Hey Igor,
I have been dealing with *CSV*/*XLSX* files from time to time and depending
on the size of those files you are mentioning, 180 seconds isn't really
that much.
>From my experience, *vroom *is the fastest I've encountered but it deals
with *CSV* files (I can support its usage for up to
According to my internet research, it looks like readxl is the fastest
package.
The profvis package indicated that the bottleneck is indeed in importing
the files.
My processor has six cores, but when I use four of them the computer
crashes completely. When I use three processors, it's still
It looks like you are reading directly from URLs? How do you know the delay is
not network I/O delay?
Parallel computation is not a panacea. It allows tasks _that are CPU-bound_ to
get through the CPU-intensive work faster. You need to be certain that your
tasks actually can benefit from
On Tue, 4 Oct 2022 15:29:54 -0300
Igor L wrote:
> The problem is that importing files in xlsx format is time consuming.
Do the openxlsx or XLConnect packages fare any better?
> plan(strategy = future::multicore(workers = 4))
As far as I understand the documentation, multicore only works on
