The curl package provides bindings to the libcurl C library for R. The package supports retrieving data in-memory, downloading to disk, or streaming using the R “connection” interface. Some knowledge of curl is recommended to use this package. For a more user-friendly HTTP client, have a look at the httr package which builds on curl with HTTP specific tools and logic.
The curl package implements several interfaces to retrieve data from a URL:
curl_fetch_memory() saves response in memorycurl_download() or curl_fetch_disk()
writes response to diskcurl() or curl_fetch_stream() streams
response datacurl_fetch_multi() (Advanced) process responses via
callback functionsEach interface performs the same HTTP request, they only differ in how response data is processed.
The curl_fetch_memory function is a blocking interface
which waits for the request to complete and returns a list with all
content (data, headers, status, timings) of the server response.
List of 10
$ url : chr "https://hb.cran.dev/get?foo=123"
$ status_code : int 200
$ type : chr "application/json"
$ headers : raw [1:846] 48 54 54 50 ...
$ modified : POSIXct[1:1], format: NA
$ times : Named num [1:6] 0 0.0234 0.0247 0.0486 0.0807 ...
..- attr(*, "names")= chr [1:6] "redirect" "namelookup" "connect" "pretransfer" ...
$ scheme : chr "HTTPS"
$ http_version: int 2
$ method : chr "GET"
$ content : raw [1:509] 7b 0a 20 20 ... [1] "HTTP/2 200 "
[2] "date: Mon, 25 Nov 2024 13:31:26 GMT"
[3] "content-type: application/json"
[4] "access-control-allow-origin: *"
[5] "access-control-allow-credentials: true"
[6] "x-powered-by: Flask"
[7] "x-processed-time: 0"
[8] "cf-cache-status: DYNAMIC"
[9] "report-to: {\"endpoints\":[{\"url\":\"https:\\/\\/a.nel.cloudflare.com\\/report\\/v4?s=Bj6BAmSkDfSeCl3FETrzG%2BOOEb3wszYkidDbvJU18brAwy91RBt1iSSj6eqKTi73CDpW8UDXKv8oeiS3%2FF3ruG%2FsS%2Fo%2BM6HilS4otIUsi0tZyAt1Cih7R4h%2FIN%2FbLw%3D%3D\"}],\"group\":\"cf-nel\",\"max_age\":604800}"
[10] "nel: {\"success_fraction\":0,\"report_to\":\"cf-nel\",\"max_age\":604800}"
[11] "server: cloudflare"
[12] "cf-ray: 8e81ffc2397f387c-IAD"
[13] "content-encoding: zstd"
[14] "alt-svc: h3=\":443\"; ma=86400"
[15] "server-timing: cfL4;desc=\"?proto=TCP&rtt=1102&sent=5&recv=8&lost=0&retrans=0&sent_bytes=3377&recv_bytes=831&delivery_rate=2689265&cwnd=252&unsent_bytes=0&cid=70f3b61b8c535654&ts=53&x=0\"" {
"args": {
"foo": "123"
},
"headers": {
"Accept": "*/*",
"Accept-Encoding": "gzip, br",
"Cdn-Loop": "cloudflare; loops=1",
"Cf-Connecting-Ip": "4.227.175.100",
"Cf-Ipcountry": "US",
"Cf-Ray": "8e81ffc2397f387c-EWR",
"Cf-Visitor": "{\"scheme\":\"https\"}",
"Connection": "close",
"Host": "httpbin:8080",
"User-Agent": "R (4.4.2 x86_64-pc-linux-gnu x86_64 linux-gnu)"
},
"origin": "4.227.175.100",
"url": "https://httpbin:8080/get?foo=123"
}
The curl_fetch_memory interface is the easiest interface
and most powerful for building API clients. However it is not suitable
for downloading really large files because it is fully in-memory. If you
are expecting 100G of data, you probably need one of the other
interfaces.
The second method is curl_download, which has been
designed as a drop-in replacement for download.file in
r-base. It writes the response straight to disk, which is useful for
downloading (large) files.
tmp <- tempfile()
curl_download("https://hb.cran.dev/get?bar=456", tmp)
jsonlite::prettify(readLines(tmp)){
"args": {
"bar": "456"
},
"headers": {
"Accept": "*/*",
"Accept-Encoding": "gzip, br",
"Cdn-Loop": "cloudflare; loops=1",
"Cf-Connecting-Ip": "4.227.175.100",
"Cf-Ipcountry": "US",
"Cf-Ray": "8e81ffc2aa1b387c-EWR",
"Cf-Visitor": "{\"scheme\":\"https\"}",
"Connection": "close",
"Host": "httpbin:8080",
"User-Agent": "R (4.4.2 x86_64-pc-linux-gnu x86_64 linux-gnu)"
},
"origin": "4.227.175.100",
"url": "https://httpbin:8080/get?bar=456"
}
The most flexible interface is the curl function, which
has been designed as a drop-in replacement for base url. It
will create a so-called connection object, which allows for incremental
(asynchronous) reading of the response.
con <- curl("https://hb.cran.dev/get")
open(con)
# Get 3 lines
out <- readLines(con, n = 3)
cat(out, sep = "\n"){
"args": {},
"headers": { "Accept": "*/*",
"Accept-Encoding": "gzip, br",
"Cdn-Loop": "cloudflare; loops=1", "Cf-Connecting-Ip": "4.227.175.100",
"Cf-Ipcountry": "US",
"Cf-Ray": "8e81ffc35d8bc9a3-EWR",
"Cf-Visitor": "{\"scheme\":\"https\"}",
"Connection": "close",
"Host": "httpbin:8080",
"User-Agent": "R (4.4.2 x86_64-pc-linux-gnu x86_64 linux-gnu)"
},
"origin": "4.227.175.100",
"url": "https://httpbin:8080/get"
}The example shows how to use readLines on an opened
connection to read n lines at a time. Similarly
readBin is used to read n bytes at a time for
stream parsing binary data.
As of version 2.3 it is also possible to open connections in
non-blocking mode. In this case readBin and
readLines will return immediately with data that is
available without waiting. For non-blocking connections we use
isIncomplete to check if the download has completed
yet.
# This httpbin mirror doesn't cache
con <- curl("https://nghttp2.org/httpbin/drip?duration=1&numbytes=50")
open(con, "rb", blocking = FALSE)
while(isIncomplete(con)){
buf <- readBin(con, raw(), 1024)
if(length(buf))
cat("received: ", rawToChar(buf), "\n")
}
close(con)The curl_fetch_stream function provides a very simple
wrapper around a non-blocking connection.
As of curl 2.0 the package provides an async interface
which can perform multiple simultaneous requests concurrently. The
curl_fetch_multi adds a request to a pool and returns
immediately; it does not actually perform the request.
pool <- new_pool()
success <- function(req){cat("success:", req$url, ": HTTP:", req$status, "\n")}
failure <- function(err){cat("failure:", err, "\n")}
curl_fetch_multi('https://www.google.com', done = success, fail = failure, pool = pool)
curl_fetch_multi('https://cloud.r-project.org', done = success, fail = failure, pool = pool)
curl_fetch_multi('https://hb.cran.dev/blabla', done = success, fail = failure, pool = pool)
curl_fetch_multi('https://doesnotexit.xyz', done = success, fail = failure, pool = pool)When we call multi_run(), all scheduled requests are
performed concurrently. The callback functions get triggered when each
request completes.
failure: Could not resolve host: doesnotexit.xyz
success: https://www.google.com/ : HTTP: 200
success: https://cloud.r-project.org/ : HTTP: 200
success: https://hb.cran.dev/blabla : HTTP: 404 $success
[1] 3
$error
[1] 1
$pending
[1] 0The system allows for running many concurrent non-blocking requests. However it is quite complex and requires careful specification of handler functions.
A HTTP requests can encounter two types of errors:
The first type of errors (connection failures) will always raise an
error in R for each interface. However if the requests succeeds and the
server returns a non-success HTTP status code, only curl()
and curl_download() will raise an error. Let’s dive a
little deeper into this.
The curl and curl_download functions are
safest to use because they automatically raise an error if the request
was completed but the server returned a non-success (400 or higher) HTTP
status. This mimics behavior of base functions url and
download.file. Therefore we can safely write code like
this:
# This is OK
curl_download('https://cloud.r-project.org/CRAN_mirrors.csv', 'mirrors.csv')
mirros <- read.csv('mirrors.csv')
unlink('mirrors.csv')If the HTTP request was unsuccessful, R will not continue:
# Oops! A typo in the URL!
curl_download('https://cloud.r-project.org/CRAN_mirrorZ.csv', 'mirrors.csv')Error in curl_download("https://cloud.r-project.org/CRAN_mirrorZ.csv", : HTTP response code said error [cloud.r-project.org]: The requested URL returned error: 404Warning: Failed to open 'https://cloud.r-project.org/CRAN_mirrorZ.csv': The requested URL returned error: 404Error in open.connection(con): cannot open the connectionWhen using any of the curl_fetch_* functions it is
important to realize that these do not raise an error
if the request was completed but returned a non-200 status code. When
using curl_fetch_memory or curl_fetch_disk you
need to implement such application logic yourself and check if the
response was successful.
[1] 200Same for downloading to disk. If you do not check your status, you might have downloaded an error page!
# Oops a typo!
req <- curl_fetch_disk('https://cloud.r-project.org/CRAN_mirrorZ.csv', 'mirrors.csv')
print(req$status_code)[1] 404[1] "<!DOCTYPE HTML PUBLIC \"-//IETF//DTD HTML 2.0//EN\">" "<html><head>"
[3] "<title>404 Not Found</title>" "</head><body>"
[5] "<h1>Not Found</h1>" "<p>The requested URL was not found on this server.</p>"If you do want the curl_fetch_* functions to
automatically raise an error, you should set the FAILONERROR
option to TRUE in the handle of the request.
h <- new_handle(failonerror = TRUE)
curl_fetch_memory('https://cloud.r-project.org/CRAN_mirrorZ.csv', handle = h)Error in curl_fetch_memory("https://cloud.r-project.org/CRAN_mirrorZ.csv", : HTTP response code said error [cloud.r-project.org]: The requested URL returned error: 404By default libcurl uses HTTP GET to issue a request to an HTTP url. To send a customized request, we first need to create and configure a curl handle object that is passed to the specific download interface.
Creating a new handle is done using new_handle. After
creating a handle object, we can set the libcurl options and http
request headers.
h <- new_handle()
handle_setopt(h, copypostfields = "moo=moomooo");
handle_setheaders(h,
"Content-Type" = "text/moo",
"Cache-Control" = "no-cache",
"User-Agent" = "A cow"
)Use the curl_options() function to get a list of the
options supported by your version of libcurl. The libcurl
documentation explains what each option does. Option names are not
case sensitive.
It is important you check the libcurl documentation to set options of the correct type. Options in libcurl take several types:
The R bindings will automatically do some type checking and coercion
to convert R values to appropriate libcurl option values. Logical
(boolean) values in R automatically get converted to 0 or
1 for example CURLOPT_VERBOSE:
However R does not know if an option is actually boolean. So passing
TRUE/ FALSE to any numeric option will simply
set it to 0 or 1 without a warning or error.
If an option value cannot be coerced, you get an error:
Error in handle_setopt(handle, maxfilesize = "foo"): Value for option maxfilesize (114) must be a number.Error in handle_setopt(handle, useragent = 12345): Value for option useragent (10018) must be a string or raw vector.Some curl options take an long in C that actually corresponds to an ENUM value.
For example the CURLOPT_USE_SSL
docs explains that there are 4 possible values for this option:
CURLUSESSL_NONE, CURLUSESSL_TRY,
CURLUSESSL_CONTROL, and CURLUSESSL_ALL. To use
this option you have to lookup the integer values for these enums in the
symbol table. These symbol values never change, so you only need to
lookup the value you need once and then hardcode the integer value in
your R code.
name introduced deprecated removed value type
1111 CURLUSESSL_ALL 7.17.0 <NA> <NA> 3 <NA>
1112 CURLUSESSL_CONTROL 7.17.0 <NA> <NA> 2 <NA>
1113 CURLUSESSL_NONE 7.17.0 <NA> <NA> 0 <NA>
1114 CURLUSESSL_TRY 7.17.0 <NA> <NA> 1 <NA>So suppose we want to set CURLOPT_USE_SSL to
CURLUSESSL_ALL we would use this R code:
Another example is the CURLOPT_HTTP_VERSION option. This option is needed to disable or enable HTTP/2. However some users are not aware this is actually an ENUM and not a regular numeric value!
The docs explain HTTP_VERSION can be set to one of several strategies for negotiating the HTTP version between client and server. Valid values are:
name introduced deprecated removed value type
35 CURL_HTTP_VERSION_1_0 7.9.1 <NA> <NA> 1 <NA>
36 CURL_HTTP_VERSION_1_1 7.9.1 <NA> <NA> 2 <NA>
37 CURL_HTTP_VERSION_2 7.43.0 <NA> <NA> 3 <NA>
38 CURL_HTTP_VERSION_2_0 7.33.0 <NA> <NA> 3 <NA>
39 CURL_HTTP_VERSION_2_PRIOR_KNOWLEDGE 7.49.0 <NA> <NA> 5 <NA>
40 CURL_HTTP_VERSION_2TLS 7.47.0 <NA> <NA> 4 <NA>
41 CURL_HTTP_VERSION_3 7.66.0 <NA> <NA> 30 <NA>
42 CURL_HTTP_VERSION_3ONLY 7.88.0 <NA> <NA> 31 <NA>
43 CURL_HTTP_VERSION_NONE 7.9.1 <NA> <NA> 0 <NA>As seen, the value 2 corresponds to
CURL_HTTP_VERSION_1_1 and 3 corresponds to
CURL_HTTP_VERSION_2_0.
As of libcurl 7.62.0, the default http_version is
CURL_HTTP_VERSION_2TLS which uses HTTP/2 when possible, but
only for HTTPS connections. Package authors should usually leave the
default to let curl select the best appropriate http protocol.
One exception is when writing a client for a server that seems to be
running a buggy HTTP/2 server. Unfortunately this is not uncommon, and
curl is a bit more picky than browsers. If you are frequently seeing
Error in the HTTP2 framing layer error messages, then there
is likely a problem with the HTTP/2 layer on the server.
The easiest remedy is to disable http/2 for this
server by forcing http 1.1 until the service has upgraded their
webservers. To do so, set the http_version to
CURL_HTTP_VERSION_1_1 (value: 2):
# Force using HTTP 1.1 (the number 2 is an enum value, see above)
handle_setopt(handle, http_version = 2)Note that the value 1 corresponds to HTTP 1.0 which is a
legacy version of HTTP that you should not use! Code that sets
http_version to 1 (or even 1.1
which R simply rounds to 1) is almost always a bug.
After the handle has been configured, it can be used with any of the
download interfaces to perform the request. For example
curl_fetch_memory will load store the output of the request
in memory:
req <- curl_fetch_memory("https://hb.cran.dev/post", handle = h)
jsonlite::prettify(rawToChar(req$content)){
"args": {
},
"data": "moo=moomooo",
"files": {
},
"form": {
},
"headers": {
"Accept": "*/*",
"Accept-Encoding": "gzip, br",
"Cache-Control": "no-cache",
"Cdn-Loop": "cloudflare; loops=1",
"Cf-Connecting-Ip": "4.227.175.100",
"Cf-Ipcountry": "US",
"Cf-Ray": "8e81ffcceef7387c-EWR",
"Cf-Visitor": "{\"scheme\":\"https\"}",
"Connection": "close",
"Content-Length": "11",
"Content-Type": "text/moo",
"Host": "httpbin:8080",
"User-Agent": "A cow"
},
"json": null,
"origin": "4.227.175.100",
"url": "https://httpbin:8080/post"
}
Alternatively we can use curl() to read the data of via
a connection interface:
{
"args": {
},
"data": "moo=moomooo",
"files": {
},
"form": {
},
"headers": {
"Accept": "*/*",
"Accept-Encoding": "gzip, br",
"Cache-Control": "no-cache",
"Cdn-Loop": "cloudflare; loops=1",
"Cf-Connecting-Ip": "4.227.175.100",
"Cf-Ipcountry": "US",
"Cf-Ray": "8e81ffcdbc0e5a03-EWR",
"Cf-Visitor": "{\"scheme\":\"https\"}",
"Connection": "close",
"Content-Length": "11",
"Content-Type": "text/moo",
"Host": "httpbin:8080",
"User-Agent": "A cow"
},
"json": null,
"origin": "4.227.175.100",
"url": "https://httpbin:8080/post"
}
Or we can use curl_download to write the response to
disk:
tmp <- tempfile()
curl_download("https://hb.cran.dev/post", destfile = tmp, handle = h)
jsonlite::prettify(readLines(tmp)){
"args": {
},
"data": "moo=moomooo",
"files": {
},
"form": {
},
"headers": {
"Accept": "*/*",
"Accept-Encoding": "gzip, br",
"Cache-Control": "no-cache",
"Cdn-Loop": "cloudflare; loops=1",
"Cf-Connecting-Ip": "4.227.175.100",
"Cf-Ipcountry": "US",
"Cf-Ray": "8e81ffce38a4387c-EWR",
"Cf-Visitor": "{\"scheme\":\"https\"}",
"Connection": "close",
"Content-Length": "11",
"Content-Type": "text/moo",
"Host": "httpbin:8080",
"User-Agent": "A cow"
},
"json": null,
"origin": "4.227.175.100",
"url": "https://httpbin:8080/post"
}
Or perform the same request with a multi pool:
curl_fetch_multi("https://hb.cran.dev/post", handle = h, done = function(res){
cat("Request complete! Response content:\n")
cat(rawToChar(res$content))
})
# Perform the request
out <- multi_run()Request complete! Response content:
{
"args": {},
"data": "moo=moomooo",
"files": {},
"form": {},
"headers": {
"Accept": "*/*",
"Accept-Encoding": "gzip, br",
"Cache-Control": "no-cache",
"Cdn-Loop": "cloudflare; loops=1",
"Cf-Connecting-Ip": "4.227.175.100",
"Cf-Ipcountry": "US",
"Cf-Ray": "8e81ffd04dc15a0a-EWR",
"Cf-Visitor": "{\"scheme\":\"https\"}",
"Connection": "close",
"Content-Length": "11",
"Content-Type": "text/moo",
"Host": "httpbin:8080",
"User-Agent": "A cow"
},
"json": null,
"origin": "4.227.175.100",
"url": "https://httpbin:8080/post"
}In most cases you should not re-use a single handle object for more than one request. The only benefit of reusing a handle for multiple requests is to keep track of cookies set by the server (seen above). This could be needed if your server uses session cookies, but this is rare these days. Most APIs set state explicitly via http headers or parameters, rather than implicitly via cookies.
In recent versions of the curl package there are no performance benefits of reusing handles. The overhead of creating and configuring a new handle object is negligible. The safest way to issue multiple requests, either to a single server or multiple servers is by using a separate handle for each request (which is the default)
req1 <- curl_fetch_memory("https://hb.cran.dev/get")
req2 <- curl_fetch_memory("https://www.r-project.org")In past versions of this package you needed to manually use a handle to take advantage of http Keep-Alive. However as of version 2.3 this is no longer the case: curl automatically maintains global a pool of open http connections shared by all handles. When performing many requests to the same server, curl automatically uses existing connections when possible, eliminating TCP/SSL handshaking overhead:
redirect namelookup connect pretransfer starttransfer total
0.000000 0.002136 0.003316 0.025358 0.068720 0.068780 redirect namelookup connect pretransfer starttransfer total
0.000000 0.000010 0.000000 0.000052 0.053131 0.053182 If you really need to re-use a handle, do note that that curl does not cleanup the handle after each request. All of the options and internal fields will linger around for all future request until explicitly reset or overwritten. This can sometimes leads to unexpected behavior.
The handle_reset function will reset all curl options
and request headers to the default values. It will not
erase cookies and it will still keep alive the connections. Therefore it
is good practice to call handle_reset after performing a
request if you want to reuse the handle for a subsequent request. Still
it is always safer to create a new fresh handle when possible, rather
than recycling old ones.
The handle_setform function is used to perform a
multipart/form-data HTTP POST request (a.k.a. posting a
form). Values can be either strings, raw vectors (for binary data) or
files.
# Posting multipart
h <- new_handle()
handle_setform(h,
foo = "blabla",
bar = charToRaw("boeboe"),
iris = form_data(serialize(iris, NULL), "application/rda"),
description = form_file(system.file("DESCRIPTION")),
logo = form_file(file.path(R.home('doc'), "html/logo.jpg"), "image/jpeg")
)
req <- curl_fetch_memory("https://hb.cran.dev/post", handle = h)The form_file function is used to upload files with the
form post. It has two arguments: a file path, and optionally a
content-type value. If no content-type is set, curl will guess the
content type of the file based on the file extension.
The form_data function is similar but simply posts a
string or raw value with a custom content-type.
All of the handle_xxx functions return the handle object
so that function calls can be chained using the popular pipe
operators:
# Perform request
res <- new_handle() |>
handle_setopt(copypostfields = "moo=moomooo") |>
handle_setheaders("Content-Type"="text/moo", "Cache-Control"="no-cache", "User-Agent"="A cow") |>
curl_fetch_memory(url = "https://hb.cran.dev/post")
# Parse response
res$content |> rawToChar() |> jsonlite::prettify(){
"args": {
},
"data": "moo=moomooo",
"files": {
},
"form": {
},
"headers": {
"Accept": "*/*",
"Accept-Encoding": "gzip, br",
"Cache-Control": "no-cache",
"Cdn-Loop": "cloudflare; loops=1",
"Cf-Connecting-Ip": "4.227.175.100",
"Cf-Ipcountry": "US",
"Cf-Ray": "8e81ffd8fef1387c-EWR",
"Cf-Visitor": "{\"scheme\":\"https\"}",
"Connection": "close",
"Content-Length": "11",
"Content-Type": "text/moo",
"Host": "httpbin:8080",
"User-Agent": "A cow"
},
"json": null,
"origin": "4.227.175.100",
"url": "https://httpbin:8080/post"
}