fread may silently discard rows with only complete columns selected

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Assessment

Difficulty
3/5
Estimated time
1-2 days
Newbie friendliness
45/100
Issue type
Bug
Clarity
Mostly clear
Activity status
Stale
Tech stack
r
Domain
data

Research direction

Start with the fread entry point and reproduce the five text examples in the issue, focusing on how trailing delimiters, missing fields, and select=1:2 affect row handling. Add regression coverage for these inputs and verify that no rows are silently discarded while the selected columns and warnings remain consistent.

Written by the indexing model from the issue text.

Description

fread

I'm working with a csv-like data obtained from an online source (e.g. sina stock prices) where all except the last row end up with an extra comma. I replace all = with ,. Still, fread may not work properly with it.
The following are some examples to demonstrate the cases where fread may silently discard rows.

library(data.table)

fread(text = "a,10.0,0\nb,15.2,0\nc,10.3,0\n", header = FALSE, col.names = c("name", "value"), select = 1:2)
#>    name value
#> 1:    a  10.0
#> 2:    b  15.2
#> 3:    c  10.3
fread(text = "a,10.0,\nb,15.2,\nc,10.3,\n", header = FALSE, col.names = c("name", "value"), select = 1:2)
#>    name value
#> 1:    a  10.0
#> 2:    b  15.2
#> 3:    c  10.3
fread(text = "a,10.0,\nb,15.2,\nc,10.3\n", header = FALSE, col.names = c("name", "value"), select = 1:2)
#> Warning in fread(text = "a,10.0,\nb,15.2,\nc,10.3\n", header = FALSE,
#> col.names = c("name", : Discarded single-line footer: <<c,10.3>>
#>    name value
#> 1:    a  10.0
#> 2:    b  15.2
fread(text = "a,10.0,\nb,15.2\nc,10.3\n", header = FALSE, col.names = c("name", "value"), select = 1:2)
#>    name value
#> 1:    b  15.2
#> 2:    c  10.3
fread(text = "a,10.0,\nb,15.2\nc,10.3,\n", header = FALSE, col.names = c("name", "value"), select = 1:2)
#>    name value
#> 1:    c  10.3
fread(text = "a,10.0,\nb,15.2\n", header = FALSE, col.names = c("name", "value"), select = 1:2)
#>    name value
#> 1:    b  15.2

There seems to be obvious inconsistencies in the examples above.

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