seplyr has a neat new feature: the function
seplyr::expand_expr() which implements what we call “the string algebra” or string expression interpolation. The function takes an expression of mixed terms, including: variables referring to names, quoted strings, and general expression terms. It then “de-quotes” all of the variables referring to quoted strings and “dereferences” variables thought to be referring to names. The entire expression is then returned as a single string.
This provides a powerful way to easily work complicated expressions into the
seplyr data manipulation methods. Continue reading Neat New seplyr Feature: String Interpolation
When I started writing about methods for better "parametric programming" interfaces for
dplyr users in December of 2016 I encountered three divisions in the audience:
dplyr users who had such a need, and wanted such extensions.
dplyr users who did not have such a need ("we always know the column names").
dplyr users who found the then-current fairly complex "underscore" and
lazyeval system sufficient for the task.
Needing name substitution is a problem an advanced full-time
R user can solve on their own. However a part-time
R would greatly benefit from a simple, reliable, readable, documented, and comprehensible packaged solution. Continue reading Let’s Have Some Sympathy For The Part-time R User
The Win-Vector public R packages now all have new
pkgdown documentation sites! (And, a thank-you to Hadley Wickham for developing the
Please check them out (hint:
vtreat is our favorite).
Continue reading More documentation for Win-Vector R packages
seplyr is an
R package that makes it easy to program over
To illustrate this we will work an example.
Continue reading Tutorial: Using seplyr to Program Over dplyr
The development version of my new
seplyr is performing in practical applications with
0.7.* much better than even I (the
seplyr package author) expected.
I think I have hit a very good set of trade-offs, and I have now spent significant time creating documentation and examples.
I wish there had been such a package weeks ago, and that I had started using this approach in my own client work at that time. If you are already a
dplyr user I strongly suggest trying
seplyr in your own analysis projects.
Please see here for details.
I have been writing a lot (too much) on the
tidyeval lately. The reason is: major changes were recently announced. If you are going to use
dplyr well and correctly going forward you may need to understand some of the new issues (if you don’t use
dplyr you can safely skip all of this). I am trying to work out (publicly) how to best incorporate the new methods into:
- real world analyses,
- reusable packages,
- and teaching materials.
I think some of the apparent discomfort on my part comes from my feeling that
dplyr never really gave standard evaluation (SE) a fair chance. In my opinion:
dplyr is based strongly on non-standard evaluation (NSE, originally through
lazyeval and now through
tidyeval) more by the taste and choice than by actual analyst benefit or need.
dplyr isn’t my package, so it isn’t my choice to make; but I can still have an informed opinion, which I will discuss below.
Continue reading dplyr 0.7 Made Simpler