Menu Home

Why to use the replyr R package

Recently I noticed that the R package sparklyr had the following odd behavior: suppressPackageStartupMessages(library("dplyr")) library("sparklyr") packageVersion("dplyr") #> [1] ‘0.7.2.9000’ packageVersion("sparklyr") #> [1] ‘0.6.2’ packageVersion("dbplyr") #> [1] ‘1.1.0.9000’ sc <- spark_connect(master = ‘local’) #> * Using Spark: 2.1.0 d <- dplyr::copy_to(sc, data.frame(x = 1:2)) dim(d) #> [1] NA ncol(d) #> [1] […]

Join Dependency Sorting

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 […]

Non-Standard Evaluation and Function Composition in R

In this article we will discuss composing standard-evaluation interfaces (SE: parametric, referentially transparent, or “looks only at values”) 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 […]

In defense of wrapr::let()

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 […]

Summarizing big data in R

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 […]

Programming over R

R is a very fluid language amenable to meta-programming, or alterations of the language itself. This has allowed the late user-driven introduction of a number of powerful features such as magrittr pipes, the foreach system, futures, data.table, and dplyr. Please read on for some small meta-programming effects we have been […]