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Is 10,000 Cells Big?

Trick question: is a 10,000 cell numeric data.frame big or small? In the era of “big data” 10,000 cells is minuscule. Such data could be fit on fewer than 1,000 punched cards (or less than half a box). The joking answer is: it is small when they are selling you […]

Announcing rquery

We are excited to announce the rquery R package. rquery is Win-Vector LLC‘s currently in development big data query tool for R. rquery supplies set of operators inspired by Edgar F. Codd‘s relational algebra (updated to reflect lessons learned from working with R, SQL, and dplyr at big data scale […]

Win-Vector LLC announces new “big data in R” tools

Win-Vector LLC is proud to introduce two important new tool families (with documentation) in the 0.5.0 version of seplyr (also now available on CRAN): partition_mutate_se() / partition_mutate_qt(): these are query planners/optimizers that work over dplyr::mutate() assignments. When using big-data systems through R (such as PostgreSQL or Apache Spark) these planners […]

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] […]

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 […]