In our latest R and Big Data article we discuss replyr. Why replyr replyr stands for REmote PLYing of big data for R. Why should R users try replyr? Because it lets you take a number of common working patterns and apply them to remote data (such as databases or Spark). replyr allows users to … Continue reading Working With R and Big Data: Use Replyr
In our latest installment of “R and big data” let’s again discuss the task of left joining many tables from a data warehouse using R and a system called "a join controller" (last discussed here). One of the great advantages to specifying complicated sequences of operations in data (rather than in code) is: it is … Continue reading Join Dependency Sorting
In this article we will discuss composing standard-evaluation interfaces (SE) and composing non-standard-evaluation interfaces (NSE) in R. In R the package tidyeval/rlang is a tool for building domain specific languages intended to allow easier composition of NSE interfaces. To use it you must know some of its structure and notation. Here are some details paraphrased … Continue reading Non-Standard Evaluation and Function Composition in R
This note describes a useful replyr tool we call a "join controller" (and is part of our "R and Big Data" series, please see here for the introduction, and here for one our big data courses). When working on real world predictive modeling tasks in production, the ability to join data and document how you … Continue reading Use a Join Controller to Document Your Work
Saw this the other day: In defense of wrapr::let() (originally part of replyr, and still re-exported by that package) I would say: let() was deliberately designed for a single real-world use case: working with data when you don’t know the column names when you are writing the code (i.e., the column names will come later … Continue reading In defense of wrapr::let()
Our next "R and big data tip" is: summarizing big data. We always say "if you are not looking at the data, you are not doing science"- and for big data you are very dependent on summaries (as you can’t actually look at everything). Simple question: is there an easy way to summarize big data … Continue reading Summarizing big data in R
In this screencast we demonstrate how to easily and effectively step-debug magrittr/dplyr pipelines in R using wrapr and replyr. Some of the big issues in trying to debug magrittr/dplyr pipelines include: Pipelines being large expressions that are hard to line-step into. Visibility of intermediate results. Localizing operations (in time and code position) in the presence … Continue reading Step-Debugging magrittr/dplyr Pipelines in R with wrapr and replyr
replyr is an R package that contains extensions, adaptions, and work-arounds to make remote R dplyr data sources (including big data systems such as Spark) behave more like local data. This allows the analyst to more easily develop and debug procedures that simultaneously work on a variety of data services (in-memory data.frame, SQLite, PostgreSQL, and … Continue reading replyr: Get a Grip on Big Data in R
This article is on writing sweet R code using the wrapr package. The problem Consider the following R puzzle. You are given: a data.frame, the name of a column that you wish to find missing values (NA) in, and the name of a column to land the result. For instance: d <- data.frame(x = c(1, … Continue reading wrapr: for sweet R code
Recently Dirk Eddelbuettel pointed out that our R function debugging wrappers would be more convenient if they were available in a low-dependency micro package dedicated to little else. Dirk is a very smart person, and like most R users we are deeply in his debt; so we (Nina Zumel and myself) listened and immediately moved … Continue reading Announcing the wrapr packge for R
