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<body>
<p>So far, all of our examples have involved chaining operations onto a single promise. In practice, you&#39;ll often find yourself needing to perform tasks that require the results of more than one promise. These are some patterns you may find useful:</p>

<ul>
<li><a href="#gathering"><strong>Gathering:</strong></a> Combining multiple independent promises into a single computation</li>
<li><a href="#nesting"><strong>Nesting:</strong></a> Using the result of one promise to affect the input or execution of another async operation</li>
<li><a href="#racing"><strong>Racing:</strong></a> Using the fastest of multiple promises</li>
<li><a href="#mapping"><strong>Mapping:</strong></a> Applying an async function to each of a list&#39;s elements and collecting the results</li>
<li><a href="#reducing"><strong>Reducing:</strong></a> Applying an async function to each of a list&#39;s elements and reducing</li>
</ul>

<h2>Gathering</h2>

<p>The most common pattern for combining promises is gathering, where you have two or more promises in hand and you want to use all of their results in a computation. The <code>promise_all</code> function is designed for this. Its signature looks like this:</p>

<pre><code class="r">promise_all(..., .list = NULL)
</code></pre>

<p><code>promise_all</code> takes any number of promises as named arguments, and returns a promise of a list containing named elements with the results of those promises.</p>

<p>Here&#39;s an example using <code>promise_all</code> to combine the results of two async <code>read.csv</code> operations:</p>

<pre><code class="r">library(promises)
library(future)
plan(multisession)

a &lt;- future(read.csv(&quot;a.csv&quot;))
b &lt;- future(read.csv(&quot;b.csv&quot;))

result &lt;- promise_all(a = a, b = b) %...&gt;% {
  rbind(.$a, .$b)
}
</code></pre>

<p>In this example, the value of <code>.</code> within the curly braces is a list whose elements <code>a</code> and <code>b</code> are both data frames. We use <code>rbind</code> to combine them.</p>

<p>The <code>.$</code> prefix is a bit inelegant, so we recommend the use of the base R function <code>with</code>, which lets you skip the prefix. Here&#39;s the same example, with <code>with</code>:</p>

<pre><code class="r">library(promises)
library(future)
plan(multisession)

a &lt;- future(read.csv(&quot;a.csv&quot;))
b &lt;- future(read.csv(&quot;b.csv&quot;))

promise_all(a = a, b = b) %...&gt;%
  with({
    rbind(a, b)
  })
</code></pre>

<p>(Note that since the <code>promise_all</code> argument names are the same as the variable names (<code>a = a</code>, <code>b = b</code>), the original variables are masked: inside the <code>with</code> block, <code>a</code> now refers to the <em>result</em> of the promise <code>a</code>, not the promise object itself. If you find this confusing, you can just choose a different argument name, like <code>promise_all(a_result = a, …)</code>.)</p>

<p>The combination of <code>promise_all</code> and <code>with</code> is a concise and powerful way to gather the results of multiple promises.</p>

<p><code>promise_all</code> also gives you two other options for passing input promises. First, if you would rather your result list be unnamed, you can pass in promises as unnamed arguments: <code>promise_all(a, b)</code> would yield <code>list(1, 2)</code>. Second, if you have a list of promises already in hand, you can pass the list as a single argument using <code>promise_all(.list = x)</code> (instead of, say, using <code>do.call(promise_all, x)</code>).</p>

<h2>Nesting</h2>

<p>Gathering is easy and convenient, but sometimes not flexible enough. For example, if you use the result of promise <code>a</code> to decide whether to launch a second async task, whose result you then use in combination with the result of <code>a</code>.</p>

<pre><code class="r">library(promises)
library(future)
plan(multisession)

a &lt;- future(1)

a %...&gt;% (function(a) {
  b &lt;- future(2)
  b %...&gt;% (function(b) {
    a + b
  })
})
</code></pre>

<p>(We use anonymous functions here to mask the names of the original promises&ndash;i.e. once inside the first anonymous function, the symbol <code>a</code> now refers to the result of the promise <code>a</code>.)</p>

<p>The nesting pattern is effective and flexible. The main downside is the physical nesting of the source code; if you use this pattern to a depth of more than a couple of promises, your code will be quite indented (in programming jargon this is referred to as the &ldquo;pyramid of doom&rdquo;).</p>

<h2>Racing</h2>

<pre><code class="r">library(promises)
library(future)
plan(multisession)

a &lt;- future({ Sys.sleep(1); 1 })
b &lt;- future({ Sys.sleep(0.5); 2 })

first &lt;- promise_race(a, b)
</code></pre>

<p><code>promise_race</code> takes multiple promises and returns a new promise that will be fulfilled with the first promise that succeeds. In the example above, <code>first</code> is a promise that will be fulfilled with <code>2</code> after 0.5 seconds.</p>

<p>If one of the input promises rejects before any succeed, then the returned promise will be rejected.</p>

<p>Note that promises does not currently support cancellation. So losing promises will attempt to run to completion even after the race ends.</p>

<h2>Mapping</h2>

<p>Use <code>promise_map</code> to run an async operation on each element of a list or vector, and collect the results in a list. It&#39;s very similar to <code>lapply</code> or <code>purrr::map</code>, except that the function to apply can return a promise, and the return value is also a promise.</p>

<p>In the example below, we iterate over a named vector of package names. For each package name, we launch an async task to download the package&#39;s description file from CRAN pick out the last published date.</p>

<pre><code class="r">library(promises)
library(future)
plan(multisession)

get_pub_date &lt;- function(pkg) {
  desc_url &lt;- paste0(&quot;https://cran.r-project.org/web/packages/&quot;, pkg, &quot;/DESCRIPTION&quot;)
  future({
    read.dcf(url(desc_url))[, &quot;Date/Publication&quot;] %&gt;% unname()
  })
}

packages &lt;- setNames(, c(&quot;ggplot2&quot;, &quot;dplyr&quot;, &quot;knitr&quot;))

pkg_dates &lt;- promise_map(packages, get_pub_date)

pkg_dates %...&gt;% print()
</code></pre>

<p>The resulting output looks like this:</p>

<pre><code>$ggplot2
[1] &quot;2016-12-30 22:45:17&quot;

$dplyr
[1] &quot;2017-09-28 20:43:29 UTC&quot;

$knitr
[1] &quot;2018-01-29 11:01:22 UTC&quot;
</code></pre>

<p><code>promise_map</code> works serially; each time it calls the given function on an element of the list/vector, it will wait for the returned promise to resolve before proceeding to the next element. Furthermore, any error or rejected promise will cause the entire <code>promise_map</code> operation to reject.</p>

<p>If you want behavior that&#39;s similar to <code>promise_map</code> but for all the async operations to occur in parallel, you can achieve that with a combination of a regular <code>purrr::map</code> and <code>promise_all</code>:</p>

<pre><code class="r">pkg_dates &lt;- purrr::map(packages, get_pub_date) %&gt;%
  promise_all(.list = .)

pkg_dates %...&gt;% print()
</code></pre>

<h2>Reducing</h2>

<p>Use <code>promise_reduce</code> when you have a list where you want to run an async operation on each of the elements, and to do so serially (i.e. only one async operation runs at a time). This can be helpful when you&#39;re searching through some elements using an async operation and want to terminate early when your search succeeds.</p>

<p>The signature of <code>promise_reduce</code> is as follows:</p>

<pre><code class="r">promise_reduce(x, func, init = NULL)
</code></pre>

<p>If you&#39;ve worked with <code>base::Reduce()</code> or <code>purr:::reduce()</code>, this should seem reasonably familiar: <code>x</code> is a vector or list; <code>func</code> is a function that takes two arguments, the accumulated value and the &ldquo;next&rdquo; value; and <code>init</code> is the default accumulated value.</p>

<p>The main difference between <code>promise_reduce</code> and <code>purrr:::reduce</code> is that with <code>promise_reduce</code>, your <code>func</code> can return a promise. If it does, <code>promise_reduce</code> will wait for it to resolve before updating the accumulated value and invoking <code>func</code> on the next element. The result returned from <code>promise_reduce</code> is a promise that resolves to the ultimate accumulated value.</p>

<p>The following example loops through a partial list of CRAN mirrors, returning the first one that passes whatever check <code>http::http_error</code> performs.</p>

<pre><code class="r">library(promises)
library(future)
plan(multisession)

cran_mirrors &lt;- c(
  &quot;https://cloud.r-project.org&quot;,
  &quot;https://cran.usthb.dz&quot;,
  &quot;https://cran.csiro.au&quot;,
  &quot;https://cran.wu.ac.at&quot;
)

promise_reduce(cran_mirrors, function(result, mirror) {
  if (!is.null(result)) {
    result
  } else {
    future({
      # Test the URL; return the URL on success, or NULL on failure
      if (!httr::http_error(mirror)) mirror
    })
  }
}, .init = NULL) %...&gt;% print()
</code></pre>

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