I’m planning to submit dplyr 0.6.0 to CRAN on May 11 (in four weeks time). In preparation, I’d like to announce that the release candidate, dplyr 0.5.0.9002 is now available. I would really appreciate it if you’d try it out and report any problems. This will ensure that the official release has as few bugs as possible.
Install the pre-release version with:
# install.packages("devtools") devtools::install_github("tidyverse/dplyr")
dplyr 0.6.0 is a major release including over 100 bug fixes and improvements. There are three big changes that I want to touch on here:
Improved encoding support (particularly for CJK on windows)
Tidyeval, a new framework for programming with dplyr
You can see a complete list of changes in the draft release notes.
Almost all database related code has been moved out of dplyr and into a new package, dbplyr. This makes dplyr simpler, and will make it easier to release fixes for bugs that only affect databases.
To install the development version of dbplyr so you can try it out, run:
There’s one major change, as well as a whole heap of bug fixes and minor improvements. It is now no longer necessary to create a remote “src”. Instead you can work directly with the database connection returned by DBI, reflecting the robustness of the DBI ecosystem. Thanks largely to the work of Kirill Muller (funded by the R Consortium) DBI backends are now much more consistent, comprehensive, and easier to use. That means that there’s no longer a need for a layer between you and DBI.
You can continue to use
src_sqlite() (which still live in dplyr), but I recommend a new style that makes the connection to DBI more clear:
con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") DBI::dbWriteTable(con, "iris", iris) #>  TRUE iris2 <- tbl(con, "iris") iris2 #> Source: table<iris> [?? x 5] #> Database: sqlite 3.11.1 [:memory:] #> #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3.0 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5.0 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5.0 3.4 1.5 0.2 setosa #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> # ... with more rows
This is particularly useful if you want to perform non-SELECT queries as you can do whatever you want with
If you’ve implemented a database backend for dplyr, please read the backend news to see what’s changed from your perspective (not much). If you want to ensure your package works with both the current and previous version of dplyr, see
wrap_dbplyr_obj() for helpers.
We have done a lot of work to ensure that dplyr works with encodings other that Latin1 on Windows. This is most likely to affect you if you work with data that contains Chinese, Japanese, or Korean (CJK) characters. dplyr should now just work with such data.
dplyr has a new approach to non-standard evaluation (NSE) called tidyeval. Tidyeval is described in detail in a new vignette about programming with dplyr but, in brief, it gives you the ability to interpolate values in contexts where dplyr usually works with expressions:
my_var <- quo(homeworld) starwars %>% group_by(!!my_var) %>% summarise_at(vars(height:mass), mean, na.rm = TRUE) #> # A tibble: 49 × 3 #> homeworld height mass #> <chr> <dbl> <dbl> #> 1 Alderaan 176.3333 64.0 #> 2 Aleen Minor 79.0000 15.0 #> 3 Bespin 175.0000 79.0 #> 4 Bestine IV 180.0000 110.0 #> 5 Cato Neimoidia 191.0000 90.0 #> 6 Cerea 198.0000 82.0 #> 7 Champala 196.0000 NaN #> 8 Chandrila 150.0000 NaN #> 9 Concord Dawn 183.0000 79.0 #> 10 Corellia 175.0000 78.5 #> # ... with 39 more rows
This will make it much easier to eliminate copy-and-pasted dplyr code by extracting repeated code into a function.
This also means that the underscored version of each main verb (
select_() etc). is no longer needed, and so these functions have been deprecated (but remain around for backward compatibility).
gt 0.6.0 includes new features that will make your display/summary tables look and work better.
We are thrilled to announce the release of vetiver, a framework for MLOps tasks in R and Python. Use vetiver to version, share, deploy, and monitor a trained model.