The spreadsheet software Microsoft Excel, when used with default settings, is known to convert gene names to dates and floating-point numbers. A programmatic scan of leading genomics journals reveals that approximately one-fifth of papers with supplementary Excel gene lists contain erroneous gene name conversions.

Letter to the Editor

controller is an R package for working with controlled vocabularies. It’s first release (v0.1.0) is now available now on CRAN. The package addresses something I find myself doing very often in analysis code: tidying messy and inconsistent terminologies. For smaller datasets, dplyr::recode() is okay for this, but writing the mapping out as an R function call gets tedious fast when dealing with a long list of terms. It becomes very tedious when you have variants distinguished only by things like capitalisation (OxA- vs. oxa-), word boundaries (Çatalhöyük vs. Çatal Höyük) or character encoding (ʿAin Ghazal vs. ʽAyn Ghazal). controller instead defines preferred terms and their variants in a data frame. Its control() verb is the equivalent of dplyr::recode() but using this thesaurus and with a few extra bells and whistles for fuzzy matching and reporting what was (and wasn’t) changed: library(controller) data("colour_thesaurus") shades <- c("daffodil", "purple", "magenta", "azure", "navy", "violet") control(shades, colour_thesaurus) #> Replaced values: #> ℹ daffodil → yellow #> ℹ azure → blue #> ℹ navy → blue #> ℹ violet → purple #> Warning: Some values of `x` were not matched in `thesaurus`: #> ✖ magenta Fuzzy matching means we don’t need to exhaustively list those variants from things like differences in case, word boundaries, or character encoding: control_ci(toupper(shades), colour_thesaurus) #> Replaced values: #> ℹ DAFFODIL → yellow #> ℹ PURPLE → purple #> ℹ AZURE → blue #> ℹ NAVY → blue #> ℹ VIOLET → purple #> [1] "yellow" "purple" "MAGENTA" "blue" "blue" "purple" #> Warning message: #> Some values of `x` were not matched in `thesaurus`: #> ✖ MAGENTA This package has been hanging around for a while! It started off as a helper function I used for cleaning up site names from prehistoric sites in Southwest Asia. The basic idea was inspired by similar functions that used to exist in c14bazAAR for cleaning sample metadata for radiocarbon date, that I thought were quite neat. So when the maintainers of that package decided to deprecate those, I took over the thesauri as part of c14 and spun the supporting functions off into controller as a standalone package. Then over the years it acquired some more functionality for working with controlled vocabularies (a surprising gap in the R ecosystem), like reading heritage vocabularies in Historic England’s FISH format. Five years later, I am finally getting around to releasing it on CRAN because I need to release c14 on CRAN, because that’s used in analyses I’m now publishing. It’s the research software engineering of changing a lightbulb, basically. The first release of controller includes: control(), control_ci(), and control_fuzzy() for recoding values control_names(), control_names_ci(), and control_names_fuzzy() for recoding names control_matches() for inspecting how matches were made read_fish() for reading vocabularies in Historic England’s FISH format colour_thesaurus, an example dataset You can install it from CRAN: install.packages("controller") Or the development version from GitHub: remotes::install_github("joeroe/controller") You can find the full documentation at https://controller.joeroe.io.

The FCC says the most controversial aspect of Reflect Orbital's Earendil-1 satellite, the giant mirror, falls out of its purview since the regulator mainly focuses on radio spectrum.

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