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https://issues.apache.org/jira/browse/SPARK-7499?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14594895#comment-14594895
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Ben Sully commented on SPARK-7499:
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I've had a go at this by implementing methods for the generic dplyr verbs, i.e.
select/filter/summarise/mutate etc. The other advantage of using these is that
commands can be chained using pipes.
There are a few more which need to be implemented (e.g. transmute) but they
should be relatively trivial.
Methods are in this gist:
https://gist.github.com/sd2k/6e94e9dc590502473746
> Investigate how to specify columns in SparkR without $ or strings
> -----------------------------------------------------------------
>
> Key: SPARK-7499
> URL: https://issues.apache.org/jira/browse/SPARK-7499
> Project: Spark
> Issue Type: Improvement
> Components: SparkR
> Reporter: Shivaram Venkataraman
>
> Right now in SparkR we need to specify the columns used using `$` or strings.
> For example to run select we would do
> {code}
> df1 <- select(df, df$age > 10)
> {code}
> It would be good to infer the set of columns in a dataframe automatically and
> resolve symbols for column names. For example
> {code}
> df1 <- select(df, age > 10)
> {code}
> One way to do this is to build an environment with all the column names to
> column handles and then use `substitute(arg, env = columnNameEnv)`
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