![]() ![]() You can pass a vector of URLs to the datasets into future_map so it downloads each file as determined by the future package processing: data_urls <- c("https./data.csv", "https./data2. I was just downloading a very small dataset ( iris.csv), so maybe on larger datasets that take more time, the time taken to open an R session would be offset by the time it takes to download larger files. ![]() I am just guessing here, but the reason that multisession is slower could be because it has to open up several R sessions before running the download.file function. Keep in mind, I am using Ubuntu, so using Windows will likely change things, since as far as I understand future doesn't allow multicore on Windows. Or go on exploration, meet other survivors, and uncover the truth about the fall of humanity. Build, craft, farm, fish, cook - with a view. Respected Ghanaian singer, Shatta Wale unleashed this record Halleluyah (I Am Da Future). Use multiprocess if you are unsure what platform your code will be run on). Welcome to the coziest post-apocalypse ever. Download Shatta Wale - Halleluyah (I Am Da Future) Mp3 Audio Music. Using multicore substantially increases the downloading speed ( Note: on Windows, multicore is not available, only multisession. I think what you mean is furrr::future_map. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |