[DOC] Added R to the list of languages with "high-level API" support in the…

README.md

@@ -1,7 +1,7 @@
 # Apache Spark
 
 Spark is a fast and general cluster computing system for Big Data. It provides
-high-level APIs in Scala, Java, and Python, and an optimized engine that
+high-level APIs in Scala, Java, Python, and R, and an optimized engine that
 supports general computation graphs for data analysis. It also supports a
 rich set of higher-level tools including Spark SQL for SQL and DataFrames,
 MLlib for machine learning, GraphX for graph processing,
@@ -94,5 +94,5 @@ distribution.
 
 ## Configuration
 
-Please refer to the [Configuration guide](http://spark.apache.org/docs/latest/configuration.html)
+Please refer to the [Configuration Guide](http://spark.apache.org/docs/latest/configuration.html)
 in the online documentation for an overview on how to configure Spark.

docs/quick-start.md

@@ -126,7 +126,7 @@ scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (w
 wordCounts: spark.RDD[(String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
 {% endhighlight %}
 
-Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
+Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations), and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
 
 {% highlight scala %}
 scala> wordCounts.collect()
@@ -163,7 +163,7 @@ One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can i
 >>> wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)
 {% endhighlight %}
 
-Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
+Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations), and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
 
 {% highlight python %}
 >>> wordCounts.collect()
@@ -217,13 +217,13 @@ a cluster, as described in the [programming guide](programming-guide.html#initia
 </div>
 
 # Self-Contained Applications
-Now say we wanted to write a self-contained application using the Spark API. We will walk through a
-simple application in both Scala (with SBT), Java (with Maven), and Python.
+Suppose we wish to write a self-contained application using the Spark API. We will walk through a
+simple application in Scala (with SBT), Java (with Maven), and Python.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-We'll create a very simple Spark application in Scala. So simple, in fact, that it's
+We'll create a very simple Spark application in Scala--so simple, in fact, that it's
 named `SimpleApp.scala`:
 
 {% highlight scala %}
@@ -258,8 +258,8 @@ We pass the SparkContext constructor a
 object which contains information about our
 application. 
 
-Our application depends on the Spark API, so we'll also include an sbt configuration file, 
-`simple.sbt` which explains that Spark is a dependency. This file also adds a repository that 
+Our application depends on the Spark API, so we'll also include an SBT configuration file, 
+`simple.sbt`, which explains that Spark is a dependency. This file also adds a repository that 
 Spark depends on:
 
 {% highlight scala %}
@@ -272,7 +272,7 @@ scalaVersion := "{{site.SCALA_VERSION}}"
 libraryDependencies += "org.apache.spark" %% "spark-core" % "{{site.SPARK_VERSION}}"
 {% endhighlight %}
 
-For sbt to work correctly, we'll need to layout `SimpleApp.scala` and `simple.sbt`
+For SBT to work correctly, we'll need to layout `SimpleApp.scala` and `simple.sbt`
 according to the typical directory structure. Once that is in place, we can create a JAR package
 containing the application's code, then use the `spark-submit` script to run our program.
 
@@ -302,7 +302,7 @@ Lines with a: 46, Lines with b: 23
 
 </div>
 <div data-lang="java" markdown="1">
-This example will use Maven to compile an application jar, but any similar build system will work.
+This example will use Maven to compile an application JAR, but any similar build system will work.
 
 We'll create a very simple Spark application, `SimpleApp.java`:
 
@@ -374,7 +374,7 @@ $ find .
 Now, we can package the application using Maven and execute it with `./bin/spark-submit`.
 
 {% highlight bash %}
-# Package a jar containing your application
+# Package a JAR containing your application
 $ mvn package
 ...
 [INFO] Building jar: {..}/{..}/target/simple-project-1.0.jar