraph’ to CRAN. There are some major changes and bug fixes. Here is a summary: Main functions for converting network data between object classes “igraph” and “network” are now called asIgraph and asNetwork. There is a generic function asDF that converts network object to a
How R Grows – not so fast
I have had some work on CRAN stats on the back-burner but the recent article How R Grows tempted me to push it up the list In the interim, I have a couple of comments on Joseph Rickert`s article. Although the body of the article refers to packages either created or updated in a time
R is His Friend
(This article was first published on Econometrics Beat: Dave Giles' Blog, and kindly contributed to R-bloggers) Marcus Beck has a nice (& relatively new) blog called R is My Friend. You can guess that his posts relate to the use of R.I particularly liked his piece on the use of the XML package in R to mine data from the...
Trevor Hastie presents glmnet: lasso and elastic-net regularization in R
by Joseph Rickert Even a casual glance at the R Community Calendar shows an impressive amount of R user group activity throughout the world: 45 events in April and 31 scheduled so far for May. New groups formed last month in Knoxville, Tennessee (The Knoxville R User Group: KRUG) and Sheffield in the UK (The Sheffield R Users). An...
Version 0.9 of timeline on CRAN
The initial version of the timeline package has been released to CRAN. This package provides creates timeline plots using ggplot2 in a style similar to Preceden. I would considered this beta quality as there are more features I would like to add but has enough functionality to possibly be useful to others.
install.packages('timeline',repos='http://cran.r-project.org')
require(timeline)
data(ww2)
timeline(ww2, ww2.events, event.spots=2, event.label='', event.above=FALSE)Read more »
A Shiny web app to find out how much medical procedures cost in your state.
Today the front page of the Huffington Post featured the new data available from the CMS that shows the cost of many popular procedures broken down by hospital. We here at Simply Statistics think you should be able to explore … Continue reading →
SAS, SPSS, Stata Users: Learn R from Home June 17
Has learning R been driving you a bit crazy? If so, it may be that you’re “lost in translation.” On June 17 and 19, I’ll be teaching a webinar, R for SAS, SPSS and Stata Users. With each R concept, … Continue reading →
What’s new in Revolution R Enterprise 6.2 (video)
If you missed last week's webinar, product manager Thomas Dinsmore shared details of the new features in Revolution R Enterprise 6.2 in the video below: You can also download slides of the presentation at the link below. Revolution Analytics webinars: What's New in Revolution R Enterprise 6.2
Computed columns for dataframes
Everyone loves to aggregate data. Everyone loves to create new columns based on other columns. Everyone hates to do the same thing twice. In my continuing work on multilevel view of loss reserving, I reached a point where I realized that I needed a robust mechanism to aggregate computed columns. SQL server and (I’m assuming) 
Feature Selection 2 – Genetic Boogaloo
Previously, I talked about genetic algorithms (GA) for feature selection and illustrated the algorithm using a modified version of the GA R package and simulated data. The data were simulated with 200 non-informative predictors and 12 linear effects and three non-linear effects. Quadratic discriminant analysis (QDA) was used to model the data. The last set of...
Building a custom database of country time-series data using Quandl
Encouraged by this post I had another look at quandl for collecting datasets from different agencies. Right now I need to get data for four countries on a couple of dozen indicators. This graphic is just a quick example with only two indicators of what I am aiming to be able to do. The process
An accept-reject sampler using RcppArmadillo::sample()
The recently added RcppArmadillo::sample() functionality provides the same algorithm used in R’s sample() to Rcpp-level code. Because R’s own sample() is written in C with minimal work done in R, writing a wrapper around RcppArmadillo::sample() to then call in R won’t get you much of a performance boost. However, if you need to repeatedly call sample(), then calling a...
An accept-reject sampler using RcppArmadillo::sample()
The recently added RcppArmadillo::sample() functionality provides the same algorithm used in R’s sample() to Rcpp-level code. Because R’s own sample() is written in C with minimal work done in R, writing a wrapper around RcppArmadillo::sample() to then call in R won’t get you much of a performance boost. However, if you need to repeatedly call sample(), then calling a...
Gambler’s Run With Shiny
I finally had an opportunity to play with Shiny, and I am very impressed. I have created a Github Project so head over there for the source code. There are a number of ways to distribute Shiny apps. If you are running R (and mostly likely you are if you are reading this), you can download and...
AIC & BIC vs. Crossvalidation
Model selection is a process of seeking the model in a set of candidate models that gives the best balance between model fit and complexity (Burnham & Anderson 2002). I have always used AIC for that. But you can also…
CAISN
Reblogged from Zero to R Hero: Canadian Aquatic Invasive Species Networks Annual General Meeting in Kananaskis, Alberta. May 03, 3:25-5:30. This 2-hour workshop will focus on how and why we do numerical simulation in R. Time permitting, we will also look at how to build and fit likelihood based statistical models. We ask that you bring your 
New geomorph function to digitize multiple 2d images
(This article was first published on geomorph, and kindly contributed to R-bloggers) Hi Morphometricians! We've enhanced geomorph's ability to continuously digitize multiple specimens' images in 2d, if these are within the same directory. This new function allows one to digitize 2d images without interruption. Thanks to Samuel Brown and Karl Fetter for suggesting the improvement. We will incorporate this function in our next...
Poisson regression on non-integers
In the course on claims reserving techniques, I did mention the use of Poisson regression, even if incremental payments were not integers. For instance, we did consider incremental triangles > source("http://perso.univ-rennes1.fr/arthur.charpentier/bases.R") > INC=PAID > INC=PAID-PAID > INC 3209 1163 39 17 7 21 3367 1292 37 24 10 NA 3871...
R in Insurance: Programme and Abstracts published
I am delighted to announce that the programme and abstracts for the first R in Insurance conference at Cass Business School in London, 15 July 2013, have been published. The conference committee received strong abstracts from academia and the industry,...
SAS Big Data Analytics Benchmark (Part Two)
by Thomas Dinsmore On April 26, SAS published on its website an undated Technical Paper entitled Big Data Analytics: Benchmarking SAS, R and Mahout. In the paper, the authors (Allison J. Ames, Ralph Abbey and Wayne Thompson) describe a recent project to compare model quality, product completeness and ease of use for two SAS products together with open source...
Eigen-analysis of Linear Model Behavior in R
This post is actually about replicating the figures in Otto and Day: A Biologist’s Guide to Mathematical Modeling in Ecology and Evolution. The figures I’m interested in for this post are Figures 9.1 and 9.2 in the chapter ‘General Solutions … Continue reading →
DataMind & The R Service Bus @ RBelgium
Within 2 weeks on Friday, May 24, The RBelgium R user group is holding its next Regular meeting in Leuven for which this is the schedule: ** Jonathan Cornelissen - DataMind Discover DataMind, a new online learning platform for d...
Subsetting data
At School we use R across many courses, because students are supposed to use statistics under a variety of contexts. Imagine their disappointment when they pass stats and discovered that R and statistics haven’t gone away! When students start working … Continue reading →
Passing columns of a dataframe to a function without quotes
I love the syntax of calls to lm and ggplot, wherein the dataframe is specified as a variable and specific columns are referenced as though they were separate variables. While developing some of my functions, I’d wanted to introduce something similar. I often find that I have a single large dataframe and want to execute 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
