R package wrapr 1.5.0 is now available on CRAN.
wrapr includes a lot of tools for writing better
I’ll be writing articles on a number of the new capabilities. For now I just leave you with the nifty operator coalesce notation.
Continue reading wrapr 1.5.0 available on CRAN
R Tip: use
A lot of R functions are type unstable, which means they return different types or classes depending on details of their values.
For example consider
all.equal(), it returns the logical value
TRUE when the items being compared are equal:
all.equal(1:3, c(1, 2, 3))
#  TRUE
However, when the items being compared are not equal
all.equal() instead returns a message:
all.equal(1:3, c(1, 2.5, 3))
#  "Mean relative difference: 0.25"
This can be inconvenient in using functions similar to
all.equal() as tests in
if()-statements and other program control structures.
The saving functions is
TRUE if its argument value is equivalent to
TRUE, and returns
R programming much easier.
Continue reading R Tip: use isTRUE()
Nina Zumel and I have been working on packaging our favorite graphing techniques in a more reusable way that emphasizes the analysis task at hand over the steps needed to produce a good visualization. We are excited to announce the WVPlots is now at version 1.0.0 on CRAN!
Continue reading WVPlots now at version 1.0.0 on CRAN!
wrapr 1.4.1 is now available on CRAN.
wrapr is a really neat
R package both organizing, meta-programming, and debugging R code. This update generalizes the dot-pipe feature’s dot S3 features.
Please give it a try!
Continue reading wrapr 1.4.1 now up on CRAN
rquery talk went very well, thank you very much to the attendees for being an attentive and generous audience.
rquery at BARUG, photo credit: Timothy Liu)
I am now looking for invitations to give a streamlined version of this talk privately to groups using
R who want to work with
SQL (with databases such as PostgreSQL or big data systems such as Apache Spark).
rquery has a number of features that greatly improve team productivity in this environment (strong separation of concerns, strong error checking, high usability, specific debugging features, and high performance queries).
If your group is in the San Francisco Bay Area and using
R to work with a
SQL accessible data source, please reach out to me at firstname.lastname@example.org, I would be honored to show your team how to speed up their project and lower development costs with
rquery. If you are a big data vendor and some of your clients use
R, I am especially interested in getting in touch: our system can help
R users start working with your installation.
R tip: use slices.
R has a very powerful array slicing ability that allows for some very slick data processing.
Continue reading R Tip: Use Slices
Another R tip. Get in the habit of using
drop = FALSE when indexing (using
[ , ] on)
Prince Rupert’s drops (img: Wikimedia Commons)
Continue reading R Tip: Use
drop = FALSE with