There is a lot of unnecessary worry over “Non Standard Evaluation” (NSE) in R versus “Standard Evaluation” (SE, or standard “variables names refer to values” evaluation). This very author is guilty of over-discussing the issue. But let’s give this yet another try.
The entire difference between NSE and regular evaluation can be summed up in the following simple table (which should be clear after we work some examples).
Continue reading Standard Evaluation Versus Non-Standard Evaluation in R
R tip: first organize your tasks in terms of data, values, and desired transformation of values, not initially in terms of concrete functions or code.
I know I write a lot about coding in R. But it is in the service of supporting statistics, analysis, predictive analytics, and data science.
Rwithout data is like going to the theater to watch the curtain go up and down.(Adapted from Ben Katchor’s Julius Knipl, Real Estate Photographer: Stories, Little, Brown, and Company, 1996, page 72, “Excursionist Drama 2”.)
Usually you come to R to work with data. If you think and plan in terms of data and values (including introducing more data to control processing) you will usually work in much faster, explainable, and maintainable fashion.
seplyr is an R package that makes it easy to program over dplyr 0.7.*.
To illustrate this we will work an example.
Continue reading Tutorial: Using seplyr to Program Over dplyr
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).
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.
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 "!!").quosures/expressions.Continue reading Non-Standard Evaluation and Function Composition in R
Jonathan Carroll had a an interesting R language idea: to use @-notation to request value substitution in a non-standard evaluation environment (inspired by msyql User-Defined Variables).
He even picked the right image:
This article is on writing sweet R code using the wrapr package.
I want to share an edited screencast of my rehearsal for my recent San Francisco Bay Area R Users Group talk:
