Choose a program with a clean, straight-forward interface that you will find easy to use. A complicated interface with lots of confusing search parameters will not be your best friend. User-friendliness – If you are not very tech-savvy, you will want to choose a subtitle downloading program that is easy to use. What features do you consider important in searching for and downloading subtitle files? What features will make the process easier for you? When considering programs, keep the following aspects in mind:įeatures – Keep in mind the features you need in a subtitle downloading program. 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.We hope this article has helped you decide which subtitle downloading program will best fit your needs. 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. Use multiprocess if you are unsure what platform your code will be run on). 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.
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