We are pleased to announce that seplyr version 0.5.8 is now available on CRAN. seplyr is an R package that provides a thin wrapper around elements of the dplyr package and (now with version 0.5.8) the tidyr package. The intent is to give the part time R user the ability […]
R tip: first organize your tasks in terms of data, values, and desired transformation of values, not initially in terms of concrete functions or code. I know I write a lot about coding in R. But it is in the service of supporting statistics, analysis, predictive analytics, and data science. […]
Another R tip. Need to replace a name in some R code or make R code re-usable? Use wrapr::let().
I currently advise R dplyr users to take care when using multiple assignment dplyr::mutate() commands on databases. (image: Kingroyos, Creative Commons Attribution-Share Alike 3.0 Unported License) In this note I exhibit a troublesome example, and a systematic solution.
A big “thank you!!!” to Microsoft for hosting our new introduction to seplyr. If you are working R and big data I think the seplyr package can be a valuable tool.
In our last article we pointed out a dangerous silent result corruption we have seen when using the R dplyr package with databases. To systematically avoid this result corruption we suggest breaking up your dplyr::mutate() statements to be dependency-free (not assigning the same value twice, and not using any value […]
Win-Vector LLC has been working on porting some significant large scale production systems from SAS to R. From this experience we want to share how to simulate, in R with Apache Spark (via Sparklyr), a nifty SAS feature: the vectorized “block if(){}else{}” structure.
The R package seplyr has a neat new feature: the function seplyr::expand_expr() which implements what we call “the string algebra” or string expression interpolation. The function takes an expression of mixed terms, including: variables referring to names, quoted strings, and general expression terms. It then “de-quotes” all of the variables […]
The R package wrapr supplies a few neat new coding notations. An Abacus, which gives us the term “calculus.”
When I started writing about methods for better "parametric programming" interfaces for dplyr for R dplyr users in December of 2016 I encountered three divisions in the audience: dplyr users who had such a need, and wanted such extensions. dplyr users who did not have such a need ("we always […]