Github user felixcheung commented on the issue:
https://github.com/apache/spark/pull/17159
I think it's a good idea to get SparkR `rbind` to match behavior of R
`data.frame` `rbind`.
We should clearly indicate the difference between SparkR `union` and
`rbind` then
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Github user actuaryzhang commented on the issue:
https://github.com/apache/spark/pull/17159
@felixcheung OK, did not know it was by design. It does seem that the
`union` behavior is similar to R's SQL (in `sqldf`), but as you pointed out,
the overload method `rbind` is different
Github user felixcheung commented on the issue:
https://github.com/apache/spark/pull/17159
hmm... this is somewhat by design - `union` could take in 2 DataFrames that
might not match in column names or type. In that case values in one of the
DataFrame will be coerced to make things
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Github user actuaryzhang commented on the issue:
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The current implementation accepts data frames with different schemas. See
issues below:
```
df <- createDataFrame(data.frame(name = c("Michael", "Andy", "Justin"), age
= c(1, 30,
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