I want to share an edited screencast of my rehearsal for my recent San Francisco Bay Area R Users Group talk:
I want to share an edited screencast of my rehearsal for my recent San Francisco Bay Area R Users Group talk:
One of the distinctive features of the R platform is how explicit and user controllable everything is. This allows the style of use of R to evolve fairly rapidly. I will discuss this and end with some new notations, methods, and tools I am nominating for inclusion into your view of the evolving “current best … Continue reading Evolving R Tools and Practices
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
I’ve been asked if the adapter “let” from our R package replyr works with data.table. My answer is: it does work. I am not a data.table user so I am not the one to ask if data.table benefits a from a non-standard evaluation to standard evaluation adapter such as replyr::let. Using replyr::let with data.table looks … Continue reading Does replyr::let work with data.table?
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
It’s a common situation to have data from multiple processes in a “long” data format, for example a table with columns measurement and process_that_produced_measurement. It’s also natural to split that data apart to analyze or transform it, per-process — and then to bring the results of that data processing together, for comparison. Such a work … Continue reading A Simple Example of Using replyr::gapply
It’s a common situation to have data from multiple processes in a “long” data format, for example a table with columns measurement and process_that_produced_measurement. It’s also natural to split that data apart to analyze or transform it, per-process — and then to bring the results of that data processing together, for comparison. Such a work … Continue reading A Simple Example of Using replyr::gapply
A bit more on the let wrapper from our replyr R package. library("replyr") help(let, package="replyr") (Edit: this has been updated to the `0.2.0` version of `replyr` which eliminates some of the `()` notation). let {replyr} R Documentation Execute expr with name substitutions specified in alias. Description let implements a mapping from desired names (names used … Continue reading help(let, package=’replyr’)
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”
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
