Richie creates R courses for DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.
R is mostly optimized to help you write data analysis code quickly and readably. Apache Spark is designed to analyze huge datasets quickly. The
sparklyr package lets you write
dplyr R code that runs on a Spark cluster, giving you the best of both worlds. This course teaches you how to manipulate Spark DataFrames using both the
dplyr interface and the native interface to Spark, as well as trying machine learning techniques. Throughout the course, you'll explore the Million Song Dataset.
In which you learn how Spark and R complement each other, how to get data to and from Spark, and how to manipulate Spark data frames using dplyr syntax.
In which you learn more about using the
dplyr interface to Spark, including advanced field selection, calculating groupwise statistics, and joining data frames.
In which you learn about Spark's machine learning data transformation features, and functionality for manipulating native DataFrames.
A case study in which you learn to use
sparklyr's machine learning routines, by predicting the year in which a song was released.