Then load it with:
dplyr 1.0.0 is chock-a-block with new features; so many, in fact, that we can’t fit them all into one post. So if you want to learn more about what’s new, we recommend reading our existing series of posts:
Major lifecycle changes. This post focusses on the idea of the “function lifecycle” which helps you understand where functions in dplyr are going. Particularly important is the idea of a “superseded” function. A superseded function is not going away, but we no longer recommend using it in new code.
rename(), and (new)
rename()can now select by position, name, function of name, type, and any combination thereof. A new
relocate()function makes it easy to change the position of columns.
Working within rows.
rowwise()has been renewed and revamped to make it easier to perform operations row-by-row. This makes it much easier to solve problems that previously required
purrr::map(), or friends.
You can see the full list of changes in the release notes.
dplyr has a new logo thanks to the talented Allison Horst!
(Stay tuned for details about how to get this sticker on to your laptop. We have some exciting news coming up!)
The best way to find out about all the cool new features dplyr has to offer is to read through the blog posts linked to above. But thanks to inspiration from Daniel Anderson here’s one example of fitting two different models by subgroup that shows off a bunch of cool features:
library(dplyr, warn.conflicts = FALSE) models <- tibble::tribble( ~model_name, ~ formula, "length-width", Sepal.Length ~ Petal.Width + Petal.Length, "interaction", Sepal.Length ~ Petal.Width * Petal.Length ) iris %>% nest_by(Species) %>% left_join(models, by = character()) %>% rowwise(Species, model_name) %>% mutate(model = list(lm(formula, data = data))) %>% summarise(broom::glance(model)) #> `summarise()` regrouping output by 'Species', 'model_name' (override with `.groups` argument) #> # A tibble: 6 x 13 #> # Groups: Species, model_name  #> Species model_name r.squared adj.r.squared sigma statistic p.value df #> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 setosa length-wi… 0.112 0.0739 0.339 2.96 6.18e- 2 3 #> 2 setosa interacti… 0.133 0.0760 0.339 2.34 8.54e- 2 4 #> 3 versic… length-wi… 0.574 0.556 0.344 31.7 1.92e- 9 3 #> 4 versic… interacti… 0.577 0.549 0.347 20.9 1.11e- 8 4 #> 5 virgin… length-wi… 0.747 0.736 0.327 69.3 9.50e-15 3 #> 6 virgin… interacti… 0.757 0.741 0.323 47.8 3.54e-14 4 #> # … with 5 more variables: logLik <dbl>, AIC <dbl>, BIC <dbl>, deviance <dbl>, #> # df.residual <int>
Note the use of:
nest_by(), which generates a nested data frame where each row represents one subgroup.
by = character()which now performs a Cartesian product, generating every combination of subgroup and model.
dplyr 1.0.0 has been one of the biggest projects that we, as a team, have ever tackled. Almost everyone in the tidyverse team has been involved in some capacity. Special thanks go to Romain François, who in his role as primary developer has been working on this release for over six months, and to Lionel Henry and Davis Vaughn for all their work on the vctrs package. Jim Hester’s work on running revdep checks in the cloud also made a big impact on our ability to understand failure modes.
A big thanks to all 137 members of the dplyr community who helped make this release possible by finding bugs, discussing issues, and writing code: @AdaemmerP, @adelarue, @ahernnelson, @alaataleb111, @antoine-sachet, @atusy, @Auld-Greg, @b-rodrigues, @batpigandme, @bedantaguru, @benjaminschlegel, @benjbuch, @bergsmat, @billdenney, @brianmsm, @bwiernik, @caldwellst, @cat-zeppelin, @chillywings, @clauswilke, @colearendt, @DanChaltiel, @danoreper, @danzafar, @davidbaniadam, @DavisVaughan, @dblodgett-usgs, @ddsjoberg, @deschen1, @dfrankow, @DiegoKoz, @dkahle, @DzimitryM, @earowang, @echasnovski, @edwindj, @elbersb, @elcega, @ericemc3, @espinielli, @FedericoConcas, @FlukeAndFeather, @GegznaV, @gergness, @ggrothendieck, @glennmschultz, @gowerc, @greg-minshall, @gregorp, @ha0ye, @hadley, @Harrison4192, @henry090, @hughjonesd, @ianmcook, @ismailmuller, @isteves, @its-gazza, @j450h1, @Jagadeeshkb, @jarauh, @jason-liu-cs, @jayqi, @JBGruber, @jemus42, @jennybc, @jflournoy, @jhuntergit, @JohannesNE, @jzadra, @karldw, @kassambara, @klin333, @knausb, @kriemo, @krispiepage, @krlmlr, @kvasilopoulos, @larry77, @leonawicz, @lionel-, @lorenzwalthert, @LudvigOlsen, @madlogos, @markdly, @markfairbanks, @meghapsimatrix, @meixiaba, @melissagwolf, @mgirlich, @Michael-Sheppard, @mikmart, @mine-cetinkaya-rundel, @mir-cat, @mjsmith037, @mlane3, @msberends, @msgoussi, @nefissakhd, @nick-youngblut, @nzbart, @pavel-shliaha, @pdbailey0, @pnacht, @ponnet, @r2evans, @ramnathv, @randy3k, @richardjtelford, @romainfrancois, @rorynolan, @ryanvoyack, @selesnow, @selin1st, @sewouter, @sfirke, @SimonDedman, @sjmgarnier, @smingerson, @stefanocoretta, @strengejacke, @tfkillian, @tilltnet, @tonyvibe, @topepo, @torockel, @trinker, @tungmilan, @tzakharko, @uasolo, @werkstattcodes, @wlandau, @xiaoa6435, @yiluheihei, @yutannihilation, @zenggyu, and @zkamvar.