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.
A progress indicator for code blocks in org-mode courtesy
of John Kitchin:
;; give us some hint we are running (defadvice org-babel-execute-src-block (around progress nil activate) (set-face-attribute 'org-block-background nil :background "LightSteelBlue") (message "Running your code block") ad-do-it (set-face-attribute 'org-block-background nil :background "gray") (message "Done with code block"))
Strings are not glamorous, high-profile components of R, but they do play a big role in many data cleaning and preparations tasks. R provides a solid set of string operations, but because they have grown organically over time, they can be inconsistent and a little hard to learn. Additionally, they lag behind the string operations in other programming languages, so that some things that are easy to do in languages like Ruby or Python are rather hard to do in R. The stringr package aims to remedy these problems by providing a clean, modern interface to common string operations.
See also http://cran.r-project.org/web/packages/stringr/index.html.
testthat is an amazingly easy library to use unit-testing in R that has enough options to account for the majority of your usage scenarios. It is so simple that I just had to copy and past my same comments about assertthat!
See also http://cran.r-project.org/web/packages/testthat/index.html.
assertthat is an amazingly easy library to use design-by-contract in R that has enough options to account for the majority of your usage scenarios.
See also http://cran.r-project.org/web/packages/assertthat/index.html.
Matt Dowle’s “data.table” talk touches upon so many revealing and educational points in only 20 short minutes. It is a must-watch if you are wanting to choose between the various data query options in R.
sqldf brokers your dataframe from R into a SQLite database, executes your SQL query, and brokers the data back as a new dataframe. Very nice. The documentation and literature reveals that it works with H2, MySQL, and PostgreSQL, and additionally does a whole lot more than the one-liner claims!
CRANberries aggregates information about new, updated and removed packages from the CRAN network for R
DATA-COPE seeks to provide information for research analysts accessing and using administrative data. A key aspect of this mission is resource sharing and curation of quality information that is recommended by our members.
Should your R code get compiled for speed gains?
This post says “yes”.
