Posted on Categories Opinion, Programming, StatisticsTags , , , , , 9 Comments on Let’s Have Some Sympathy For The Part-time R User

Let’s Have Some Sympathy For The Part-time R User

When I started writing about methods for better "parametric programming" interfaces for dplyr for R 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

Posted on Categories Administrativia, data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags , , , , , , , 1 Comment on More documentation for Win-Vector R packages

More documentation for Win-Vector R packages

The Win-Vector public R packages now all have new pkgdown documentation sites! (And, a thank-you to Hadley Wickham for developing the pkgdown tool.)

Please check them out (hint: vtreat is our favorite).

NewImage Continue reading More documentation for Win-Vector R packages

Posted on Categories Opinion, Programming, StatisticsTags , , , , , 2 Comments on Using wrapr::let() with tidyeval

Using wrapr::let() with tidyeval

While going over some of the discussion related to my last post I came up with a really neat way to use wrapr::let() and rlang/tidyeval together.

Please read on to see the situation and example. Continue reading Using wrapr::let() with tidyeval

Posted on Categories Opinion, Programming, StatisticsTags , , , 5 Comments on Please Consider Using wrapr::let() for Replacement Tasks

Please Consider Using wrapr::let() for Replacement Tasks

From dplyr issue 2916.

The following appears to work.

suppressPackageStartupMessages(library("dplyr"))

COL <- "homeworld"
starwars %>%
  group_by(.data[[COL]]) %>%
  head(n=1)
## # A tibble: 1 x 14
## # Groups:   COL [1]
##             name height  mass hair_color skin_color eye_color birth_year
##            <chr>  <int> <dbl>      <chr>      <chr>     <chr>      <dbl>
## 1 Luke Skywalker    172    77      blond       fair      blue         19
## # ... with 7 more variables: gender <chr>, homeworld <chr>, species <chr>,
## #   films <list>, vehicles <list>, starships <list>, COL <chr>

Though notice it reports the grouping is by "COL", not by "homeworld". Also the data set now has 14 columns, not the original 13 from the starwars data set.

Continue reading Please Consider Using wrapr::let() for Replacement Tasks

Posted on Categories Coding, Programming, Statistics, TutorialsTags , , , Leave a comment on wrapr Implementation Update

wrapr Implementation Update

Introduction

The development version CRAN version of our R helper function wrapr::let() has switched from string-based substitution to abstract syntax tree based substitution (AST based substitution, or language based substitution).

Wraprs

I am looking for some feedback from wrapr::let() users already doing substantial work with wrapr::let(). If you are already using wrapr::let() please test if the current development version of wrapr works with your code. If you run into problems: I apologize, and please file a GitHub issue.

Continue reading wrapr Implementation Update

Posted on Categories Coding, data science, Opinion, Programming, Statistics, TutorialsTags , , , , , , , , , , 10 Comments on Non-Standard Evaluation and Function Composition in R

Non-Standard Evaluation and Function Composition in R

In this article we will discuss composing standard-evaluation interfaces (SE: parametric, referentially transparent, or “looks only at values”) and composing non-standard-evaluation interfaces (NSE) in R.

In R the package tidyeval/rlang is a tool for building domain specific languages intended to allow easier composition of NSE interfaces.

To use it you must know some of its structure and notation. Here are some details paraphrased from the major tidyeval/rlang client, the package dplyr: vignette('programming', package = 'dplyr')).

  • ":=" is needed to make left-hand-side re-mapping possible (adding yet another "more than one assignment type operator running around" notation issue).
  • "!!" substitution requires parenthesis to safely bind (so the notation is actually "(!! )", not "!!").
  • Left-hand-sides of expressions are names or strings, while right-hand-sides are quosures/expressions.

Continue reading Non-Standard Evaluation and Function Composition in R

Posted on Categories Coding, Opinion, Programming, StatisticsTags , , , 7 Comments on In defense of wrapr::let()

In defense of wrapr::let()

Saw this the other day:

Wraprvstidyeval

In defense of wrapr::let() (originally part of replyr, and still re-exported by that package) I would say:

  • let() was deliberately designed for a single real-world use case: working with data when you don’t know the column names when you are writing the code (i.e., the column names will come later in a variable). We can re-phrase that as: there is deliberately less to learn as let() is adapted to a need (instead of one having to adapt to let()).
  • The R community already has months of experience confirming let() working reliably in production while interacting with a number of different packages.
  • let() will continue to be a very specific, consistent, reliable, and relevant tool even after dpyr 0.6.* is released, and the community gains experience with rlang/tidyeval in production.

If rlang/tidyeval is your thing, by all means please use and teach it. But please continue to consider also using wrapr::let(). If you are trying to get something done quickly, or trying to share work with others: a “deeper theory” may not be the best choice.

An example follows. Continue reading In defense of wrapr::let()

Posted on Categories Coding, Opinion, Programming, StatisticsTags , , , , 6 Comments on Why to use wrapr::let()

Why to use wrapr::let()

I have written about referential transparency before. In this article I would like to discuss “leaky abstractions” and why wrapr::let() supplies a useful (but leaky) abstraction for R programmers.

Wraprs Continue reading Why to use wrapr::let()

Posted on Categories TutorialsTags , , 11 Comments on Programming over R

Programming over R

R is a very fluid language amenable to meta-programming, or alterations of the language itself. This has allowed the late user-driven introduction of a number of powerful features such as magrittr pipes, the foreach system, futures, data.table, and dplyr. Please read on for some small meta-programming effects we have been experimenting with.

NewImage Continue reading Programming over R

Posted on Categories Exciting Techniques, Pragmatic Data Science, Programming, Statistics, TutorialsTags , , , ,

Step-Debugging magrittr/dplyr Pipelines in R with wrapr and replyr

In this screencast we demonstrate how to easily and effectively step-debug magrittr/dplyr pipelines in R using wrapr and replyr.



Continue reading Step-Debugging magrittr/dplyr Pipelines in R with wrapr and replyr