Sparklyr

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
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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 →