background preloader

R packages

Facebook Twitter

Janitor (Tools for Examining and Cleaning Dirty Data)

FactomineR - factoextra. Startup. Bookdown. MailR. Shinythemes 1.1.1. If there’s one word that could describe the default styling of Shiny applications, it might be “minimalist.”

shinythemes 1.1.1

Shiny’s UI components are built using the Bootstrap web framework, and unless the appearance is customized, the application will be mostly white and light gray. Fortunately, it’s easy to add a bit of flavor to your Shiny application, with the shinythemes package. We’ve just released version 1.1.1 of shinythemes, which includes many new themes from bootswatch.com, as well as a theme selector which you can use to test out different themes on a live Shiny application. Here’s an example of the theme selector in use (try out the app here): To install the latest version of shinythemes, run: install.packages("shinythemes")

Hunspell

rSymPy. Pkgsnap. The magrittr monad – Win-Vector Blog. Posted on Author John MountCategories Computer ScienceTags programming patterns, R Monads are a formal theory of composition where programmers get to invoke some very abstract mathematics (category theory) to argue the minutia of annotating, scheduling, sequencing operations, and side effects.

The magrittr monad – Win-Vector Blog

On the positive side the monad axioms are a guarantee that related ways of writing code are in fact substitutable and equivalent; so you want your supplied libraries to obey such axioms to make your life easy. On the negative side, the theory is complicated. Purrr. XLConnect. Caret.

Profvis (interactive HTML code profiling)

Google trends. Lubridate / Anytime. Fitting Distributions. SparkR. Showtext. Shinyjs. Ggextra. Radiant. DiagrammeR. HtmlTable. Get data out of excel and into R with readxl. I’m pleased to announced that the first version of readxl is now available on CRAN.

Get data out of excel and into R with readxl

Readxl makes it easy to get tabular data out of excel. It: Supports both the legacy .xls format and the modern xml-based .xlsx format. .xls support is made possible the with libxls C library, which abstracts away many of the complexities of the underlying binary format.

Tidyr

Data.table. Stringr. Magrittr 1.5. (Posted on behalf of Stefan Milton Bache) Sometimes it’s the small things that make a big difference.

magrittr 1.5

For me, the introduction of our awkward looking friend, %>%, was one such little thing. Rvest: easy web scraping with R. Rvest is new package that makes it easy to scrape (or harvest) data from html web pages, inspired by libraries like beautiful soup.

rvest: easy web scraping with R

It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces. Install it with: install.packages("rvest") GTrendsR package to Explore Google trending for Field Dependent Terms. (This article was originally published at TRinker's R Blog, and syndicated at StatsBlogs.)

GTrendsR package to Explore Google trending for Field Dependent Terms

My friend, Steve Simpson, introduced me to Philippe Massicotte and Dirk Eddelbuettel’s GTrendsR GitHub package this week. It’s a pretty nifty wrapper to the Google Trends API that enables one to search phrase trends over time. The trend indices that are given are explained in more detail here:

Stringi

Squash - multivariate visualization package for R. Squash is an add-on package for the R statistical environment.

squash - multivariate visualization package for R

This package provides functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps. Lower-level functions are provided to map numeric values to colors, display a matrix as an array of colors, and draw color keys. Higher-level plotting functions are provided to generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more. The current version is 1.0.6 (2014-08-04).

Slackr

RStudio Blog. SjPlot. Rgitbook. Mosaic. Package: mosaic Type: Package Title: Project MOSAIC (mosaic-web.org) statistics and mathematics teaching utilities Version: 0.6-2 Date: 2012-09-03 Depends: R (>= 2.10), lattice, grid, methods, survival, Hmisc, utils Suggests: MASS, reshape2, manipulate, vcd, RCurl, testthat Author: Randall Pruim <rpruim@calvin.edu>, Daniel Kaplan <kaplan@macalester.edu>, Nicholas Horton <nhorton@smith.edu> Maintainer: Randall Pruim <rpruim@calvin.edu> Description: Data sets and utilities from Project MOSAIC (mosaic-web.org) used to teach mathematics, statistics, computation and modeling.

mosaic

Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all. Install log. Announcing dtupdate v1.0 - R Package Reporter/Updater for the devtools Universe. MissMech. WRS - Robust statistics.

Today a new version (0.23.1) of the WRS package (Wilcox’ Robust Statistics) has been released.

WRS - Robust statistics

This package is the companion to his rather exhaustive book on robust statistics, “Introduction to Robust Estimation and Hypothesis Testing” (Amazon Link de/us). For a fail-safe installation of the package, follow this instruction.

Dplyr

Reshape2. Deducer-Quick-Start-Guide. Latex output. XLConnect. PowerBuilder and R get together. The other day I was thinking about writing a blog using PowerBuilder, but couldn't decide which one other technology I should integrate it...of course...R came to my mind...

PowerBuilder and R get together

My journey started around 4 days ago...when I start looking for ways to call R from an external language...last time I used Rook and Heroku to call R from SAP Mobile Platform as explained in my blog Consuming R from SAP Mobile Platform, but this time I knew that I needed to do something different. My first thought was to use Rserve which is an R Server used by the SAP HANA Studio to connect to R as explained in my blog When SAP HANA met R - First kiss, so I downloaded the REngine files which are two .jar Java files. In order to connect to Rserve we need to set it up...so basically with the Rserve package installed on my RStudio I simply need to create a small file... When you run this...the R Server will start as a process that can be seen in the Task Manager. Let's take a look at the layout... Updating R through a menu-bar: installr. In preparation for the upcoming release of R 3.0.0, a new release 0.9 of installr is now on CRAN. The package can be installed and loaded using: The new version includes various bug fixes (as can be seen in the NEWS file) and new functions and features.

The most user visible feature is that from now on, whenever loading installr in the Rgui, it will add a new menu-bar for updating your R version (the menu is removed when the package is detached). When choosing to update R, a new GUI based system will guide you step by step through the updating process. It will first check if a newer version of R is available, if so, it will offer to show the latest NEWS of that release, download and install the new version, and copy/move your packages from the previous library folder, to the one in the new installation.

I hope you’ll enjoy this new release, and as always – please let me know in the comments (or via e-mail) if you come across any bugs or have suggestions for new features. Installing & Using R on Amazon EC2. If you’re an R user, you’ve surely heard all the hype around ‘big data’ and how R is commonly used to analyze these volumes of data. One thing that’s often missing from the discussion is HOW to work around issues using big data and R, specifically how to deal with the fact that R stores all its objects in-memory. While you can use packages such as ff and bigmemory to overcome the in-memory limits of your local machine, these additional packages do require some re-engineering of your code. Instead, consider using Amazon EC2 to provision the resources you need. A Guide to Speeding Up R Code. This is an overview of tools for speeding up your R code that I wrote for the Davis R Users’ Group.

First, Ask “Why?” It’s customary to quote Donald Knuth at this point, but instead I’ll quote my twitter buddy Ted Hart to illustrate a point: I’m just going to say it.I like for loops in #Rstats, makes my code readable.All you [a-z]*ply snobs can shove it! — Ted Hart (@DistribEcology) March 12, 2013. New in forecast 5.0. Fastclust - Fast hierarchical clustering.