It’s dark outside and all you want is to hit the road for some well-deserved miles. Our Tracer360 uses the full color spectrum in specially designed illumination modes to keep you highly visible. Paired with high quality 3M reflective materials the Tracer360 is the perfect, lightweight replacement for your bulky and uncomfortable safety vest.
Want it for motorcycling, will start running just to be able to wear it.
Thanks for the link M. Arnott via Facebook.
rbenchmark is inspired by the Perl module Benchmark, and is intended to facilitate benchmarking of arbitrary R code. The library consists of just one function, benchmark, which is a simple wrapper around system.time. Given a specification of the benchmarking process (counts of replications, evaluation environment) and an arbitrary number of expressions, benchmark evaluates each of the expressions in the specified environment, replicating the evaluation as many times as specified, and returning the results conveniently wrapped into a data frame.
This is an absolute must have for every situation where you want to do some
simple benchmarking.
For some reason when I read the documentation it sounds harder to use then it
really is.
Give it a try and you will be rewarded greatly.
This package provides many approaches for both reading and creating XML (and HTML) documents (including DTDs), both local and accessible via HTTP or FTP. It also offers access to an XPath “interpreter”.
Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.
This package is a fork of the RJSONIO package by Duncan Temple Lang. It builds on the same libjson c++ parser, but implements a smarter mapping between JSON data and R classes. This is particularly useful when working with JSON data from pipelines and web APIs. The vignettes describe the behavior in great detail. In addition to drop-in replacements for toJSON and fromJSON, the package contains functions to validate, prettify and minify JSON, and many unit tests to verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications.
This package does what ESS does for R for various REPLs, including ielm.
Emacs Speaks Statistics (ESS) package has a nice function called ess-eval-region-or-line-and-step, which is assigned to C-RET. This function sends a line or a selected region to the corresponding shell (R, Julia, Stata, etc) visibly. It also start up a shell if there is none.
This package implements similar work flow for various read-eval-print-loops (REPLs)
Via ess-help.
For experienced programmers, Advanced R will serve you as an excellent resource
to learn about specific aspects of R in a comprehensive manner, in addition to
digging through the individual (and wonderful) documentation entries inside
of R.
Getting through the book requires experience with R to make sense of it, so it
may not be the best place to start. However, an experienced programmer will have
questions that only this book addresses directly and easily. As such, using it
as-needed for reference is a fine approach.
yhat posted a nice detailed and light overview.
This volume takes a positive spin on the field of statistics. Statistics is seen by students as difficult and boring, however, the authors of this book have eliminated that theory. Teaching Statistics: A Bag Of Tricks, brings together a complete set of examples, demonstrations and projects that not only will increase class participation but will help to eliminate any negative feelings toward the area of statistics.
Research in the areas of psychology, statistical education, and mathematics education is reviewed
and the results applied to the teaching of college-level statistics courses. The argument is made that
statistics educators need to determine what it is they really want students to learn, to modify their
teaching according to suggestions from the research literature, and to use assessment to determine if
their teaching is effective and if students are developing statistical understanding and competence.
