[
https://issues.apache.org/jira/browse/ARROW-13887?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17425014#comment-17425014
]
Dragoș Moldovan-Grünfeld edited comment on ARROW-13887 at 10/6/21, 2:32 PM:
----------------------------------------------------------------------------
I think there might be a bit more than meets the eye to this issue, to the
point that it becomes a different conversation (about the API of the function).
Both `col_types` and `schema` arguments accept a `Schema` object, but the way
they treat it are a bit different:
* `schema` errors if the CSV file has column headers, while
* `col_types` ignores it the column names. (although the documentation states
that the names in the object and in the column headers *must* match, nothing
happens if they don't)
A slightly modified example:
{code:java}
library(arrow)
share_data <- tibble::tibble(
company = c("AMZN", "GOOG", "BKNG", "TSLA"),
price = c(3463.12, 2884.38, 2300.46, 732.39)
)
tf <- tempfile()
write.csv(share_data, tf, row.names = FALSE)
share_schema <- schema(
company = utf8(),
price = float64()
)
# this errors
read_csv_arrow(tf, schema = share_schema)
# this works
read_csv_arrow(tf, col_types = share_schema)
conflict_col_names_schema <- schema(
company_test = utf8(),
price = float64()
)
# this works, but as per the current documentation it should error, but
# read_csv_arrow() simply ignores the mismatched column names
# (Schema object vs CSV file)
read_csv_arrow(tf, col_types = conflict_col_names_schema)
unlink(tf){code}
was (Author: dragosmg):
I think there might be a bit more than meets the eye to this issue, to the
point that it becomes a different conversation (about the API of the function).
Both `col_types` and `schema` arguments accept a `Schema` object, but the way
they treat it are a bit different:
* `schema` errors if the CSV file has column headers, while
* `col_types` ignores it the column names. (although the documentation states
that the names in the object and in the column headers *must* match, nothing
happens if they don't)
A slightly modified example:
{code:java}
// code placeholder
library(arrow)
share_data <- tibble::tibble(
company = c("AMZN", "GOOG", "BKNG", "TSLA"),
price = c(3463.12, 2884.38, 2300.46, 732.39)
)
tf <- tempfile()
write.csv(share_data, tf, row.names = FALSE)
share_schema <- schema(
company = utf8(),
price = float64()
)
# this errors
read_csv_arrow(tf, schema = share_schema)
# this works
read_csv_arrow(tf, col_types = share_schema)
conflict_col_names_schema <- schema(
company_test = utf8(),
price = float64()
)
# this works, but as per the current documentation it should error, but
# read_csv_arrow() simply ignores the mismatched column names
# (Schema object vs CSV file)
read_csv_arrow(tf, col_types = conflict_col_names_schema)
unlink(tf){code}
> [R] Capture error produced when reading in CSV file with headers and using a
> schema, and add suggestion
> -------------------------------------------------------------------------------------------------------
>
> Key: ARROW-13887
> URL: https://issues.apache.org/jira/browse/ARROW-13887
> Project: Apache Arrow
> Issue Type: Improvement
> Components: R
> Reporter: Nicola Crane
> Assignee: Dragoș Moldovan-Grünfeld
> Priority: Major
> Labels: good-first-issue
> Fix For: 6.0.0
>
>
> When reading in a CSV with headers, and also using a schema, we get an error
> as the code tries to read in the header as a line of data.
> {code:java}
> share_data <- tibble::tibble(
> company = c("AMZN", "GOOG", "BKNG", "TSLA"),
> price = c(3463.12, 2884.38, 2300.46, 732.39)
> )
> readr::write_csv(share_data, file = "share_data.csv")
> share_schema <- schema(
> company = utf8(),
> price = float64()
> )
> read_csv_arrow("share_data.csv", schema = share_schema)
> {code}
> {code:java}
> Error: Invalid: In CSV column #1: CSV conversion error to double: invalid
> value 'price'
> /home/nic2/arrow/cpp/src/arrow/csv/converter.cc:492 decoder_.Decode(data,
> size, quoted, &value)
> /home/nic2/arrow/cpp/src/arrow/csv/parser.h:84 status
> /home/nic2/arrow/cpp/src/arrow/csv/converter.cc:496
> parser.VisitColumn(col_index, visit) {code}
> The correct thing here would have been for the user to supply the argument
> {{skip=1}} to {{read_csv_arrow()}} but this is not immediately obvious from
> the error message returned from C++. We should capture the error and instead
> supply our own error message using {{rlang::abort}} which informs the user of
> the error and then suggests what they can do to prevent it.
>
> For similar examples (and their associated PRs) see
> {color:#1d1c1d}ARROW-11766, and ARROW-12791{color}
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
