Packages

sparklyr 0.6

2017-07-31 Javier Luraschi
We’re excited to announce a new release of the sparklyr package, available in CRAN today! sparklyr 0.6 introduces new features to: Distribute R computations using spark_apply() to execute arbitrary R code across your Spark cluster. You can now use all of your favorite R packages and functions in a distributed context. Connect to External Data Sources using spark_read_source(), spark_write_source(), spark_read_jdbc() and spark_write_jdbc(). Use the Latest Frameworks including dplyr 0.7, DBI 0. Read more →

haven 1.1.0

2017-07-13 Hadley Wickham
I’m pleased to announce the release of haven 1.1.0. Haven is designed to faciliate the transfer of data between R and SAS, SPSS, and Stata. It makes it easy to read SAS, SPSS, and Stata file formats in to R data frames, and makes it easy to save your R data frames in to SPSS and Stata if you need to collaborate with others using closed source statistical software. Install the latest version by running: Read more →

Introducing learnr

2017-07-11 Garrett Grolemund
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We’re pleased to introduce the learnr package, now available on CRAN. The learnr package makes it easy to turn any R Markdown document into an interactive tutorial. Tutorials consist of content along with interactive components for checking and reinforcing understanding. Tutorials can include any or all of the following: Narrative, figures, illustrations, and equations. Code exercises (R code chunks that users can edit and execute directly). Multiple choice quizzes. Read more →

dbplyr 1.1.0

2017-06-27 Hadley Wickham
I’m pleased to announce the release of the dbplyr package, which now contains all dplyr code related to connecting to databases. This shouldn’t affect you-as-a-user much, but it makes dplyr simpler, and makes it easier to release improvements just for database related code. You can install the latest version of dbplyr with: install.packages("dbplyr") DBI and dplyr alignment The biggest change in this release is that dplyr/dbplyr works much more directly with DBI database connections. Read more →

bigrquery 0.4.0

2017-06-26 Hadley Wickham
I’m pleased to announce that bigrquery 0.4.0 is now on CRAN. bigrquery makes it possible to talk to Google’s BigQuery cloud database. It provides both DBI and dplyr backends so you can interact with BigQuery using either low-level SQL or high-level dplyr verbs. Install the latest version of bigrquery with: install.packages("bigrquery") Basic usage Connect to a bigquery database using DBI: library(dplyr) con <- DBI::dbConnect(dbi_driver(), project = "publicdata", dataset = "samples", billing = "887175176791" ) DBI::dbListTables(con) #> [1] "github_nested" "github_timeline" "gsod" "natality" #> [5] "shakespeare" "trigrams" "wikipedia" (You’ll be prompted to authenticate interactively, or you can use a service token with set_service_token(). Read more →

dplyr 0.7.0

2017-06-13 Hadley Wickham
I’m pleased to announce that dplyr 0.7.0 is now on CRAN! (This was dplyr 0.6.0 previously; more on that below.) dplyr provides a “grammar” of data transformation, making it easy and elegant to solve the most common data manipulation challenges. dplyr supports multiple backends: as well as in-memory data frames, you can also use it with remote SQL databases. If you haven’t heard of dplyr before, the best place to start is the Data transformation chapter in R for Data Science. Read more →

shinydashboard 0.6.0

2017-05-18 Bárbara Borges
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Shinydashboard 0.6.0 is now on CRAN! This release of shinydashboard was aimed at both fixing bugs and also bringing the package up to speed with users’ requests and Shiny itself (especially fully bringing bookmarkable state to shinydashboard’s sidebar). In addition to bug fixes and new features, we also added a new “Behavior” section to the shinydashboard website to explain this release’s two biggest new features, and also to provide users with more material about shinydashboard-specific behavior. Read more →

readxl 1.0.0

2017-04-19 Jenny Bryan
I’m pleased to announce that readxl 1.0.0 is available on CRAN. readxl makes it easy to bring tabular data out of Excel and into R, for modern .xlsx files and the legacy .xls format. readxl does not have any tricky external dependencies, such as Java or Perl, and is easy to install and use on Mac, Windows, and Linux. You can install it with: install.packages("readxl") As well as fixing many bugs, this release: Read more →

dplyr 0.6.0 coming soon!

2017-04-13 Hadley Wickham
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. Installation Install the pre-release version with: # install.packages("devtools") devtools::install_github("tidyverse/dplyr") If you discover any problems, please file a minimal reprex on GitHub. Read more →

tidyverse updates

2017-04-12 Hadley Wickham
Over the couple of months there have been a bunch of smaller releases to packages in the tidyverse. This includes: forcats 0.2.0, for working with factors. readr 1.1.0, for reading flat-files from disk. stringr 1.2.0, for manipulating strings. tibble 1.3.0, a modern re-imagining of the data frame. This blog post summarises the most important new features, and points to the full release notes where you can learn more. Read more →