CODEWITHSUNDEEP
rapache-sparkdataframemergesparkr
Maximilian Kohl
Maximilian Kohl

Reputation: 640

SparkR merge without creating duplicate columns

I decided to create this question because the answer provided to a very similar problem does not work for my scenario:

I want to merge these two SparkR dataframes df1

col1    col2    
1       11      
2       22      
3       33      
4       44

and df2

col1    col2    newcol
1       11      401
2       22      402
3       33      403
4       44      404

The dfs should be merged on columns 1 and 2. I tried all steps proposed in the link above (e.g. omitting "by" statement), but the result always looks like this:

col1_x  col2_x  col1_y  col2_y  newcol
1       11      1       11      401
2       22      2       22      402
3       33      3       33      403
4       44      4       44      404

I don't want to have duplicate columns and for now I have to delete col1_y, col2_y manually and rename col1_x, col2_x to only have col1, col2 and newcol as result:

df <- merge(df1, df2)  # <- how can I change this?
df$col1_y <- NULL
df$col2_y <- NULL
df <- withColumnRenamed(df, "col1_x", "col1")
df <- withColumnRenamed(df, "col2_x", "col2")

I tried all kinds of combinations using the by, by.x and by.y statement but none worked. Is there a way I can simplify this tedious correction chain by omitting one of the steps? Thank you!

Upvotes: 3

Views: 928

Answers (1)

mtoto
mtoto

Reputation: 24188

This is expected behaviour, one option is to select() relevant columns from the resulting DataFrame:

library(magrittr) 

# We'll use join to avoid suffixes '_x' & '_y'
join(df1,df2,(df1$col1==df2$col1)&(df1$col2==df2$col2)) %>%
  select(df1$col1,df1$col2,df2$newcol) %>%
  showDF()
#+----+----+------+
#|col1|col2|newcol|
#+----+----+------+
#|   1|11.0|   401|
#|   2|22.0|   402|
#|   3|33.0|   403|
#|   4|55.0|   404|
#+----+----+------+

Upvotes: 1

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