Month: June 2015

dendextend version 1.0.1 + useR!2015 presentation

When using the dendextend package in your work, please cite it using:

Tal Galili (2015). dendextend: an R package for visualizing, adjusting, and comparing trees of hierarchical clustering. Bioinformatics. doi:10.1093/bioinformatics/btv428

My R package dendextend (version 1.0.1) is now on CRAN!

The dendextend package Offers a set of functions for extending dendrogram objects in R, letting you visualize and compare trees of hierarchical clusterings. With it you can (1) Adjust a tree’s graphical parameters – the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different dendrograms to one another.

The previous release of dendextend (0.18.3) was half a year ago, and this version includes many new features and functions.

To help you discover how dendextend can solve your dendrogram/hierarchical-clustering issues, you may consult one of the following vignettes:

Here is an example figure from the first vignette (analyzing the Iris dataset)

iris_heatmap_dend

 

This week, at useR!2015, I will give a talk on the package. This will offer a quick example, and a step-by-step example of some of the most basic/useful functions of the package. Here are the slides:

 

Lastly, I would like to mention the new d3heatmap package for interactive heat maps. This package is by Joe Cheng from Rstudio, and integrates well with dendrograms in general and dendextend in particular (thanks to some lovely github-commit-discussion between Joe and I). You are invited to see lively examples of the package in the post at the RStudio blog. Here is just one quick example:

d3heatmap(nba_players, colors = “Blues”, scale = “col”, dendrogram = “row”, k_row = 3)

d3heatmap

Setting Rstudio server using Amazon Web Services (AWS) – a step by step (screenshots) tutorial

(this is a guest post by Liad Shekel)

Amazon Web Services (AWS) include many different computational tools, ranging from storage systems and virtual servers to databases and analytical tools. For us R-programmers, being familiar and experienced with these tools can be extremely beneficial in terms of efficiency, style, money-saving and more.

In this post we present a step-by-step screenshot tutorial that will get you to know Amazon EC2 service. We will set up an EC2 instance (Amazon virtual server), install an Rstudio server on it and use our beloved Rstudio via browser (all for free!). The slides below will also include an introduction to linux commands (basic), instructions for connecting to a remote server via ssh and more. No previous knowledge is required.

Useful links:

  1. Set up an AWS account (do not worry about the credit card details, you will not be charged for any of  our actions) – the steps are presented in the slides below.
  2. Windows users: download MobaXterm (or any other ssh client software).
    Mac users: make sure you are familiar with the terminal (cause I’m not).

 

R 3.2.1 is released

R 3.2.1 (codename “World-Famous Astronaut”) was released yesterday. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below.

Upgrading to R 3.2.1 on Windows

If you are using Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code in Rgui:

install.packages("installr") # install 
installr::updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). There is also a step by step tutorial (with screenshots) on how to upgrade R on Windows, using the installr package.

I try to keep the installr package updated and useful, so if you have any suggestions or remarks on the package – you are invited to open an issue in the github page.

CHANGES IN R 3.2.1:

 

NEW FEATURES

  • utf8ToInt() now checks that its input is valid UTF-8 and returns NA if it is not.
  • install.packages() now allows type = "both" with repos = NULL if it can infer the type of file.
  • nchar(x, *) and nzchar(x) gain a new argument keepNA which governs how the result for NAs in x is determined. For the R 3.2.x series, the default remains FALSE which is fully back compatible. From R 3.3.0, the default will change to keepNA = NA and you are advised to consider this for code portability.
  • news() more flexibly extracts dates from package ‘NEWS.Rd’ files.
  • lengths(x) now also works (trivially) for atomic x and hence can be used more generally as an efficient replacement of sapply(x, length) and similar.
  • The included version of PCRE has been updated to 8.37, a bug-fix release.
  • diag() no longer duplicates a matrix when extracting its diagonal.
  • as.character.srcref() gains an argument to allow characters corresponding to a range of source references to be extracted.

BUG FIXES

Continue reading “R 3.2.1 is released”

A step by step (screenshots) tutorial for upgrading R on Windows

tl;dr

If you are running R on Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code:

# installing/loading the latest installr package:
install.packages("installr"); library(installr) # install+load installr

updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). just press “next”, “OK”, and “Yes” on everything…

A GUI interface to updating R on Windows

Starting from installr version 0.15.0, the upgradingprocess can be done with a click-on-menus GUI interface. Here is how to use it.

Continue reading “A step by step (screenshots) tutorial for upgrading R on Windows”