[jira] [Commented] (SPARK-7499) Investigate how to specify columns in SparkR without $ or strings

["Ben Sully (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-7499?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14594895#comment-14594895 ] 

Ben Sully commented on SPARK-7499:
----------------------------------

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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