Changig column classes is slow.

Open
#2,138 8 comments 0 reactions 0 assignees View on GitHub

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

performance

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

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from Rdatatable/data.table

All issues in Rdatatable/data.table

Similar issues

More R issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.