[GitHub] spark issue #17159: [SPARK-19818][SparkR] union should check for name consis...
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 --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #17159: [SPARK-19818][SparkR] union should check for name consis...
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 from base R, which checks name
consistency. See examples below. Should I make the change to `rbind`, or leave
it as is and close this PR? Thanks.
```
df <- data.frame(name = c("Michael", "Andy", "Justin"), age = c(1, 30, 19))
df2 <- df
names(df2)[1] <- "name2"
# 1. SQL
library(sqldf)
query <- "select * from df union all select * from df2"
sqldf(query)
name age
1 Michael 1
2Andy 30
3 Justin 19
4 Michael 1
5Andy 30
6 Justin 19
# 2. rbind
rbind(df, df2)
Error in match.names(clabs, names(xi)) :
names do not match previous names
```
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[GitHub] spark issue #17159: [SPARK-19818][SparkR] union should check for name consis...
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 fit
```
>>> d = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
>>> l = spark.createDataFrame([(1, 2)])
>>> d.union(l).head(2)
[Row(age=1, name=u'Alice'), Row(age=1, name=u'2')]
>>> l.dtypes
[('_1', 'bigint'), ('_2', 'bigint')]
>>> d.dtypes
[('age', 'bigint'), ('name', 'string')]
```
Do you see this as something that might be unexpected for R users (in which
case `rbind` might be the overload to look into) or SQL users (documented as
equivalent to SQL UNION ALL)?
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[GitHub] spark issue #17159: [SPARK-19818][SparkR] union should check for name consis...
Github user actuaryzhang commented on the issue:
https://github.com/apache/spark/pull/17159
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, 19)))
union(df, df[, c(2, 1)])
name age
1 Michael 1.0
2Andy30.0
3 Justin19.0
4 1.0 Michael
```
@felixcheung
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