This is a note on debugging magrittr pipelines in R using Bizarro Pipe and eager assignment. Pipes in R The magrittr R package supplies an operator called “pipe” which is written as “%>%“. The pipe operator is partly famous due to its extensive use in dplyr and use by dplyr users. The pipe operator is … Continue reading Debugging Pipelines in R with Bizarro Pipe and Eager Assignment
Version 2.0 of my data set validation package assertr hit CRAN just this weekend. It has some pretty great improvements over version 1. For those new to the package, what follows is a short and new introduction. For those who… Continue reading →
I am going to write about an insidious statistical, data analysis, and presentation fallacy I call “the zero bug” and the habits you need to cultivate to avoid it. The zero bug Here is the zero bug in a nutshell: common data aggregation tools often can not “count to zero” from examples, and this causes … Continue reading The Zero Bug
I have just finished and released a free new R video lecture demonstrating how to use the “Bizarro pipe” to debug magrittr pipelines. I think R dplyr users will really enjoy it. Please read on for the link to the video lecture. In this video lecture I use the “Bizarro pipe” to debug the example … Continue reading Using the Bizarro Pipe to Debug magrittr Pipelines in R
I am happy to announce a couple of exciting upcoming Win-Vector LLC public speaking engagements. BARUG Meetup Tuesday, Tuesday February 7, 2017 ~7:50pm, Intuit, Building 20, 2600 Marine Way, Mountain View, CA. Win-Vector LLC’s John Mount will be giving a “lightning talk” (15 minutes) on R calling conventions (standard versus non-standard) and showing how to … Continue reading Upcoming Win-Vector LLC public speaking engagements
Consider the problem of “parametric programming” in R. That is: simply writing correct code before knowing some details, such as the names of the columns your procedure will have to be applied to in the future. Our latest version of replyr::let makes such programming easier. Archie’s Mechanics #2 (1954) copyright Archie Publications (edit: great news! … Continue reading Comparative examples using replyr::let
Consider the common following problem: compute for a data set (say the infamous iris example data set) per-group ranks. Suppose we want the rank of iris Sepal.Lengths on a per-Species basis. Frankly this is an “ugh” problem for many analysts: it involves all at the same time grouping, ordering, and window functions. It also is … Continue reading Organize your data manipulation in terms of “grouped ordered apply”
R has a number of assignment operators (at least “<-“, “=“, and “->“; plus “<<-” and “->>” which have different semantics). The R-style guides routinely insist on “<-” as being the only preferred form. In this note we are going to try to make the case for “->” when using magrittr pipelines. [edit: After reading … Continue reading The Case For Using -> In R
Statisticians and data scientists want a neat world where data is arranged in a table such that every row is an observation or instance, and every column is a variable or measurement. Getting to this state of “ready to model format” (often called a denormalized form by relational algebra types) often requires quite a bit … Continue reading The case for index-free data manipulation
Imagine that in the course of your analysis, you regularly require summaries of numerical values. For some applications you want the mean of that quantity, plus/minus a standard deviation; for other applications you want the median, and perhaps an interval around the median based on the interquartile range (IQR). In either case, you may want … Continue reading Using replyr::let to Parameterize dplyr Expressions
