R-users.com: invite fellow R-users to Jobs, conferences, and R-projects

Dear R users,

I am happy to officially announce a new website called R-users.com. The idea of the site is that community members will invite other R users to join them in their R projects, conferences, and work places.

R-users_homepage_screeshot

This site is a “job board” for R users, hosting various “call to action” to R-users, to do stuff such as:

  1. Join a open-source or paid projects of R programming
  2. Send/give a presentation for conferences (on R, statistics, machine learning, data science, etc.)
  3. Apply to be a student/researcher in an academic institution
  4. And other “R jobs”

For example, I am the author of the R package “installr” for easily updating R on windows. However, I would love for someone who is a mac/linux user to expend my package for non-Windows users. Hence, I created a new “job”, inviting help on this project, which you may see in this link.

If you also wish to post your own “R job” for other R-users to see, here is a very short presentation on how to do it:

The basic steps are:

  1. Register/login to the site (you can use your facebook/gmail account with just one click-registration)
  2. Fill in your proposed project/job details
  3. That’s it!

I intend to promote this site on r-bloggers.com, please help me in promoting this site on facebook and your own websites – so that more of us will be able to work together.

Yours,
Tal Galili

Plotly Beta: Collaborative Plotting with R

(Guest post by Matt Sundquist on a lovely new service which is pro-actively supporting an API for R)

The Plotly R graphing library  allows you to create and share interactive, publication-quality plots in your browser. Plotly is also built for working together, and makes it easy to post graphs and data publicly with a URL or privately to collaborators.

In this post, we’ll demo Plotly, make three graphs, and explain sharing. As we’re quite new and still in our beta, your help, feedback, and suggestions go a long way and are appreciated. We’re especially grateful for Tal’s help and the chance to post.

Installing Plotly

Sign-up and Install (more in documentation)

From within the R console:

install.packages("devtools")
library("devtools")

Next, install plotly (a big thanks to Hadley, who suggested the GitHub route):

devtools::install_github("plotly/R-api")
# ...
# * DONE (plotly)

Then sign-up like this or at https://plot.ly/:

>library(plotly)
>response = signup (username = 'username', email= 'youremail')
…
Thanks for signing up to plotly! 
 
Your username is: MattSundquist
 
Your temporary password is: pw. You use this to log into your plotly account at https://plot.ly/plot. Your API key is: “API_Key”. You use this to access your plotly account through the API.
 
To get started, initialize a plotly object with your username and api_key, e.g. 
>>> p < - plotly(username="MattSundquist", key="API_Key")
Then, make a graph!
>>> res < - p$plotly(c(1,2,3), c(4,2,1))

And we’re up and running! You can change and access your password and key in your homepage.

1. Overlaid Histograms:

Here is our first script.

library("plotly")
p < - plotly(username="USERNAME", key="API_Key")
 
x0 = rnorm(500)
x1 = rnorm(500)+1
data0 = list(x=x0,
             type='histogramx',
opacity=0.8)
data1 = list(x=x1,
             type='histogramx',
opacity=0.8)
layout = list(barmode='overlay')  
 
response = p$plotly(data0, data1, kwargs=list(layout=layout)) 
 
browseURL(response$url)

The script makes a graph. Use the RStudio viewer or add “browseURL(response$url)” to your script to avoid copy and paste routines of your URL and open the graph directly.

image001

Continue reading Plotly Beta: Collaborative Plotting with R

R-bloggers: an example of how interest networks propel viral events

A guest post by Jeff Hemsley, who has co-authored with Karine Nahon a new book titled Going Viral.
————————-

In Going Viral (Polity Press, 2013) we explore the topic of virality, the process of sharing messages that results in a fast, broad spread of information. What does that have to do R, or the R-bloggers community? First and foremost, we use the R-bloggers community as an example of the role of interest networks (see description below) in driving viral events. But we also used R as our go-to tool for our research that went into the book. Even the cover art, pictured here, was created with R, using the iGraph package. Included below is an excerpt from chapter 4 that includes the section on interest networks and R-bloggers.

GoingViral

Continue reading R-bloggers: an example of how interest networks propel viral events

R 3.0.2 and RStudio 0.9.8 are released!

R 3.0.2 (codename “Frisbee Sailing”) was released yesterday. The full list of new features and bug fixes is provided below.

Also, RStudio v0.98 (in a “secret” preview) was announced two days ago with MANY new features, including:

Upgrading to R 3.0.2

You can download the latest version from here. Or, if you are using Windows, you can upgrade to the latest version using the installr package (also available on CRAN and github). Simply run the following code:

# installing/loading the package:
if(!require(installr)) { 
install.packages("installr"); require(installr)} #load / install+load installr
 
updateR(to_checkMD5sums = FALSE) # the use of to_checkMD5sums is because of a slight bug in the MD5 file on R 3.0.2. This issue is already resolved in the installr version on github, and will be released into CRAN in about a month from now..

I try to keep the installr package updated and useful. If you have any suggestions or remarks on the package, you’re invited to leave a comment below.

If you use the global library system (as I do), you can run the following in the new version of R:

source("http://www.r-statistics.com/wp-content/uploads/2010/04/upgrading-R-on-windows.r.txt")
New.R.RunMe()

p.s: you can also use the installr package to quickly install the new RStudio by using:

# installing/loading the package:
if(!require(installr)) { 
install.packages("installr"); require(installr)} #load / install+load installr
 
install.RStudio()

Continue reading R 3.0.2 and RStudio 0.9.8 are released!

A speed test comparison of plyr, data.table, and dplyr

ssssssspeed_521872450_d085d1e928

Guest post by Jake Russ

For a recent project I needed to make a simple sum calculation on a rather large data frame (0.8 GB, 4+ million rows, and ~80,000 groups). As an avid user of Hadley Wickham’s packages, my first thought was to use plyr. However, the job took plyr roughly 13 hours to complete.

plyr is extremely efficient and user friendly for most problems, so it was clear to me that I was using it for something it wasn’t meant to do, but I didn’t know of any alternative screwdrivers to use.

I asked for some help on the manipulator Google group , and their feedback led me to data.table and dplyr, a new, and still in progress, package project by Hadley.

What follows is a speed comparison of these three packages incorporating all the feedback from the manipulator folks. They found it informative, so Tal asked me to write it up as a reproducible example.

Continue reading A speed test comparison of plyr, data.table, and dplyr

K-means Clustering (from “R in Action”)

In R’s partitioning approach, observations are divided into K groups and reshuffled to form the most cohesive clusters possible according to a given criterion. There are two methods—K-means and partitioning around mediods (PAM). In this article, based on chapter 16 of R in Action, Second Edition, author Rob Kabacoff discusses K-means clustering.

Until Aug 21, 2013, you can buy the book: R in Action, Second Edition with a 44% discount, using the code: “mlria2bl”.

K-means clustering

The most common partitioning method is the K-means cluster analysis. Conceptually, the K-means algorithm:

  1. Selects K centroids (K rows chosen at random)
  2. Assigns each data point to its closest centroid
  3. Recalculates the centroids as the average of all data points in a cluster (i.e., the centroids are p-length mean vectors, where p is the number of variables)
  4. Assigns data points to their closest centroids
  5. Continues steps 3 and 4 until the observations are not reassigned or the maximum number of iterations (R uses 10 as a default) is reached.

Implementation details for this approach can vary.

R uses an efficient algorithm by Hartigan and Wong (1979) that partitions the observations into k groups such that the sum of squares of the observations to their assigned cluster centers is a minimum. This means that in steps 2 and 4, each observation is assigned to the cluster with the smallest value of:

equation_1_RinAction2CH16

Where k is the cluster,xij is the value of the jth variable for the ith observation, and xkj-bar is the mean of the jth variable for the kth cluster.

Continue reading K-means Clustering (from “R in Action”)

Analyzing Your Data on the AWS Cloud (with R)

Guest post by Jonathan Rosenblatt

Disclaimer:
This post is not intended to be a comprehensive review, but more of a “getting started guide”. If I did not mention an important tool or package I apologize, and invite readers to contribute in the comments.

Introduction

I have recently had the delight to participate in a “Brain Hackathon” organized as part of the OHBM2013 conference. Being supported by Amazon, the hackathon participants were provided with Amazon credit in order to promote the analysis using Amazon’s Web Services (AWS). We badly needed this computing power, as we had 14*109 p-values to compute in order to localize genetic associations in the brain leading to Figure 1.

Figure 1- Brain volumes significantly associated to genotype.
brain_image01

While imaging genetics is an interesting research topic, and the hackathon was a great idea by itself, it is the AWS I wish to present in this post. Starting with the conclusion: 

Storing your data and analyzing it on the cloud, be it AWSAzureRackspace or others, is a quantum leap in analysis capabilities. I fell in love with my new cloud powers and I strongly recommend all statisticians and data scientists get friendly with these services. I will also note that if statisticians do not embrace these new-found powers, we should not be surprised if data analysis becomes synonymous with Machine Learning and not with Statistics (if you have no idea what I am talking about, read this excellent post by Larry Wasserman).

As motivation for analysis in the cloud consider:

  1. The ability to do your analysis from any device, be it a PC, tablet or even smartphone.
  2. The ability to instantaneously augment your CPU and memory to any imaginable configuration just by clicking a menu. Then scaling down to save costs once you are done.
  3. The ability to instantaneously switch between operating systems and system configurations.
  4. The ability to launch hundreds of machines creating your own cluster, parallelizing your massive job, and then shutting it down once done.

Here is a quick FAQ before going into the setup stages.

FAQ

Q: How does R fit in?

Continue reading Analyzing Your Data on the AWS Cloud (with R)

Tailor Your Tables with stargazer: New Features for LaTeX and Text Output

Guest post by Marek Hlavac

Since its first introduction on this blog, stargazer, a package for turning R statistical output into beautiful LaTeX and ASCII text tables, has made a great deal of progress. Compared to available alternatives (such as apsrtable or texreg), the latest version (4.0) of stargazer supports the broadest range of model objects. In particular, it can create side-by-side regression tables from statistical model objects created by packages AER, betareg, dynlm, eha, ergm, gee, gmm, lme4, MASS, mgcv, nlme, nnet, ordinal, plm, pscl, quantreg, relevent, rms, robustbase, spdep, stats, survey, survival and Zelig.  You can install stargazer from CRAN in the usual way:

install.packages(“stargazer”)

New Features: Text Output and Confidence Intervals

In this blog post, I would like to draw attention to two new features of stargazer that make the package even more useful:

  • stargazer can now produce ASCII text output, in addition to LaTeX code. As a result, users can now create beautiful tables that can easily be inserted into Microsoft Word documents, published on websites, or sent via e-mail. Sharing your regression results has never been easier. Users can also use this feature to preview their LaTeX tables before they use the stargazer-generated code in their .tex documents.
  • In addition to standard errors, stargazer can now report confidence intervals at user-specified confidence levels (with a default of 95 percent). This possibility might be especially appealing to researchers in public health and biostatistics, as the reporting of confidence intervals is very common in these disciplines.

In the reproducible example presented below, I demonstrate these two new features in action.

 

Reproducible Example

I begin by creating model objects for two Ordinary Least Squares (OLS) models (using the lm() command) and a probit model (using glm() ). Note that I use data from attitude, one of the standard data frames that should be provided with your installation of R.

## 2 OLS models
 
linear.1 < - lm(rating ~ complaints + privileges + learning + raises + critical, data=attitude)
linear.2 <- lm(rating ~ complaints + privileges + learning, data=attitude)
 
## create an indicator dependent variable, and run a probit model
 
attitude$high.rating <- (attitude$rating > 70)
probit.model < - glm(high.rating ~ learning + critical + advance, data=attitude, family = binomial(link = "probit"))

I then use stargazer to create a ‘traditional’ LaTeX table with standard errors. With the sole exception of the argument no.space – which I use to save space by removing all empty lines in the table – both the command call and the resulting table should look familiar from earlier versions of the package:

stargazer(linear.1, linear.2, probit.model, title="Regression Results", align=TRUE, dep.var.labels=c("Overall Rating","High Rating"), covariate.labels=c("Handling of Complaints","No Special Privileges", "Opportunity to Learn","Performance-Based Raises","Too Critical","Advancement"), omit.stat=c("LL","ser","f"), no.space=TRUE)

table_example_1

Continue reading Tailor Your Tables with stargazer: New Features for LaTeX and Text Output

Statistics with R, and open source stuff (software, data, community)