Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/5442#discussion_r28188837
--- Diff: docs/quick-start.md ---
@@ -171,6 +205,44 @@ Here, we combined the
[`flatMap`](programming-guide.html#transformations), [`map
{% endhighlight %}
</div>
+<div data-lang="r" markdown="1">
+
+{% highlight r %}
+> reduce(map(textFile, function(line) { length(strsplit(line, " ")[[1]])
}), function(a, b) {max(a, b)})
+[1] 14
+{% endhighlight %}
+
+This first maps a line to an integer value, creating a new RDD. `reduce`
is called on that RDD to find the largest line count. The arguments to `map`
and `reduce` are R [anonymous
functions](http://adv-r.had.co.nz/Functional-programming.html#anonymous-functions),
+but we can also pass any top-level R function we want.
+For example, we'll define a `mymax` function to make this code easier to
understand:
+
+{% highlight r %}
+> mymax <- function(a, b) { max(a, b) }
+> reduce(map(textFile, function(line) { length(strsplit(line, " ")[[1]])
}), mymax)
+[1] 14
+{% endhighlight %}
+
+One common data flow pattern is MapReduce, as popularized by Hadoop. Spark
can implement MapReduce flows easily:
+
+{% highlight r %}
+> wordCounts <- reduceByKey(map(flatMap(textFile, function(line)
strsplit(line, " ")[[1]]), function(word) list(word, 1)), "+", 2)
--- End diff --
Can you split this into multiple lines, so it becomes more readable ? i.e.
something like
```
wordCounts <- reduceByKey(
map(
flatMap(textFile, function(line) strsplit(line, " ")[[1]]),
function(word) list(word, 1)),
"+", 2)
```
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