Kyle Kavanagh created ARROW-10088:
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Summary: [R] Integer64 incorrectly read into R data.table
Key: ARROW-10088
URL: https://issues.apache.org/jira/browse/ARROW-10088
Project: Apache Arrow
Issue Type: Bug
Components: R
Affects Versions: 1.0.1
Reporter: Kyle Kavanagh
I've got a proprietary dataset where one of the columns is an integer64 but all
of the values would fit within 32bits. As I understand it, arrow/feather will
downcast that column when the data is read back into R (not ideal IMO, but not
an issue generally). However, I'm having some trouble with a specific dataset.
When I read in the data, the column is set to the class "integer64", however
the column type (typeof) is 'integer' and not 'double', which is the underlying
type used by bit64. This mismatch causes R data.table to error out
([https://github.com/Rdatatable/data.table/blob/master/src/rbindlist.c#L325)]
I do not have any issue with integer64 columns which have values > 2^32, and
suspiciously I am also unable to recreate the issue by manually creating a
data.table with an int64 column with small values (e.g
data.table(col=as.integer64(c(1,2,3))) )
I did look thru the arrow::r cpp source and couldnt find an obvious case where
the underlying storage array would be an integer but also have the 'integer64'
class attr assigned... A fix would either be to remove the integer64 class
attr, or ensure that the underlying data store is a REALSXP instead of
INTEGERSXP
My company's network policies wont let me upload the sample dataset, hoping to
see if this triggers an immediate thoughts. If not, I can try to figure our
how to upload the dataset.
{code:java}
> arrow::write_feather(df[,list(testCol)][1], '~/test.feather')
> test = arrow::read_feather('~/test.feather')
> class(test$testCol)
[1] "integer64" "np.ulong"
> typeof(test$testCol)
[1] "integer"
> str(test)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1 obs. of 1 variable: $
testCol:Error in as.character.integer64(object) : REAL() can only be applied
to a 'numeric', not a 'integer'
#In the larger original dataset, it handles most columns properly, only the
'testCol' breaks things. Note the difference:
> typeof(df$goodCol)
[1] "double"
> class(df$goodCol)
[1] "integer64" "np.ulong"
> typeof(df$testCol)
[1] "integer"
> class(df$testCol)
[1] "integer64" "np.ulong"
> str(df)
Classes ‘data.table’ and 'data.frame': 214781 obs. of 17 variables:
$ goodCol :integer64 1599777000000604025 ...
$ testCol :Error in as.character.integer64(object) :
> sessionInfo()
R version 3.6.1 (2019-07-05)Platform: x86_64-pc-linux-gnu (64-bit)Running
under: Red Hat Enterprise Linux Server 7.7 (Maipo)
Matrix products: defaultBLAS: /usr/lib64/libblas.so.3.4.2LAPACK:
/usr/lib64/liblapack.so.3.4.2locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8
LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C [9] LC_ADDRESS=C
LC_TELEPHONE=C[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:[1] stats graphics grDevices utils datasets
methods baseother attached packages:[1] data.table_1.13.0 bit64_4.0.5
bit_4.0.4loaded via a namespace (and not attached): [1] Rcpp_1.0.5
lattice_0.20-41 arrow_1.0.1 [4] assertthat_0.2.1 rappdirs_0.3.1
grid_3.6.1 [7] R6_2.4.1 jsonlite_1.7.1 magrittr_1.5[10]
rlang_0.4.7 Matrix_1.2-18 vctrs_0.3.4[13] reticulate_1.14-9001
tools_3.6.1 glue_1.4.2[16] purrr_0.3.4 compiler_3.6.1
tidyselect_1.1.0{code}
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