vkhodygo opened a new issue, #36847:
URL: https://github.com/apache/arrow/issues/36847
### Describe the enhancement requested
I have certain columns in my CSV files that are integers but stored as
floats , i.e., years etc.
```bash
$ cat test.csv
a,b
1.0,2.1
2.0,2
3,3.33
```
I'd like to read them as integers explicitly, and I do the following:
```R
t <- open_csv_dataset("./test.csv",
skip = 1,
schema = schema(
field("a", uint16(), nullable = FALSE),
field("b", float32(), nullable = FALSE)
)
)
t |> collect()
Error in `compute.Dataset()`:
! Invalid: Could not open CSV input source '/home/usr/test.csv': Invalid: In
CSV column #0: Row #2: CSV conversion error to uint16: invalid value '1.0'
ℹ If you have supplied a schema and your data contains a header row, you
should supply the argument `skip = 1` to prevent the header being read in as
data.
Run `rlang::last_trace()` to see where the error occurred.
```
Obviously, I could follow
https://github.com/apache/arrow/issues/12469#issuecomment-1046246626 and just
cast, but this approach clutters the code.
@thisisnic What are your thoughts regarding this?
### Component(s)
R
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