Changig column classes is slow.
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
- Issue type
- Bug
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- r
- Domain
- performance
Research direction
Start by benchmarking the reported mydata[, names(mydata) := lapply(.SD, as.character)] operation against fread(..., colClasses=list(character=1:3775)) on similarly large mixed-type data. Trace the column-class conversion path and measure whether an alternative or improvement avoids the reported slowdown; done means faster conversion with results equivalent to the current method.
Written by the indexing model from the issue text.
Description
I'm working with large datasets (around 4GB), mixing dates, numbers and factors.
And I want to perform operations such as changing all column classes to character.
mydata[, names(mydata) := lapply(.SD, as.character)]
But it's quite slow.
If you just had the data on the disk and reload it with
my <- fread("mydata.csv", stringsAsFactors=F, colClasses=list(character=1:3775))
it's much faster.
Is there alternative to the first method?
Or maybe it could be improved in some way.
- Dominant language
- R
- Stars
- 3.9k
- Forks
- 1.1k
- Avg merge
- 14h 4m
- Merged PRs (30d)
- 4
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