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Download File R



What happens to the destination file(s) in the case of error depends on the method and R version. Currently the "internal", "wininet" and "libcurl" methods will remove the file if there the URL is unavailable except when mode specifies appending when the file should be unchanged.




Download File R



Proxies can be specified via environment variables. Setting no_proxy to * stops any proxy being tried. Otherwise the setting of http_proxy or ftp_proxy (or failing that, the all upper-case version) is consulted and if non-empty used as a proxy site. For FTP transfers, the username and password on the proxy can be specified by ftp_proxy_user and ftp_proxy_password. The form of http_proxy should be or :8080/ where the port defaults to 80 and the trailing slash may be omitted. For ftp_proxy use the form :3128/ where the default port is 21. These environment variables must be set before the download code is first used: they cannot be altered later by calling Sys.setenv.


Under Windows, if http_proxy_user is set to ask then a dialog box will come up for the user to enter the username and password. NB: you will be given only one opportunity to enter this, but if proxy authentication is required and fails there will be one further prompt per download.


This is an issue for method = "libcurl" on Windows, where the OS does not provide a suitable CA certificate bundle, so by default on Windows certificates are not verified. To turn verification on, set environment variable CURL_CA_BUNDLE to the path to a certificate bundle file, usually named ca-bundle.crt or curl-ca-bundle.crt. (This is normally done for a binary installation of R, which installs R_HOME/etc/curl-ca-bundle.crt and sets CURL_CA_BUNDLE to point to it if that environment variable is not already set.) For an updated certificate bundle, see Currently one can download a copy from -bundle/master/ca-bundle.crt and set CURL_CA_BUNDLE to the full path to the downloaded file.


If you use download.file in a package or script, you must check the return value, since it is possible that the download will fail with a non-zero status but not an R error. (This was more likely prior to R 3.4.0.)


The function download.file can be used to download a single file as described by url from the internet and store it in destfile. The url must start with a scheme such as http://, https://, ftp:// or file://.


When method "libcurl" is used, it provides (non-blocking) access to https:// and (usually) ftps:// URLs. There is support for simultaneous downloads, so url and destfile can be character vectors of the same length greater than one (but the method has to be specified explicitly and not via "auto"). For a single URL and quiet = FALSE a progress bar is shown in interactive use.


See url for how file:// URLs are interpreted, especially on Windows. The "internal" and "wininet" methods do not percent-decode file:// URLs, but the "libcurl" and "curl" methods do: method "wget" does not support them.


If the file length is known, the full width of the bar is the known length. Otherwise the initial width represents 100 Kbytes and is doubled whenever the current width is exceeded. (In non-interactive use this uses a text version. If the file length is known, an equals sign represents 2% of the transfer completed: otherwise a dot represents 10Kb.)


The choice of binary transfer (mode = "wb" or "ab") is important on Windows, since unlike Unix-alikes it does distinguish between text and binary files and for text transfers changes \n line endings to \r\n (aka CRLF).


Offering the curl package as an alternative that I found to be reliable when extracting large files from an online database. In a recent project, I had to download 120 files from an online database and found it to half the transfer times and to be much more reliable than download.file.


In this case, rough timing on your URL showed no consistent difference in transfer times. In my application, using curl_download in a script to select and download 120 files from a website decreased my transfer times from 2000 seconds per file to 1000 seconds and increased the reliability from 50% to 2 failures in 120 files. The script is posted in my answer to a question I asked earlier, see .


The R script files that contain the functions used in the chapters are available here. Each chapter contains one or more R functions which are helpful in examining the various applications presented in the chapters. The R functions are explained in each chapter according to the basic input, process, and output operations. The R functions can be opened and run in the R GUI Console window or in R Studio. Results will be the same as in the chapters, except where it is noted that the set.seed () command was not used.


When method "libcurl" is used, there is support forsimultaneous downloads, so url and destfile can becharacter vectors of the same length greater than one (but the methodhas to be specified explicitly and not via "auto"). Fora single URL and quiet = FALSE a progress bar is shown ininteractive use.


The timeout for many parts of the transfer can be set by the optiontimeout which defaults to 60 seconds. This is ofteninsufficient for downloads of large files (50MB or more) andso should be increased when download.file is used in packagesto do so. Note that the user can set the default timeout by theenvironment variable R_DEFAULT_INTERNET_TIMEOUT in recentversions of R, so to ensure that this is not decreased packages shoulduse something like


If the file length is known, thefull width of the bar is the known length. Otherwise the initialwidth represents 100 Kbytes and is doubled whenever the current widthis exceeded. (In non-interactive use this uses a text version. If thefile length is known, an equals sign represents 2% of the transfercompleted: otherwise a dot represents 10Kb.)


What happens to the destination file(s) in the case of error dependson the method and R version. Currently the "internal","wininet" and "libcurl" methods will remove the file ifthe URL is unavailable except when mode specifiesappending when the file should be unchanged.


I am trying to create a package to download, import and clean data from the Dominican Republic Central Bank web page. I have done all the coding in Rstudio.cloud and everything works just fine, but when I try the functions in my local machine they do not work.


The Department of Criminal Justice in Texas keeps records of every inmate they execute. This tutorial will show you how to scrape that data, which lives in a table on the website and download the images.


The tutorial uses rvest and xml to scrape tables, purrr to download and export files, and magick to manipulate images. For an introduction to R Studio go here and for help with dplyr go here.


As you can see, downloadHandler takes a filename argument, which tells the web browser what filename to default to when saving. This argument can either be a simple string, or it can be a function that returns a string (as is the case here).


The content argument must be a function that takes a single argument, the file name of a non-existent temp file. The content function is responsible for writing the contents of the file download into that temp file.


Both the filename and content arguments can use reactive values and expressions (although in the case of filename, if you are using a reactive value, be sure your argument is an actual function; filename = paste(input$dataset, ".csv", sep = "") will not work the way you want it to, since it is evaluated only once, when the download handler is being defined).


Call renv::snapshot() again to save the state ofyour project library if your attempts to update R packages weresuccessful, or call renv::restore() to revert to theprevious state as encoded in the lockfile if your attempts to updatepackages introduced some new problems.


The renv::init() function attempts to ensure thenewly-created project library includes all R packages currently used bythe project. It does this by crawling R files within the project fordependencies with the renv::dependencies() function. Thediscovered packages are then installed into the project library with therenv::hydrate() function, which will also attempt to savetime by copying packages from your user library (rather thanreinstalling from CRAN) as appropriate.


Calling renv::init() will also write out theinfrastructure necessary to automatically load and use the privatelibrary for new R sessions launched from the project root directory.This is accomplished by creating (or amending) a project-local.Rprofile with the necessary code to load the project whenthe R session is started.


In addition, be aware that package installation may fail if a packagewas originally installed through a CRAN-available binary, but thatbinary is no longer available. renv will attempt to installthe package from sources in this situation, but attempts to install fromsource can (and often do) fail due to missing system prerequisites forcompilation of a package. The renv::equip() function may beuseful in these scenarios, especially on Windows: it will downloadexternal software commonly used when compiling R packages from sources,and instruct R to use that software during compilation.


In particular, renv/activate.R ensures that the projectlibrary is made active for newly launched R sessions. It isautomatically sourced via a call tosource("renv/activate.R"), which is inserted into theproject .Rprofile when renv::init() orrenv::activate() is called. This ensures that any new Rprocesses launched within the project directory will use the projectlibrary, and hence are isolated from the regular user library.


For development and collaboration, the .Rprofile,renv.lock and renv/activate.R files should becommitted to your version control system; the renv/librarydirectory should normally be ignored. Note thatrenv::init() will attempt to write the requisite ignorestatements to the project .gitignore. 041b061a72


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