Sparklyr

sparklyr 0.7

2018-01-29 Kevin Kuo
Thumbnail
We are excited to share that sparklyr 0.7 is now available on CRAN! Sparklyr provides an R interface to Apache Spark. It supports dplyr syntax for working with Spark DataFrames and exposes the full range of machine learning algorithms available in Spark. You can also learn more about Apache Spark and sparklyr in spark.rstudio.com and our new webinar series on Apache Spark. Features in this release: Adds support for ML Pipelines which provide a uniform set of high-level APIs to help create, tune, and deploy machine learning pipelines at scale. Read more →

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 →

See RStudio + sparklyr for big data at Strata + Hadoop World

2017-02-13 Roger Oberg
If big data is your thing, you use R, and you’re headed to Strata + Hadoop World in San Jose March 13 & 14th, you can experience in person how easy and practical it is to analyze big data with R and Spark. In a beginner level talk by RStudio’s Edgar Ruiz and an intermediate level workshop by Win-Vector’s John Mount, we cover the spectrum: What R is, what Spark is, how Sparklyr works, and what is required to set up and tune a Spark cluster. Read more →

sparklyr 0.5

2017-01-24 Javier Luraschi
Thumbnail
We’re happy to announce that version 0.5 of the sparklyr package is now available on CRAN. The new version comes with many improvements over the first release, including: Extended dplyr support by implementing: do() and n_distinct(). New functions including sdf_quantile(), ft_tokenizer() and ft_regex_tokenizer(). Improved compatibility, sparklyr now respects the value of the ‘na.action’ R option and dim(), nrow() and ncol(). Experimental support for Livy to enable clients, including RStudio, to connect remotely to Apache Spark. Read more →