Publications by John Mount
Unit Tests in R
I am collecting here some notes on testing in R. There seems to be a general (false) impression among non R-core developers that to run tests, R package developers need a test management system such as RUnit or testthat. And a further false impression that testthat is the only R test management system. This is in fact not true, as R itself has a ...
10475 sym
Software Dependencies and Risk
Dirk Eddelbuettel just shared an important point on software and analyses: dependencies are hard to manage risks. If your software or research depends on many complex and changing packages, you have no way to establish your work is correct. This is because to establish the correctness of your work, you would need to also establish the correctnes...
2813 sym 16 img
wrapr::let()
I would like to once again recommend our readers to our note on wrapr::let(), an R function that can help you eliminate many problematic NSE (non-standard evaluation) interfaces (and their associate problems) from your R programming tasks. The idea is to imitate the following lambda-calculus idea: let x be y in z := ( λ x . z ) y The ab...
1655 sym
Quantifying R Package Dependency Risk
We recently commented on excess package dependencies as representing risk in the R package ecosystem. The question remains: how much risk? Is low dependency a mere talisman, or is there evidence it is a good practice (or at least correlates with other good practices)? Well, it turns out we can quantify it: each additional non-core package decla...
2388 sym 2 img
Tidyverse users: gather/spread are on the way out
From https://twitter.com/sharon000/status/1107771331012108288: From https://tidyr.tidyverse.org/dev/articles/pivot.html: There are two important new features inspired by other R packages that have been advancing of reshaping in R: The reshaping operation can be specified with a data frame that describes precisely how metadata stored in column ...
1756 sym 2 img
Why we Did Not Name the cdata Transforms wide/tall/long/short
We recently saw this UX (user experience) question from the tidyr author as he adapts tidyr to cdata techniques. The terminology that he is not adopting from cdata is “unpivot_to_blocks()” and “pivot_to_rowrecs()”. One of the research ideas in the cdata package is that the important thing to call out is record structure. The key point i...
1792 sym 6 img
How cdata Control Table Data Transforms Work
With all of the excitement surrounding cdata style control table based data transforms (the cdata ideas being named as the “replacements” for tidyr‘s current methodology, by the tidyr authors themselves!) I thought I would take a moment to describe how they work. cdata defines two primary data manipulation operators: rowrecs_to_blocks() an...
2779 sym R (459 sym/1 pcs)
Operator Notation for Data Transforms
As of cdata version 1.0.8 cdata implements an operator notation for data transform. The idea is simple, yet powerful. First let’s start with some data. d <- wrapr::build_frame( "id", "measure", "value" | 1 , "AUC" , 0.7 | 1 , "R2" , 0.4 | 2 , "AUC" , 0.8 | 2 , "R2" , 0.5 ) knitr::kable(...
1126 sym R (689 sym/7 pcs) 5 tbl
Standard Evaluation Versus Non-Standard Evaluation in R
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 sum...
4447 sym R (347 sym/7 pcs) 2 img
C++ is Often Used in R Packages
The recent r-project article “Use of C++ in Packages” stated as its own summary of recommendation: don’t use C++ to interface with R. A careful reading of the article exposes at least two possible meanings of this: Don’t use C++ to directly call R or directly manipulate R structures. A technical point directly argued (for right or wro...
3282 sym