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.
devtools: Tools to make developing R code easier
They do and it is better for you to read about them before you need them.
ADDENDUM: 2014-09-06T09:08:01
devtools: Tools to make developing R code easier
That is a bit too terse. Intro to the README follows
The aim of devtools is to make your life as a package developer easier by providing R functions that simplify many common tasks. R packages are actually really simple, and with the right tools it should be easier to use the package structure than not. Package development in R can feel intimidating, but devtools does every thing it can to make it as welcoming as possible. devtools comes with a small guarantee: if because of a bug in devtools a member of R-core gets angry with you, I will send you a handwritten apology note. Just forward me the email and your address, and I’ll get a card in the mail.
Excellent.
At the very least, just know of this package, as you will be installing it if
you want to us tidyr.
install.packages("devtools")
devtools::install_github("devtools")
library(devtools)
plyr is a set of tools for a common set of problems: you need to split up a big data structure into homogeneous pieces, apply a function to each piece and then combine all the results back together.
dplyr is the next iteration of plyr, focussed on tools for working with data frames (hence the d in the name). It has three main goals:
- Identify the most important data manipulation tools needed for data analysis
and make them easy to use from R.- Provide blazing fast performance for in-memory data by writing key pieces in
C++.- Use the same interface to work with data no matter where it’s stored, whether
in a data frame, a data table or database.
These two are a couple of the other mainstream manipulation tools outside of
base R.
data.table is a nice option for retaining the familiarity of a dataframe while
opening the door for pass-by-reference semantics and a more SQL-like query
language. The literature is really wonderful too providing all levels of detail
ranging from the 10-minute introduction to the entirety of the API itself.
The 10-minute introduction is really revealing of things that you would probably
enjoy in your personal analytical workflow whether you obtain them with
data.table or elsewhere. The short introduction is also revealing that whatever
solution you choose requires sincere and focused mastery in order to truly
utilize its power without making major, major mistakes.
Resources:
crantastic, a community site for R packages where you can search for, review and tag CRAN packages.
The keyword there is review. That is very helpful to get a sense of what packages are valued by the community because they may also be valuable to you.