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https://issues.apache.org/jira/browse/SPARK-7499?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14595153#comment-14595153
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Shivaram Venkataraman commented on SPARK-7499:
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[~sd2k] Thanks for taking a shot at this -- One question I have about this
approach is if we can avoid implementing new versions of all these functions.
i.e. is there a common sub-routine we can create that given a set of
identifiers / columns will resolve them to SparkR DataFrame columns ?
Also just to help me understand this better, what is the specific function in
dplyr we are using here ?
> 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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