seplyr is an R package that makes it easy to program over dplyr 0.7.*. To illustrate this we will work an example. Suppose you had worked out a dplyr pipeline that performed an analysis you were interested in. For an example we could take something similar to one of the examples from the dplyr 0.7.0 … Continue reading Tutorial: Using seplyr to Program Over dplyr
The development version of my new R package seplyr is performing in practical applications with dplyr 0.7.* much better than even I (the seplyr package author) expected. I think I have hit a very good set of trade-offs, and I have now spent significant time creating documentation and examples. I wish there had been such … Continue reading seplyr update
I have been writing a lot (too much) on the R topics dplyr/rlang/tidyeval lately. The reason is: major changes were recently announced. If you are going to use dplyr well and correctly going forward you may need to understand some of the new issues (if you don’t use dplyr you can safely skip all of … Continue reading dplyr 0.7 Made Simpler
For R dplyr users one of the promises of the new rlang/tidyeval system is an improved ability to program over dplyr itself. In particular to add new verbs that encapsulate previously compound steps into better self-documenting atomic steps. Let’s take a look at this capability. First let’s start dplyr. suppressPackageStartupMessages(library("dplyr")) packageVersion("dplyr") ## [1] '0.7.1.9000' A … Continue reading Better Grouped Summaries in dplyr
For many R users the magrittr pipe is a popular way to arrange computation and famously part of the tidyverse. The tidyverse itself is a rapidly evolving centrally controlled package collection. The tidyverse authors publicly appear to be interested in re-basing the tidyverse in terms of their new rlang/tidyeval package. So it is natural to … Continue reading What is magrittr’s future in the tidyverse?
There has been some talk of adding native pipe notation to R (for example here, here, and here). And even a tidyeval/rlang pipe here. I think a critical aspect of such an extension would be to treat such a notation as syntactic sugar and not insist such a pipe match magrittr semantics, or worse yet … Continue reading In praise of syntactic sugar
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
While going over some of the discussion related to my last post I came up with a really neat way to use wrapr::let() and rlang/tidyeval together. Please read on to see the situation and example.Suppose we want to parameterize over a couple of names, one denoting a variable coming from the current environment and one … Continue reading Using wrapr::let() with tidyeval
From dplyr issue 2916. The following appears to work. suppressPackageStartupMessages(library("dplyr")) COL <- "homeworld" starwars %>% group_by(.data[[COL]]) %>% head(n=1) ## # A tibble: 1 x 14 ## # Groups: COL [1] ## name height mass hair_color skin_color eye_color birth_year ## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> ## 1 Luke Skywalker 172 77 blond fair blue … Continue reading Please Consider Using wrapr::let() for Replacement Tasks
