Blog Archives

Operator Notation for Data Transforms

March 25, 2019
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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

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How cdata Control Table Data Transforms Work

March 23, 2019
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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() and blocks_to_rowrecs(). These are the … Continue reading How...

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Why we Did Not Name the cdata Transforms wide/tall/long/short

March 22, 2019
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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 … Continue reading Why...

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Tidyverse users: gather/spread are on the way out

March 19, 2019
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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 names becomes data variables (and vice versa). This is inspired by the cdata package … Continue reading Tidyverse...

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Quantifying R Package Dependency Risk

March 18, 2019
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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 … Continue reading Quantifying...

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wrapr::let()

March 16, 2019
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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 … Continue reading wrapr::let()

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Software Dependencies and Risk

March 15, 2019
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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 … Continue reading Software...

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Unit Tests in R

March 13, 2019
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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 … Continue reading Unit...

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Data Manipulation Corner Cases

March 10, 2019
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Data Manipulation Corner Cases

Let’s try some "ugly corner cases" for data manipulation in R. Corner cases are examples where the user might be running to the edge of where the package developer intended their package to work, and thus often where things can go wrong. Let’s see what happens when we try to stick a fork in the … Continue reading Data...

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Starting With Data Science: A Rigorous Hands-On Introduction to Data Science for Engineers

March 6, 2019
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Starting With Data Science A rigorous hands-on introduction to data science for engineers. Win Vector LLC is now offering a 4 day on-site intensive data science course. The course targets engineers familiar with Python and introduces them to the basics of current data science practice. This is designed as an interactive in-person (not remote or … Continue reading Starting...

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