error in librarysparkr lib.loc cfile.pathsys.getenv spark home r lib no library trees found in

Error In Librarysparkr Lib.Loc Cfile.Pathsys.Getenv Spark Home R Lib No Library Trees Found In How To Join SparkR
Error In Librarysparkr Lib.Loc Cfile.Pathsys.Getenv Spark Home R Lib No Library Trees Found In Code On Spark

Jul 06, 2017 Thanks for contributing an answer to Stack Overflow Please be sure to answer the question.Provide details and share your research Asking for help, clarification, or responding to other answers.

R; R is the latest addition and support for it certainly lags the other three languages.

In Spark 1.x there was no support for accessing the Spark ML (machine learning) libraries from R.

Error In Librarysparkr Lib.Loc Cfile.Pathsys.Getenv Spark Home R Lib No Library Trees Found In Code On Spark

The performance of R code on Spark was also considerably worse than could be achieved using, say, Scala.

Error In Librarysparkr Lib.Loc Cfile.Pathsys.Getenv Spark Home R Lib No Library Trees Found In How To Join SparkR

Sign up Find file Copy path Cannot retrieve contributors at this time Merging SparkR DataFrames Sarah Armstrong, Urban Institute July 7, 2016 Last Updated: August 18, 2016 Objective: The following tutorial provides an overview of how to join SparkR DataFrames by column and by row.

The join operation allows us to perform most SQL join types on SparkR DFs, including: inner (default): Returns rows where there is a match in both DFs outer: Returns rows where there is a match in both DFs, as well as rows in both the right and left DF where there was no match full, fullouter: Returns rows where there is a match in one of the DFs left, leftouter, leftouter: Returns all rows from the left DF, even if there are no matches in the right DF right, rightouter, rightouter: Returns all rows from the right DF, even if there are no matches in the left DF Cartesian: Returns the Cartesian product of the sets of records from the two or more joined DFs - join will return this DF when we do not specify a joinType nor a joinExpr (discussed below) We communicate to SparkR what condition we want to join DFs on with the joinExpr specification in join.

Below, we perform an inner (default) join on the DFs a and b on the condition that their loanid values be equal: ab1 join( a, b, a loanid b loanid ) str( ab1 ) Note that the resulting DF includes two (2) loanid columns.

Unfortunately, we cannot direct SparkR to keep only one of these columns when using join to merge by row, and the following command (which we introduced in the subsetting tutorial) drops both loanid columns: ab1 loanid NULL We can avoid this by renaming one of the columns before performing join and then, utilizing that the columns have distinct names, tell SparkR to drop only one of the columns.

For example, we could rename loanid in a with the expression a The merge operation, alternatively, allows us to join DFs and produces two (2) distinct merge columns.

We can use this feature to retain the column on which we joined the DFs, but we must still perform a withColumnRenamed step if we want our merge column to retain its original column name.

Rather than defining a joinExpr, we explictly specify the column(s) that SparkR should merge the DFs on with the operation parameters by and by.xby.y (if the merging column is named differently across the DFs).

Note that, if we do not specify by, SparkR will merge the DFs on the list of common column names shared by the DFs.

Rather than specifying a type of join, merge determines how SparkR should merge DFs based on boolean values, all.x and all.y, which indicate which rows in x and y should be included in the join, respectively.

We can specify merge type with the following parameter values: all.x FALSE, all.y FALSE: Returns an inner join (this is the default and can be achieved by not specifying values for all.x and all.y) all.x TRUE, all.y FALSE: Returns a left outer join all.x FALSE, all.y TRUE: Returns a right outer join all.x TRUE, all.y TRUE: Returns a full outer join The following merge expression is equivalent to the join expression in the preceding example: ab2 merge( a, b, by loanid ) str( ab2 ) Note that the two merging columns are distinct as indicated by the x and y name assignments performed by merge.

Below, we define A as a random subset of df with a row count that is approximately equal to half the size of nrow(df).

We use the DF operation except to create B, which includes every row of df, except for those included in A: A sample( df, withReplacement FALSE, fraction 0.5, seed 1 ) B except( df, A ) Lets also examine the row count for each subsetted row and confirm that A and B do not share common rows.

We can check this with the SparkR operation intersect, which performs the intersection set operation on two DFs: ( nA nrow( A )) ( nB nrow( B )) nA nB Equal to nrow(df) AintB intersect( A, B ) nrow( AintB ) (2i) Append rows when column name lists are equal across DFs: If we are certain that the two DFs have equivalent column name lists (with respect to both string values and column ordering), then appending the rows of one DF to another is straightforward.

Here, we append the rows of B to A with the rbind operation: df1 rbind( A, B ) nrow( df1 ) nrow( df ) We can see in the results above that df1 is equivalent to df.

We could, alternatively, accomplish this with the unionALL operation (e.g.

Append rows when DF column name lists are not equal: Before we can discuss appending rows when we do not have column name equivalency, we must first create two DataFrames that have different column names.

Append by an intersection of the two sets of column names, or 2.

Use withColumn to add columns to DF where they are missing and set each entry in the appended rows of these columns equal to NA.

Below is a function, rbind.intersect, that accomplishes the first approach.

We can see that, while the row count for df2 is equal to that for df, the DF does not include the loanage column (just as we expected).

A, B ) dim( df2 ) colnames( df2 ) unpersist( df2 ) rm( df2 ) Accomplishing the second approach is somewhat more involved.

The rbind.fill function, given below, identifies the outersection of the list of column names for two (2) DataFrames and adds them onto one (1) or both of the DataFrames as needed using withColumn.

By appending B to A with the rbind.fill function, therefore, we should have inserted exactly nrow(B) many empty string entries in df3.

Note that loanage is currently cast as string dtype and, therefore, the column does not contain any null values and we will need to recast the column to a numerical dtype.