R Notebook operation combines the capabilities of the
SparkR shell with
the rich selection of features the Jupyter
notebook offers. It provides users with a unique environment to explore their data sets.
R Notebooks allow the user to analyze their data by operating directly on the input
by means of Apache Spark R API. The results of R code execution are presented immediately
and retained across user’s sessions. Due to their versatility,
R Notebooks serve both as a way to
get familiarized with the data and as a record of completed research.
In order to use the
R Notebook, the user has to drag and drop the operation onto the canvas and
connect a DataFrame to its input port. The connected
be accessed from within the
R Notebook by calling the
The user can start editing the code by clicking Open notebook in the
R Notebook operation’s
The variables and functions available in the operations’ global scope:
dataframe() - a function that returns the input
DataFrame for this operation.
Everytime the input
DataFrame changes, the
dataframe() returns the updated
sc - Spark Context
spark - Spark Session
Since: Seahorse 1.3.0
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R Notebook operation does not produce any output.
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