diff --git a/.travis.yml b/.travis.yml
index 72b748e1342..c458ea45398 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -20,19 +20,26 @@ cache:
   directories:
     - .spark-dist
 
+addons:
+  apt:
+    sources:
+      r-packages-precise
+    packages:
+      r-base
+
 matrix:
   include:
     # Test all modules
     - jdk: "oraclejdk7"
-      env: SPARK_VER="1.6.1" HADOOP_VER="2.3" PROFILE="-Pspark-1.6 -Phadoop-2.3 -Ppyspark -Pscalding" BUILD_FLAG="package -Pbuild-distr" TEST_FLAG="verify -Pusing-packaged-distr" TEST_PROJECTS=""
+      env: SPARK_VER="1.6.1" HADOOP_VER="2.3" PROFILE="-Pspark-1.6 -Phadoop-2.3 -Ppyspark -Psparkr -Pscalding" BUILD_FLAG="package -Pbuild-distr" TEST_FLAG="verify -Pusing-packaged-distr" TEST_PROJECTS=""
 
     # Test spark module for 1.5.2
     - jdk: "oraclejdk7"
-      env: SPARK_VER="1.5.2" HADOOP_VER="2.3" PROFILE="-Pspark-1.5 -Phadoop-2.3 -Ppyspark" BUILD_FLAG="package -DskipTests" TEST_FLAG="verify" TEST_PROJECTS="-pl zeppelin-interpreter,zeppelin-zengine,zeppelin-server,zeppelin-display,spark-dependencies,spark -Dtest=org.apache.zeppelin.rest.*Test,org.apache.zeppelin.spark* -DfailIfNoTests=false"
+      env: SPARK_VER="1.5.2" HADOOP_VER="2.3" PROFILE="-Pspark-1.5 -Phadoop-2.3 -Ppyspark -Psparkr" BUILD_FLAG="package -DskipTests" TEST_FLAG="verify" TEST_PROJECTS="-pl zeppelin-interpreter,zeppelin-zengine,zeppelin-server,zeppelin-display,spark-dependencies,spark -Dtest=org.apache.zeppelin.rest.*Test,org.apache.zeppelin.spark* -DfailIfNoTests=false"
 
     # Test spark module for 1.4.1
     - jdk: "oraclejdk7"
-      env: SPARK_VER="1.4.1" HADOOP_VER="2.3" PROFILE="-Pspark-1.4 -Phadoop-2.3 -Ppyspark" BUILD_FLAG="package -DskipTests" TEST_FLAG="verify" TEST_PROJECTS="-pl zeppelin-interpreter,zeppelin-zengine,zeppelin-server,zeppelin-display,spark-dependencies,spark -Dtest=org.apache.zeppelin.rest.*Test,org.apache.zeppelin.spark* -DfailIfNoTests=false"
+      env: SPARK_VER="1.4.1" HADOOP_VER="2.3" PROFILE="-Pspark-1.4 -Phadoop-2.3 -Ppyspark -Psparkr" BUILD_FLAG="package -DskipTests" TEST_FLAG="verify" TEST_PROJECTS="-pl zeppelin-interpreter,zeppelin-zengine,zeppelin-server,zeppelin-display,spark-dependencies,spark -Dtest=org.apache.zeppelin.rest.*Test,org.apache.zeppelin.spark* -DfailIfNoTests=false"
 
     # Test spark module for 1.3.1
     - jdk: "oraclejdk7"
@@ -55,6 +62,11 @@ before_install:
   - "export DISPLAY=:99.0"
   - "sh -e /etc/init.d/xvfb start"
 
+  # install R packages
+  - mkdir -p ~/Rlib
+  - echo 'R_LIBS=~/Rlib' > ~/.Renviron
+  - Rscript -e "install.packages('knitr', repos = 'http://cran.us.r-project.org')"
+
 install:
   - mvn $BUILD_FLAG $PROFILE -B
 
diff --git a/conf/zeppelin-site.xml.template b/conf/zeppelin-site.xml.template
index 93d0495ee10..383e751368e 100755
--- a/conf/zeppelin-site.xml.template
+++ b/conf/zeppelin-site.xml.template
@@ -144,7 +144,7 @@
 
 <property>
   <name>zeppelin.interpreters</name>
-  <value>org.apache.zeppelin.spark.SparkInterpreter,org.apache.zeppelin.spark.PySparkInterpreter,org.apache.zeppelin.spark.SparkSqlInterpreter,org.apache.zeppelin.spark.DepInterpreter,org.apache.zeppelin.markdown.Markdown,org.apache.zeppelin.angular.AngularInterpreter,org.apache.zeppelin.shell.ShellInterpreter,org.apache.zeppelin.hive.HiveInterpreter,org.apache.zeppelin.tajo.TajoInterpreter,org.apache.zeppelin.file.HDFSFileInterpreter,org.apache.zeppelin.flink.FlinkInterpreter,org.apache.zeppelin.lens.LensInterpreter,org.apache.zeppelin.ignite.IgniteInterpreter,org.apache.zeppelin.ignite.IgniteSqlInterpreter,org.apache.zeppelin.cassandra.CassandraInterpreter,org.apache.zeppelin.geode.GeodeOqlInterpreter,org.apache.zeppelin.postgresql.PostgreSqlInterpreter,org.apache.zeppelin.jdbc.JDBCInterpreter,org.apache.zeppelin.phoenix.PhoenixInterpreter,org.apache.zeppelin.kylin.KylinInterpreter,org.apache.zeppelin.elasticsearch.ElasticsearchInterpreter,org.apache.zeppelin.scalding.ScaldingInterpreter,org.apache.zeppelin.alluxio.AlluxioInterpreter,org.apache.zeppelin.hbase.HbaseInterpreter</value>
+<value>org.apache.zeppelin.spark.SparkInterpreter,org.apache.zeppelin.spark.PySparkInterpreter,org.apache.zeppelin.spark.SparkRInterpreter,org.apache.zeppelin.spark.SparkSqlInterpreter,org.apache.zeppelin.spark.DepInterpreter,org.apache.zeppelin.markdown.Markdown,org.apache.zeppelin.angular.AngularInterpreter,org.apache.zeppelin.shell.ShellInterpreter,org.apache.zeppelin.hive.HiveInterpreter,org.apache.zeppelin.tajo.TajoInterpreter,org.apache.zeppelin.flink.FlinkInterpreter,org.apache.zeppelin.lens.LensInterpreter,org.apache.zeppelin.ignite.IgniteInterpreter,org.apache.zeppelin.ignite.IgniteSqlInterpreter,org.apache.zeppelin.cassandra.CassandraInterpreter,org.apache.zeppelin.geode.GeodeOqlInterpreter,org.apache.zeppelin.postgresql.PostgreSqlInterpreter,org.apache.zeppelin.jdbc.JDBCInterpreter,org.apache.zeppelin.phoenix.PhoenixInterpreter,org.apache.zeppelin.kylin.KylinInterpreter,org.apache.zeppelin.elasticsearch.ElasticsearchInterpreter,org.apache.zeppelin.scalding.ScaldingInterpreter,org.apache.zeppelin.alluxio.AlluxioInterpreter,org.apache.zeppelin.hbase.HbaseInterpreter</value>
   <description>Comma separated interpreter configurations. First interpreter become a default</description>
 </property>
 
diff --git a/docs/_includes/themes/zeppelin/_navigation.html b/docs/_includes/themes/zeppelin/_navigation.html
index c60ba747162..c616107bf4c 100644
--- a/docs/_includes/themes/zeppelin/_navigation.html
+++ b/docs/_includes/themes/zeppelin/_navigation.html
@@ -54,6 +54,7 @@
                 <li><a href="{{BASE_PATH}}/interpreter/lens.html">Lens</a></li>
                 <li><a href="{{BASE_PATH}}/interpreter/markdown.html">Markdown</a></li>
                 <li><a href="{{BASE_PATH}}/interpreter/postgresql.html">Postgresql, hawq</a></li>
+                <li><a href="{{BASE_PATH}}/interpreter/R.html">R</a></li>
                 <li><a href="{{BASE_PATH}}/interpreter/scalding.html">Scalding</a></li>
                 <li><a href="{{BASE_PATH}}/pleasecontribute.html">Shell</a></li>
                 <li><a href="{{BASE_PATH}}/interpreter/spark.html">Spark</a></li>
diff --git a/docs/interpreter/R.md b/docs/interpreter/R.md
new file mode 100644
index 00000000000..fde99833e5b
--- /dev/null
+++ b/docs/interpreter/R.md
@@ -0,0 +1,41 @@
+---
+layout: page
+title: "R Interpreter"
+description: ""
+group: manual
+---
+{% include JB/setup %}
+
+## R Interpreter for Apache Zeppelin
+
+[R](https://www.r-project.org) is a free software environment for statistical computing and graphics.
+
+To run R code and visualize plots in Apache Zeppelin, you will need R on your master node (or your dev laptop).
+
++ For Centos: `yum install R R-devel libcurl-devel openssl-devel`
++ For Ubuntu: `apt-get install r-base`
+    
+Validate your installation with a simple R command:
+
+```
+R -e "print(1+1)"
+```
+
+To enjoy plots, install additional libraries with:
+
+```
++ devtools with `R -e "install.packages('devtools', repos = 'http://cran.us.r-project.org')"`
++ knitr with `R -e "install.packages('knitr', repos = 'http://cran.us.r-project.org')"`
++ ggplot2 with `R -e "install.packages('ggplot2', repos = 'http://cran.us.r-project.org')"`
++ Other vizualisation librairies: `R -e "install.packages(c('devtools','mplot', 'googleVis'), repos = 'http://cran.us.r-project.org'); require(devtools); install_github('ramnathv/rCharts')"`
+```
+
+We recommend you to also install the following optional R libraries for happy data analytics:
+
++ glmnet
++ pROC
++ data.table
++ caret
++ sqldf
++ wordcloud
+
diff --git a/docs/rest-api/rest-configuration.md b/docs/rest-api/rest-configuration.md
index 587b065971d..b7d2c145673 100644
--- a/docs/rest-api/rest-configuration.md
+++ b/docs/rest-api/rest-configuration.md
@@ -79,7 +79,7 @@ limitations under the License.
       "zeppelin.server.context.path":"/",
       "zeppelin.ssl.keystore.type":"JKS",
       "zeppelin.ssl.truststore.path":"truststore",
-      "zeppelin.interpreters":"org.apache.zeppelin.spark.SparkInterpreter,org.apache.zeppelin.spark.PySparkInterpreter,org.apache.zeppelin.spark.SparkSqlInterpreter,org.apache.zeppelin.spark.DepInterpreter,org.apache.zeppelin.markdown.Markdown,org.apache.zeppelin.angular.AngularInterpreter,org.apache.zeppelin.shell.ShellInterpreter,org.apache.zeppelin.hive.HiveInterpreter,org.apache.zeppelin.tajo.TajoInterpreter,org.apache.zeppelin.flink.FlinkInterpreter,org.apache.zeppelin.lens.LensInterpreter,org.apache.zeppelin.ignite.IgniteInterpreter,org.apache.zeppelin.ignite.IgniteSqlInterpreter,org.apache.zeppelin.cassandra.CassandraInterpreter,org.apache.zeppelin.geode.GeodeOqlInterpreter,org.apache.zeppelin.postgresql.PostgreSqlInterpreter,org.apache.zeppelin.phoenix.PhoenixInterpreter,org.apache.zeppelin.kylin.KylinInterpreter,org.apache.zeppelin.elasticsearch.ElasticsearchInterpreter,org.apache.zeppelin.scalding.ScaldingInterpreter",
+      "zeppelin.interpreters":"org.apache.zeppelin.spark.SparkInterpreter,org.apache.zeppelin.spark.PySparkInterpreter,org.apache.zeppelin.spark.SparkRInterpreter,org.apache.zeppelin.spark.SparkSqlInterpreter,org.apache.zeppelin.spark.DepInterpreter,org.apache.zeppelin.markdown.Markdown,org.apache.zeppelin.angular.AngularInterpreter,org.apache.zeppelin.shell.ShellInterpreter,org.apache.zeppelin.hive.HiveInterpreter,org.apache.zeppelin.tajo.TajoInterpreter,org.apache.zeppelin.flink.FlinkInterpreter,org.apache.zeppelin.lens.LensInterpreter,org.apache.zeppelin.ignite.IgniteInterpreter,org.apache.zeppelin.ignite.IgniteSqlInterpreter,org.apache.zeppelin.cassandra.CassandraInterpreter,org.apache.zeppelin.geode.GeodeOqlInterpreter,org.apache.zeppelin.postgresql.PostgreSqlInterpreter,org.apache.zeppelin.phoenix.PhoenixInterpreter,org.apache.zeppelin.kylin.KylinInterpreter,org.apache.zeppelin.elasticsearch.ElasticsearchInterpreter,org.apache.zeppelin.scalding.ScaldingInterpreter",
       "zeppelin.ssl":"false",
       "zeppelin.notebook.autoInterpreterBinding":"true",
       "zeppelin.notebook.homescreen":"",
diff --git a/notebook/r/note.json b/notebook/r/note.json
new file mode 100644
index 00000000000..35c2bbbbee9
--- /dev/null
+++ b/notebook/r/note.json
@@ -0,0 +1,1036 @@
+{
+  "paragraphs": [
+    {
+      "title": "Hello R",
+      "text": "%r\nfoo \u003c- TRUE\nprint(foo)\nbare \u003c- c(1, 2.5, 4)\nprint(bare)\ndouble \u003c- 15.0\nprint(double)",
+      "dateUpdated": "Feb 23, 2016 2:44:13 PM",
+      "config": {
+        "colWidth": 4.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "editorMode": "ace/mode/scala",
+        "enabled": true,
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1429882946244_-381648689",
+      "id": "20150424-154226_261270952",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "[1] TRUE\n[1] 1.0 2.5 4.0\n[1] 15"
+      },
+      "dateCreated": "Apr 24, 2015 3:42:26 PM",
+      "dateStarted": "Feb 23, 2016 2:44:13 PM",
+      "dateFinished": "Feb 23, 2016 2:44:55 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Load R Librairies",
+      "text": "%r\nlibrary(data.table)\ndt \u003c- data.table(1:3)\nprint(dt)\nfor (i in 1:5) {\n  print(i*2)\n}\nprint(1:50)",
+      "dateUpdated": "Feb 23, 2016 2:45:12 PM",
+      "config": {
+        "colWidth": 4.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "editorMode": "ace/mode/scala",
+        "enabled": true,
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1429882976611_1352445253",
+      "id": "20150424-154256_645296307",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "V1\n1:  1\n2:  2\n3:  3\n[1] 2\n[1] 4\n[1] 6\n[1] 8\n[1] 10\n [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n[24] 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46\n[47] 47 48 49 50"
+      },
+      "dateCreated": "Apr 24, 2015 3:42:56 PM",
+      "dateStarted": "Feb 23, 2016 2:45:13 PM",
+      "dateFinished": "Feb 23, 2016 2:45:13 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Load Iris Dataset",
+      "text": "%r\ncolnames(iris)\niris$Petal.Length\niris$Sepal.Length",
+      "dateUpdated": "Feb 23, 2016 2:45:14 PM",
+      "config": {
+        "colWidth": 4.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala",
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455138077044_161383897",
+      "id": "20160210-220117_115873183",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "[1] “Sepal.Length” “Sepal.Width”  “Petal.Length” “Petal.Width” \n[5] “Species”\u003cbr /\u003e\n  [1] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 1.5 1.6 1.4 1.1 1.2 1.5 1.3\n [18] 1.4 1.7 1.5 1.7 1.5 1.0 1.7 1.9 1.6 1.6 1.5 1.4 1.6 1.6 1.5 1.5 1.4\n [35] 1.5 1.2 1.3 1.4 1.3 1.5 1.3 1.3 1.3 1.6 1.9 1.4 1.6 1.4 1.5 1.4 4.7\n [52] 4.5 4.9 4.0 4.6 4.5 4.7 3.3 4.6 3.9 3.5 4.2 4.0 4.7 3.6 4.4 4.5 4.1\n [69] 4.5 3.9 4.8 4.0 4.9 4.7 4.3 4.4 4.8 5.0 4.5 3.5 3.8 3.7 3.9 5.1 4.5\n [86] 4.5 4.7 4.4 4.1 4.0 4.4 4.6 4.0 3.3 4.2 4.2 4.2 4.3 3.0 4.1 6.0 5.1\n[103] 5.9 5.6 5.8 6.6 4.5 6.3 5.8 6.1 5.1 5.3 5.5 5.0 5.1 5.3 5.5 6.7 6.9\n[120] 5.0 5.7 4.9 6.7 4.9 5.7 6.0 4.8 4.9 5.6 5.8 6.1 6.4 5.6 5.1 5.6 6.1\n[137] 5.6 5.5 4.8 5.4 5.6 5.1 5.1 5.9 5.7 5.2 5.0 5.2 5.4 5.1\n  [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4\n [18] 5.1 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5\n [35] 4.9 5.0 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0\n [52] 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8\n [69] 6.2 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4\n [86] 6.0 6.7 6.3 5.6 5.5 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8\n[103] 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7\n[120] 6.0 6.9 5.6 7.7 6.3 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7\n[137] 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8 6.7 6.7 6.3 6.5 6.2 5.9"
+      },
+      "dateCreated": "Feb 10, 2016 10:01:17 PM",
+      "dateStarted": "Feb 23, 2016 2:45:14 PM",
+      "dateFinished": "Feb 23, 2016 2:45:14 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "HTML Display",
+      "text": "%r \n\nprint(\"%html \u003ch3\u003eHello HTML\u003c/h3\u003e\")\nprint(\"\u003cfont color\u003d\u0027blue\u0027\u003e\u003cspan class\u003d\u0027fa fa-bars\u0027\u003e Easy...\u003c/font\u003e\u003c/span\u003e\")\nfor (i in 1:10) {\n  print(paste0(\"\u003ch4\u003e\", i, \" * 2 \u003d \", i*2, \"\u003c/h4\u003e\"))\n}",
+      "dateUpdated": "Feb 23, 2016 2:45:15 PM",
+      "config": {
+        "colWidth": 3.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala",
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1456140102445_51059930",
+      "id": "20160222-122142_1323614681",
+      "result": {
+        "code": "SUCCESS",
+        "type": "HTML",
+        "msg": "\u003cp\u003e\u003c/p\u003e\u003ch3\u003eHello HTML\u003c/h3\u003e\u003cfont color\u003d\"blue\"\u003e\u003cspan class\u003d\"fa fa-bars\"\u003e Easy…\u003c/span\u003e\u003c/font\u003e\u003ch4\u003e1 * 2 \u003d 2\u003c/h4\u003e\u003ch4\u003e2 * 2 \u003d 4\u003c/h4\u003e\u003ch4\u003e3 * 2 \u003d 6\u003c/h4\u003e\u003ch4\u003e4 * 2 \u003d 8\u003c/h4\u003e\u003ch4\u003e5 * 2 \u003d 10\u003c/h4\u003e\u003ch4\u003e6 * 2 \u003d 12\u003c/h4\u003e\u003ch4\u003e7 * 2 \u003d 14\u003c/h4\u003e\u003ch4\u003e8 * 2 \u003d 16\u003c/h4\u003e\u003ch4\u003e9 * 2 \u003d 18\u003c/h4\u003e\u003ch4\u003e10 * 2 \u003d 20\u003c/h4\u003e\u003cp\u003e\u003c/p\u003e"
+      },
+      "dateCreated": "Feb 22, 2016 12:21:42 PM",
+      "dateStarted": "Feb 23, 2016 2:45:15 PM",
+      "dateFinished": "Feb 23, 2016 2:45:16 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "TABLE Display",
+      "text": "%r print(\"%table name\\tsize\\nsmall\\t100\\nlarge\\t1000\")",
+      "dateUpdated": "Feb 23, 2016 2:45:18 PM",
+      "config": {
+        "colWidth": 3.0,
+        "graph": {
+          "mode": "multiBarChart",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [
+            {
+              "name": "name",
+              "index": 0.0,
+              "aggr": "sum"
+            }
+          ],
+          "values": [
+            {
+              "name": "size",
+              "index": 1.0,
+              "aggr": "sum"
+            }
+          ],
+          "groups": [],
+          "scatter": {
+            "xAxis": {
+              "name": "name",
+              "index": 0.0,
+              "aggr": "sum"
+            },
+            "yAxis": {
+              "name": "size",
+              "index": 1.0,
+              "aggr": "sum"
+            }
+          }
+        },
+        "enabled": true,
+        "title": true,
+        "editorMode": "ace/mode/scala"
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1456216582752_6855525",
+      "id": "20160223-093622_330111284",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TABLE",
+        "msg": "name\tsize\nsmall\t100\nlarge\t1000"
+      },
+      "dateCreated": "Feb 23, 2016 9:36:22 AM",
+      "dateStarted": "Feb 23, 2016 2:45:18 PM",
+      "dateFinished": "Feb 23, 2016 2:45:18 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "IMG Display",
+      "text": "%r print(paste0(\"%img \", \"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\u003d\"))",
+      "dateUpdated": "Feb 23, 2016 2:47:07 PM",
+      "config": {
+        "colWidth": 3.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala",
+        "title": true,
+        "editorHide": false
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1456216588116_1304228816",
+      "id": "20160223-093628_594223067",
+      "result": {
+        "code": "SUCCESS",
+        "type": "IMG",
+        "msg": "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\u003d"
+      },
+      "dateCreated": "Feb 23, 2016 9:36:28 AM",
+      "dateStarted": "Feb 23, 2016 2:47:07 PM",
+      "dateFinished": "Feb 23, 2016 2:47:07 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "text": "z.input(\"name\", \"sun\")\n\n\n\n\n\n",
+      "dateUpdated": "Feb 23, 2016 3:06:16 PM",
+      "config": {
+        "colWidth": 3.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala"
+      },
+      "settings": {
+        "params": {
+          "name": "sun"
+        },
+        "forms": {
+          "name": {
+            "name": "name",
+            "displayName": "name",
+            "defaultValue": "sun",
+            "hidden": false
+          }
+        }
+      },
+      "jobName": "paragraph_1456235221022_-1625740722",
+      "id": "20160223-144701_1698149301",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "res13: Object \u003d sun\n"
+      },
+      "dateCreated": "Feb 23, 2016 2:47:01 PM",
+      "dateStarted": "Feb 23, 2016 3:06:16 PM",
+      "dateFinished": "Feb 23, 2016 3:06:16 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "[WIP] Dynamic Form",
+      "text": "%r \nprint(paste0(\"%html Hello \", z.input(\"name\", \"sun\")))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+      "dateUpdated": "Feb 23, 2016 3:06:30 PM",
+      "config": {
+        "colWidth": 3.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala",
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1456217039220_218125354",
+      "id": "20160223-094359_718035548",
+      "result": {
+        "code": "SUCCESS",
+        "type": "HTML",
+        "msg": "\u003cp\u003eHello sun\u003c/p\u003e"
+      },
+      "dateCreated": "Feb 23, 2016 9:43:59 AM",
+      "dateStarted": "Feb 23, 2016 3:01:38 PM",
+      "dateFinished": "Feb 23, 2016 3:01:38 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Write Scala To R",
+      "text": "val s \u003d \"Hello R from Scala\"\nz.put(\"s\", s)\nval b \u003d new Integer(42)\nz.put(\"b\", b)\nval a: Array[Double] \u003d Array[Double](30.1, 20.0)\nz.put(\"a\", a)\nval m \u003d Array(Array(1, 4), Array(8, 16))\nz.put(\"m\", m)\nval v \u003d Vector(1, 2, 3, 4)\nz.put(\"v\", v)",
+      "authenticationInfo": {},
+      "dateUpdated": "Mar 30, 2016 9:05:07 AM",
+      "config": {
+        "colWidth": 3.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "editorMode": "ace/mode/scala",
+        "enabled": true,
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1429862281402_-79250404",
+      "id": "20150424-095801_125725189",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "s: String \u003d Hello R from Scala\nb: Integer \u003d 42\na: Array[Double] \u003d Array(30.1, 20.0)\nm: Array[Array[Int]] \u003d Array(Array(1, 4), Array(8, 16))\nv: scala.collection.immutable.Vector[Int] \u003d Vector(1, 2, 3, 4)\n"
+      },
+      "dateCreated": "Apr 24, 2015 9:58:01 AM",
+      "dateStarted": "Mar 30, 2016 9:05:07 AM",
+      "dateFinished": "Mar 30, 2016 9:05:07 AM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Read from R the Scala Variables",
+      "text": "%r\nz.get(\"s\")\nz.get(\"b\")\nprint(unlist(z.get(\"a\")))\nprint(unlist(z.get(\"m\")))\nz.get(\"v\")",
+      "authenticationInfo": {},
+      "dateUpdated": "Mar 30, 2016 9:05:08 AM",
+      "config": {
+        "colWidth": 3.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "editorMode": "ace/mode/scala",
+        "enabled": true,
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1438930802740_-1781296534",
+      "id": "20150807-090002_1514685133",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "[1] “Hello R from Scala”\n[1] 42\n[1] 30.1 20.0\n[1]  1  4  8 16\nJava ref type scala.collection.immutable.Vector id 92"
+      },
+      "dateCreated": "Aug 7, 2015 9:00:02 AM",
+      "dateStarted": "Mar 30, 2016 9:05:08 AM",
+      "dateFinished": "Mar 30, 2016 9:05:08 AM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Write R to Scala",
+      "text": "%r\ns \u003c- \"Hello Scala from R\"\nprint(s)\nz.put(\"rs\", s)\nb \u003c- TRUE\nprint(b)\nz.put(\"rb\", b)\nd \u003c- 15.0\nprint(d)\nz.put(\"rd\", d)\nm \u003c- c(2.4, 2.5, 4)\nprint(m)\nz.put(\"rm\", m)",
+      "authenticationInfo": {},
+      "dateUpdated": "Mar 30, 2016 9:05:25 AM",
+      "config": {
+        "colWidth": 3.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala",
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455137934157_-1786381957",
+      "id": "20160210-215854_620520530",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "[1] “Hello Scala from R”\nNULL\n[1] TRUE\nNULL\n[1] 15\nNULL\n[1] 2.4 2.5 4.0\nNULL"
+      },
+      "dateCreated": "Feb 10, 2016 9:58:54 PM",
+      "dateStarted": "Mar 30, 2016 9:05:25 AM",
+      "dateFinished": "Mar 30, 2016 9:05:25 AM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Read from Scala the R Variables",
+      "text": "println(\"rs \u003d \"+ z.get(\"rs\"))\nprintln(\"rb \u003d \"+ z.get(\"rb\"))\nprintln(\"rd \u003d \"+ z.get(\"rd\"))\nprintln(\"rm \u003d \"+ z.get(\"rm\"))\n// println(z.get(\"rm\").getClass)\n// println(\"rm \u003d \"+ z.get(\"rm\").asInstanceOf[Array[Double]].toSeq)",
+      "authenticationInfo": {},
+      "dateUpdated": "Mar 30, 2016 9:05:27 AM",
+      "config": {
+        "colWidth": 3.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala",
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455138066039_1048230112",
+      "id": "20160210-220106_141884849",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "rs \u003d Hello Scala from R\nrb \u003d true\nrd \u003d 15.0\nrm \u003d 2.4\n"
+      },
+      "dateCreated": "Feb 10, 2016 10:01:06 PM",
+      "dateStarted": "Mar 30, 2016 9:05:27 AM",
+      "dateFinished": "Mar 30, 2016 9:05:27 AM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Create a Spark Dataframe",
+      "text": "import org.apache.commons.io.IOUtils\nimport java.net.URL\nimport java.nio.charset.Charset\n\nval bankText \u003d sc.parallelize(\n    IOUtils.toString(\n        new URL(\"https://s3.amazonaws.com/apache-zeppelin/tutorial/bank/bank.csv\"),\n        Charset.forName(\"utf8\")).split(\"\\n\"))\n\ncase class Bank(age: Integer, job: String, marital: String, education: String, balance: Integer)\n\nval bank \u003d bankText.map(s \u003d\u003e s.split(\";\")).filter(s \u003d\u003e s(0) !\u003d \"\\\"age\\\"\").map(\n    s \u003d\u003e Bank(s(0).toInt, \n            s(1).replaceAll(\"\\\"\", \"\"),\n            s(2).replaceAll(\"\\\"\", \"\"),\n            s(3).replaceAll(\"\\\"\", \"\"),\n            s(5).replaceAll(\"\\\"\", \"\").toInt\n        )\n).toDF()\nbank.registerTempTable(\"bank\")",
+      "dateUpdated": "Feb 23, 2016 2:49:36 PM",
+      "config": {
+        "colWidth": 6.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "lineNumbers": false,
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455142039343_-233762796",
+      "id": "20160210-230719_2111095838",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "import org.apache.commons.io.IOUtils\nimport java.net.URL\nimport java.nio.charset.Charset\nbankText: org.apache.spark.rdd.RDD[String] \u003d ParallelCollectionRDD[0] at parallelize at \u003cconsole\u003e:27\ndefined class Bank\nbank: org.apache.spark.sql.DataFrame \u003d [age: int, job: string, marital: string, education: string, balance: int]\n"
+      },
+      "dateCreated": "Feb 10, 2016 11:07:19 PM",
+      "dateStarted": "Feb 23, 2016 2:49:36 PM",
+      "dateFinished": "Feb 23, 2016 2:49:41 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Read the Spark Dataframe from R",
+      "text": "%r\n\ndf \u003c- sql(sqlContext, \"select count(*) from bank\")\nprintSchema(df)\nSparkR::head(df)",
+      "dateUpdated": "Feb 23, 2016 2:49:52 PM",
+      "config": {
+        "colWidth": 6.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala",
+        "tableHide": false,
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455142043062_1598026718",
+      "id": "20160210-230723_1811469598",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "root\n |– _c0: long (nullable \u003d false)\n   _c0\n1 4521"
+      },
+      "dateCreated": "Feb 10, 2016 11:07:23 PM",
+      "dateStarted": "Feb 23, 2016 2:49:52 PM",
+      "dateFinished": "Feb 23, 2016 2:49:54 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Query with Spark Dataframe with SQL",
+      "text": "%sql select count(*) from bank",
+      "dateUpdated": "Feb 23, 2016 2:49:56 PM",
+      "config": {
+        "colWidth": 6.0,
+        "graph": {
+          "mode": "table",
+          "height": 99.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala",
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455142050697_-1353382095",
+      "id": "20160210-230730_1259663883",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TABLE",
+        "msg": "_c0\n4521\n"
+      },
+      "dateCreated": "Feb 10, 2016 11:07:30 PM",
+      "dateStarted": "Feb 23, 2016 2:49:56 PM",
+      "dateFinished": "Feb 23, 2016 2:49:56 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Create a R Dataframe",
+      "text": "%r \n\nlocalNames \u003c- data.frame(name\u003dc(\"John\", \"Smith\", \"Sarah\"), budget\u003dc(19, 53, 18))\nnames \u003c- createDataFrame(sqlContext, localNames)\nprintSchema(names)\nregisterTempTable(names, \"names\")\n\n# SparkR::head(names)",
+      "dateUpdated": "Feb 23, 2016 2:50:02 PM",
+      "config": {
+        "colWidth": 6.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "title": true,
+        "editorMode": "ace/mode/scala"
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455142112413_519883679",
+      "id": "20160210-230832_1847721959",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "root\n |– name: string (nullable \u003d true)\n |– budget: double (nullable \u003d true)"
+      },
+      "dateCreated": "Feb 10, 2016 11:08:32 PM",
+      "dateStarted": "Feb 23, 2016 2:50:02 PM",
+      "dateFinished": "Feb 23, 2016 2:50:02 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Read the R Dataframe from Spark",
+      "text": "sqlc.sql(\"select * from names\").head",
+      "dateUpdated": "Feb 23, 2016 2:50:09 PM",
+      "config": {
+        "colWidth": 6.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala",
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455188357108_95477841",
+      "id": "20160211-115917_445850505",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": "res32: org.apache.spark.sql.Row \u003d [John,19.0]\n"
+      },
+      "dateCreated": "Feb 11, 2016 11:59:17 AM",
+      "dateStarted": "Feb 23, 2016 2:50:09 PM",
+      "dateFinished": "Feb 23, 2016 2:50:10 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "Query the R Datafame with SQL",
+      "text": "%sql select * from names\n",
+      "dateUpdated": "Feb 23, 2016 2:50:15 PM",
+      "config": {
+        "colWidth": 6.0,
+        "graph": {
+          "mode": "pieChart",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [
+            {
+              "name": "name",
+              "index": 0.0,
+              "aggr": "sum"
+            }
+          ],
+          "values": [],
+          "groups": [],
+          "scatter": {
+            "xAxis": {
+              "name": "name",
+              "index": 0.0,
+              "aggr": "sum"
+            }
+          }
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/sql",
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455142115582_-1840950897",
+      "id": "20160210-230835_19876971",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TABLE",
+        "msg": "name\tbudget\nJohn\t19.0\nSmith\t53.0\nSarah\t18.0\n"
+      },
+      "dateCreated": "Feb 10, 2016 11:08:35 PM",
+      "dateStarted": "Feb 23, 2016 2:50:15 PM",
+      "dateFinished": "Feb 23, 2016 2:50:15 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "text": "%r pairs(iris)",
+      "dateUpdated": "Feb 23, 2016 2:50:20 PM",
+      "config": {
+        "colWidth": 4.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala"
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455137735427_-1023869289",
+      "id": "20160210-215535_1815168219",
+      "result": {
+        "code": "SUCCESS",
+        "type": "HTML",
+        "msg": "\u003cp\u003e\u003cimg 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+      "dateStarted": "Feb 23, 2016 2:50:20 PM",
+      "dateFinished": "Feb 23, 2016 2:50:20 PM",
+      "status": "FINISHED",
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+    },
+    {
+      "text": "%r {\"imageWidth\": \"400px\"} plot(iris, col \u003d heat.colors(3))",
+      "dateUpdated": "Feb 23, 2016 2:50:22 PM",
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+          "values": [],
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+        "enabled": true,
+        "editorMode": "ace/mode/scala"
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+      "settings": {
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+        "forms": {}
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+      "jobName": "paragraph_1455137737773_-549089146",
+      "id": "20160210-215537_582262164",
+      "result": {
+        "code": "SUCCESS",
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\u003d\" alt\u003d\"plot of chunk unnamed-chunk-1\" width\u003d\"400px\" /\u003e \u003c/p\u003e"
+      },
+      "dateCreated": "Feb 10, 2016 9:55:37 PM",
+      "dateStarted": "Feb 23, 2016 2:50:22 PM",
+      "dateFinished": "Feb 23, 2016 2:50:22 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "text": "%r {\"imageWidth\": \"250px\"}\n\nmosaicplot(Titanic, main \u003d \"Survival on the Titanic\")\n\npairs(mtcars)\nlibrary(data.table)\n\ndt \u003c- data.table(1:30)\nplot(dt[[1]], 1:30)",
+      "dateUpdated": "Feb 23, 2016 2:50:23 PM",
+      "config": {
+        "colWidth": 4.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala"
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455130080006_-1413003071",
+      "id": "20160210-194800_298249044",
+      "result": {
+        "code": "SUCCESS",
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\u003d\" 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\u003d\" alt\u003d\"plot of chunk unnamed-chunk-1\" width\u003d\"250px\" /\u003e \u003c/p\u003e"
+      },
+      "dateCreated": "Feb 10, 2016 7:48:00 PM",
+      "dateStarted": "Feb 23, 2016 2:50:23 PM",
+      "dateFinished": "Feb 23, 2016 2:50:24 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "RCharts Map",
+      "text": "%r\nrequire(rCharts)\nmap3 \u003c- Leaflet$new()\nmap3$setView(c(51.505, -0.09), zoom \u003d 13)\nmap3$marker(c(51.5, -0.09), bindPopup \u003d \"\u003cp\u003e Hi. I am a popup \u003c/p\u003e\")\nmap3$marker(c(51.495, -0.083), bindPopup \u003d \"\u003cp\u003e Hi. I am another popup \u003c/p\u003e\")\nmap3$print(\"map3\", include_assets\u003dTRUE, cdn\u003dTRUE)\n",
+      "dateUpdated": "Feb 23, 2016 2:50:25 PM",
+      "config": {
+        "colWidth": 6.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala",
+        "lineNumbers": false,
+        "title": true,
+        "editorHide": false
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455141266539_-447013854",
+      "id": "20160210-225426_326747114",
+      "result": {
+        "code": "SUCCESS",
+        "type": "HTML",
+        "msg": "\u003cp\u003e\u003clink rel\u003d\"stylesheet\" href\u003d\"http://cdn.leafletjs.com/leaflet-0.5.1/leaflet.css\" /\u003e\u003c/p\u003e\u003cscript type\u003d\"text/javascript\" src\u003d\"http://cdn.leafletjs.com/leaflet-0.5.1/leaflet.js\"\u003e\u003c/script\u003e\u003cscript type\u003d\"text/javascript\" src\u003d\"http://rawgithub.com/leaflet-extras/leaflet-providers/gh-pages/leaflet-providers.js\"\u003e\u003c/script\u003e\u003cscript type\u003d\"text/javascript\" src\u003d\"http://harrywood.co.uk/maps/examples/leaflet/leaflet-plugins/layer/vector/KML.js\"\u003e\u003c/script\u003e\u003cp\u003e\u003cstyle\u003e\n  .rChart {\n    display: block;\n    margin-left: auto; \n    margin-right: auto;\n    width: 800px;\n    height: 400px;\n  }\u003cbr/\u003e\n  \u003c/style\u003e\u003c/p\u003e\u003cdiv id\u003d\"map3\" class\u003d\"rChart leaflet\"\u003e\u003c/div\u003e\u003cscript\u003e\n  var spec \u003d {\n \"dom\": \"map3\",\n\"width\":            800,\n\"height\":            400,\n\"urlTemplate\": \"http://{s}.tile.osm.org/{z}/{x}/{y}.png\",\n\"layerOpts\": {\n \"attribution\": \"Map data\u003ca href\u003d\\\"http://openstreetmap.org\\\"\u003eOpenStreetMap\u003c/a\u003e\\n         contributors, Imagery\u003ca href\u003d\\\"http://mapbox.com\\\"\u003eMapBox\u003c/a\u003e\" \n},\n\"center\": [         51.505,          -0.09 ],\n\"zoom\":             13,\n\"id\": \"map3\" \n}\n\n  var map \u003d L.map(spec.dom, spec.mapOpts)\n  \n    map.setView(spec.center, spec.zoom);\n\n    if (spec.provider){\n      L.tileLayer.provider(spec.provider).addTo(map)    \n    } else {\n          L.tileLayer(spec.urlTemplate, spec.layerOpts).addTo(map)\n    }\n     \n    L\n  .marker([\n   51.5,\n -0.09 \n])\n  .addTo( map )\n  .bindPopup(\"\u003cp\u003e Hi. I am a popup \u003c/p\u003e\")\nL\n  .marker([\n 51.495,\n-0.083 \n])\n  .addTo( map )\n  .bindPopup(\"\u003cp\u003e Hi. I am another popup \u003c/p\u003e\")\n    \n    \n    \n    \n    if (spec.circle2){\n      for (var c in spec.circle2){\n        var circle \u003d L.circle(c.center, c.radius, c.opts)\n         .addTo(map);\n      }\n    }\n    \n    \n    \n    \n    \n   \n   \n   \n\u003c/script\u003e"
+      },
+      "dateCreated": "Feb 10, 2016 10:54:26 PM",
+      "dateStarted": "Feb 23, 2016 2:50:25 PM",
+      "dateFinished": "Feb 23, 2016 2:50:26 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "title": "GoogleViz",
+      "text": "%r\nlibrary(googleVis)\nbubble \u003c- gvisBubbleChart(Fruits, idvar\u003d\"Fruit\", \n                          xvar\u003d\"Sales\", yvar\u003d\"Expenses\",\n                          colorvar\u003d\"Year\", sizevar\u003d\"Profit\",\n                          options\u003dlist(\n                            hAxis\u003d\u0027{minValue:75, maxValue:125}\u0027))\nprint(bubble, tag \u003d \u0027chart\u0027)",
+      "dateUpdated": "Feb 23, 2016 2:50:27 PM",
+      "config": {
+        "colWidth": 6.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala",
+        "title": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455141578555_-1713165000",
+      "id": "20160210-225938_1538591791",
+      "result": {
+        "code": "ERROR",
+        "type": "HTML",
+        "msg": "\u003c!-- BubbleChart generated in R 3.1.2 by googleVis 0.5.10 package --\u003e\n\n\u003c!-- Tue Feb 23 14:50:27 2016 --\u003e\n\n\u003c!-- jsHeader --\u003e\n\n\u003cscript type\u003d\"text/javascript\"\u003e\n \n// jsData \nfunction gvisDataBubbleChartID3c7e5eba3096 () {\nvar data \u003d new google.visualization.DataTable();\nvar datajson \u003d\n[\n [\n \"Apples\",\n98,\n78,\n\"2008\",\n20 \n],\n[\n \"Apples\",\n111,\n79,\n\"2009\",\n32 \n],\n[\n \"Apples\",\n89,\n76,\n\"2010\",\n13 \n],\n[\n \"Oranges\",\n96,\n81,\n\"2008\",\n15 \n],\n[\n \"Bananas\",\n85,\n76,\n\"2008\",\n9 \n],\n[\n \"Oranges\",\n93,\n80,\n\"2009\",\n13 \n],\n[\n \"Bananas\",\n94,\n78,\n\"2009\",\n16 \n],\n[\n \"Oranges\",\n98,\n91,\n\"2010\",\n7 \n],\n[\n \"Bananas\",\n81,\n71,\n\"2010\",\n10 \n] \n];\ndata.addColumn(\u0027string\u0027,\u0027Fruit\u0027);\ndata.addColumn(\u0027number\u0027,\u0027Sales\u0027);\ndata.addColumn(\u0027number\u0027,\u0027Expenses\u0027);\ndata.addColumn(\u0027string\u0027,\u0027Year\u0027);\ndata.addColumn(\u0027number\u0027,\u0027Profit\u0027);\ndata.addRows(datajson);\nreturn(data);\n}\n \n// jsDrawChart\nfunction drawChartBubbleChartID3c7e5eba3096() {\nvar data \u003d gvisDataBubbleChartID3c7e5eba3096();\nvar options \u003d {};\noptions[\"hAxis\"] \u003d {minValue:75, maxValue:125};\n\n    var chart \u003d new google.visualization.BubbleChart(\n    document.getElementById(\u0027BubbleChartID3c7e5eba3096\u0027)\n    );\n    chart.draw(data,options);\n    \n\n}\n  \n \n// jsDisplayChart\n(function() {\nvar pkgs \u003d window.__gvisPackages \u003d window.__gvisPackages || [];\nvar callbacks \u003d window.__gvisCallbacks \u003d window.__gvisCallbacks || [];\nvar chartid \u003d \"corechart\";\n  \n// Manually see if chartid is in pkgs (not all browsers support Array.indexOf)\nvar i, newPackage \u003d true;\nfor (i \u003d 0; newPackage \u0026\u0026 i \u003c pkgs.length; i++) {\nif (pkgs[i] \u003d\u003d\u003d chartid)\nnewPackage \u003d false;\n}\nif (newPackage)\n  pkgs.push(chartid);\n  \n// Add the drawChart function to the global list of callbacks\ncallbacks.push(drawChartBubbleChartID3c7e5eba3096);\n})();\nfunction displayChartBubbleChartID3c7e5eba3096() {\n  var pkgs \u003d window.__gvisPackages \u003d window.__gvisPackages || [];\n  var callbacks \u003d window.__gvisCallbacks \u003d window.__gvisCallbacks || [];\n  window.clearTimeout(window.__gvisLoad);\n  // The timeout is set to 100 because otherwise the container div we are\n  // targeting might not be part of the document yet\n  window.__gvisLoad \u003d setTimeout(function() {\n  var pkgCount \u003d pkgs.length;\n  google.load(\"visualization\", \"1\", { packages:pkgs, callback: function() {\n  if (pkgCount !\u003d pkgs.length) {\n  // Race condition where another setTimeout call snuck in after us; if\n  // that call added a package, we must not shift its callback\n  return;\n}\nwhile (callbacks.length \u003e 0)\ncallbacks.shift()();\n} });\n}, 100);\n}\n \n// jsFooter\n\u003c/script\u003e\n \n\n\u003c!-- jsChart --\u003e  \n\n\u003cscript type\u003d\"text/javascript\" src\u003d\"https://www.google.com/jsapi?callback\u003ddisplayChartBubbleChartID3c7e5eba3096\"\u003e\u003c/script\u003e\n \n\n\u003c!-- divChart --\u003e\n\n\u003cdiv id\u003d\"BubbleChartID3c7e5eba3096\" style\u003d\"width: 500; height: automatic;\"\u003e\n\u003c/div\u003e"
+      },
+      "dateCreated": "Feb 10, 2016 10:59:38 PM",
+      "dateStarted": "Feb 23, 2016 2:50:27 PM",
+      "dateFinished": "Feb 23, 2016 2:50:27 PM",
+      "status": "ERROR",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "text": "%r\nlibrary(googleVis)\ngeo \u003d gvisGeoChart(Exports, locationvar \u003d \"Country\", colorvar\u003d\"Profit\", options\u003dlist(Projection \u003d \"kavrayskiy-vii\"))\nprint(geo, tag \u003d \u0027chart\u0027)",
+      "dateUpdated": "Feb 23, 2016 2:50:29 PM",
+      "config": {
+        "colWidth": 6.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala"
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455140544963_1486338978",
+      "id": "20160210-224224_735421242",
+      "result": {
+        "code": "ERROR",
+        "type": "HTML",
+        "msg": "\u003c!-- GeoChart generated in R 3.1.2 by googleVis 0.5.10 package --\u003e\n\n\u003c!-- Tue Feb 23 14:50:29 2016 --\u003e\n\n\u003c!-- jsHeader --\u003e\n\n\u003cscript type\u003d\"text/javascript\"\u003e\n \n// jsData \nfunction gvisDataGeoChartID3c7e62c8d602 () {\nvar data \u003d new google.visualization.DataTable();\nvar datajson \u003d\n[\n [\n \"Germany\",\n3 \n],\n[\n \"Brazil\",\n4 \n],\n[\n \"United States\",\n5 \n],\n[\n \"France\",\n4 \n],\n[\n \"Hungary\",\n3 \n],\n[\n \"India\",\n2 \n],\n[\n \"Iceland\",\n1 \n],\n[\n \"Norway\",\n4 \n],\n[\n \"Spain\",\n5 \n],\n[\n \"Turkey\",\n1 \n] \n];\ndata.addColumn(\u0027string\u0027,\u0027Country\u0027);\ndata.addColumn(\u0027number\u0027,\u0027Profit\u0027);\ndata.addRows(datajson);\nreturn(data);\n}\n \n// jsDrawChart\nfunction drawChartGeoChartID3c7e62c8d602() {\nvar data \u003d gvisDataGeoChartID3c7e62c8d602();\nvar options \u003d {};\noptions[\"width\"] \u003d    556;\noptions[\"height\"] \u003d    347;\noptions[\"Projection\"] \u003d \"kavrayskiy-vii\";\n\n    var chart \u003d new google.visualization.GeoChart(\n    document.getElementById(\u0027GeoChartID3c7e62c8d602\u0027)\n    );\n    chart.draw(data,options);\n    \n\n}\n  \n \n// jsDisplayChart\n(function() {\nvar pkgs \u003d window.__gvisPackages \u003d window.__gvisPackages || [];\nvar callbacks \u003d window.__gvisCallbacks \u003d window.__gvisCallbacks || [];\nvar chartid \u003d \"geochart\";\n  \n// Manually see if chartid is in pkgs (not all browsers support Array.indexOf)\nvar i, newPackage \u003d true;\nfor (i \u003d 0; newPackage \u0026\u0026 i \u003c pkgs.length; i++) {\nif (pkgs[i] \u003d\u003d\u003d chartid)\nnewPackage \u003d false;\n}\nif (newPackage)\n  pkgs.push(chartid);\n  \n// Add the drawChart function to the global list of callbacks\ncallbacks.push(drawChartGeoChartID3c7e62c8d602);\n})();\nfunction displayChartGeoChartID3c7e62c8d602() {\n  var pkgs \u003d window.__gvisPackages \u003d window.__gvisPackages || [];\n  var callbacks \u003d window.__gvisCallbacks \u003d window.__gvisCallbacks || [];\n  window.clearTimeout(window.__gvisLoad);\n  // The timeout is set to 100 because otherwise the container div we are\n  // targeting might not be part of the document yet\n  window.__gvisLoad \u003d setTimeout(function() {\n  var pkgCount \u003d pkgs.length;\n  google.load(\"visualization\", \"1\", { packages:pkgs, callback: function() {\n  if (pkgCount !\u003d pkgs.length) {\n  // Race condition where another setTimeout call snuck in after us; if\n  // that call added a package, we must not shift its callback\n  return;\n}\nwhile (callbacks.length \u003e 0)\ncallbacks.shift()();\n} });\n}, 100);\n}\n \n// jsFooter\n\u003c/script\u003e\n \n\n\u003c!-- jsChart --\u003e  \n\n\u003cscript type\u003d\"text/javascript\" src\u003d\"https://www.google.com/jsapi?callback\u003ddisplayChartGeoChartID3c7e62c8d602\"\u003e\u003c/script\u003e\n \n\n\u003c!-- divChart --\u003e\n\n\u003cdiv id\u003d\"GeoChartID3c7e62c8d602\" style\u003d\"width: 556; height: 347;\"\u003e\n\u003c/div\u003e"
+      },
+      "dateCreated": "Feb 10, 2016 10:42:24 PM",
+      "dateStarted": "Feb 23, 2016 2:50:29 PM",
+      "dateFinished": "Feb 23, 2016 2:50:29 PM",
+      "status": "ERROR",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "text": "%r\nrequire(rCharts)\ndata(economics, package \u003d \u0027ggplot2\u0027)\necon \u003c- transform(economics, date \u003d as.character(date))\nm1 \u003c- mPlot(x \u003d \u0027date\u0027, y \u003d c(\u0027psavert\u0027, \u0027uempmed\u0027), type \u003d \u0027Line\u0027, data \u003d econ)\nm1$set(pointSize \u003d 0, lineWidth \u003d 1)\nm1$show(\u0027inline\u0027, include_assets\u003dTRUE, cdn\u003dTRUE)",
+      "dateUpdated": "Feb 23, 2016 2:50:31 PM",
+      "config": {
+        "colWidth": 6.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true,
+        "editorMode": "ace/mode/scala"
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455130083870_1243919591",
+      "id": "20160210-194803_744501428",
+      "result": {
+        "code": "SUCCESS",
+        "type": "HTML",
+        "msg": "\u003cp\u003e\u003clink rel\u003d\"stylesheet\" href\u003d\"http://cdn.oesmith.co.uk/morris-0.4.2.min.css\" /\u003e\u003c/p\u003e\u003cscript type\u003d\"text/javascript\" src\u003d\"http://ajax.googleapis.com/ajax/libs/jquery/1.9.0/jquery.min.js\"\u003e\u003c/script\u003e\u003cscript type\u003d\"text/javascript\" src\u003d\"http://cdnjs.cloudflare.com/ajax/libs/raphael/2.1.0/raphael-min.js\"\u003e\u003c/script\u003e\u003cscript type\u003d\"text/javascript\" src\u003d\"http://cdn.oesmith.co.uk/morris-0.4.2.min.js\"\u003e\u003c/script\u003e\u003cp\u003e\u003cstyle\u003e\n  .rChart {\n    display: block;\n    margin-left: auto; \n    margin-right: auto;\n    width: 800px;\n    height: 400px;\n  }\u003cbr/\u003e\n  \u003c/style\u003e\u003c/p\u003e\u003cdiv id\u003d\"chart3c7e79bc798\" class\u003d\"rChart morris\"\u003e\u003c/div\u003e\u003cscript type\u003d\"text/javascript\"\u003e\n    var chartParams \u003d {\n \"element\": \"chart3c7e79bc798\",\n\"width\":            800,\n\"height\":            400,\n\"xkey\": \"date\",\n\"ykeys\": [\n \"psavert\",\n\"uempmed\" \n],\n\"data\": [\n {\n \"date\": \"1967-06-30\",\n\"pce\":          507.8,\n\"pop\": 198712,\n\"psavert\":            9.8,\n\"uempmed\":            4.5,\n\"unemploy\": 2944 \n},\n{\n \"date\": \"1967-07-31\",\n\"pce\":          510.9,\n\"pop\": 198911,\n\"psavert\":            9.8,\n\"uempmed\":            4.7,\n\"unemploy\": 2945 \n},\n{\n \"date\": \"1967-08-31\",\n\"pce\":          516.7,\n\"pop\": 199113,\n\"psavert\":              9,\n\"uempmed\":            4.6,\n\"unemploy\": 2958 \n},\n{\n \"date\": \"1967-09-30\",\n\"pce\":          513.3,\n\"pop\": 199311,\n\"psavert\":            9.8,\n\"uempmed\":            4.9,\n\"unemploy\": 3143 \n},\n{\n \"date\": \"1967-10-31\",\n\"pce\":          518.5,\n\"pop\": 199498,\n\"psavert\":            9.7,\n\"uempmed\":            4.7,\n\"unemploy\": 3066 \n},\n{\n \"date\": \"1967-11-30\",\n\"pce\":          526.2,\n\"pop\": 199657,\n\"psavert\":            9.4,\n\"uempmed\":            4.8,\n\"unemploy\": 3018 \n},\n{\n \"date\": \"1967-12-31\",\n\"pce\":            532,\n\"pop\": 199808,\n\"psavert\":              9,\n\"uempmed\":            5.1,\n\"unemploy\": 2878 \n},\n{\n \"date\": \"1968-01-31\",\n\"pce\":          534.7,\n\"pop\": 199920,\n\"psavert\":            9.5,\n\"uempmed\":            4.5,\n\"unemploy\": 3001 \n},\n{\n \"date\": \"1968-02-29\",\n\"pce\":          545.4,\n\"pop\": 200056,\n\"psavert\":            8.9,\n\"uempmed\":            4.1,\n\"unemploy\": 2877 \n},\n{\n \"date\": \"1968-03-31\",\n\"pce\":          545.1,\n\"pop\": 200208,\n\"psavert\":            9.6,\n\"uempmed\":            4.6,\n\"unemploy\": 2709 \n},\n{\n \"date\": \"1968-04-30\",\n\"pce\":          550.9,\n\"pop\": 200361,\n\"psavert\":            9.3,\n\"uempmed\":            4.4,\n\"unemploy\": 2740 \n},\n{\n \"date\": \"1968-05-31\",\n\"pce\":          557.4,\n\"pop\": 200536,\n\"psavert\":            8.9,\n\"uempmed\":            4.4,\n\"unemploy\": 2938 \n},\n{\n \"date\": \"1968-06-30\",\n\"pce\":          564.4,\n\"pop\": 200706,\n\"psavert\":            7.8,\n\"uempmed\":            4.5,\n\"unemploy\": 2883 \n},\n{\n \"date\": \"1968-07-31\",\n\"pce\":          568.2,\n\"pop\": 200898,\n\"psavert\":            7.6,\n\"uempmed\":            4.2,\n\"unemploy\": 2768 \n},\n{\n \"date\": \"1968-08-31\",\n\"pce\":          569.5,\n\"pop\": 201095,\n\"psavert\":            7.6,\n\"uempmed\":            4.6,\n\"unemploy\": 2686 \n},\n{\n \"date\": \"1968-09-30\",\n\"pce\":          572.9,\n\"pop\": 201290,\n\"psavert\":            7.8,\n\"uempmed\":            4.8,\n\"unemploy\": 2689 \n},\n{\n \"date\": \"1968-10-31\",\n\"pce\":            578,\n\"pop\": 201466,\n\"psavert\":            7.6,\n\"uempmed\":            4.4,\n\"unemploy\": 2715 \n},\n{\n \"date\": \"1968-11-30\",\n\"pce\":          577.9,\n\"pop\": 201621,\n\"psavert\":            8.1,\n\"uempmed\":            4.4,\n\"unemploy\": 2685 \n},\n{\n \"date\": \"1968-12-31\",\n\"pce\":          584.9,\n\"pop\": 201760,\n\"psavert\":            7.1,\n\"uempmed\":            4.4,\n\"unemploy\": 2718 \n},\n{\n \"date\": \"1969-01-31\",\n\"pce\":          590.2,\n\"pop\": 201881,\n\"psavert\":            6.5,\n\"uempmed\":            4.9,\n\"unemploy\": 2692 \n},\n{\n \"date\": \"1969-02-28\",\n\"pce\":          590.4,\n\"pop\": 202023,\n\"psavert\":              7,\n\"uempmed\":              4,\n\"unemploy\": 2712 \n},\n{\n \"date\": \"1969-03-31\",\n\"pce\":          595.4,\n\"pop\": 202161,\n\"psavert\":            6.6,\n\"uempmed\":              4,\n\"unemploy\": 2758 \n},\n{\n \"date\": \"1969-04-30\",\n\"pce\":          601.8,\n\"pop\": 202331,\n\"psavert\":              7,\n\"uempmed\":            4.2,\n\"unemploy\": 2713 \n},\n{\n \"date\": \"1969-05-31\",\n\"pce\":          602.4,\n\"pop\": 202507,\n\"psavert\":            7.9,\n\"uempmed\":            4.4,\n\"unemploy\": 2816 \n},\n{\n \"date\": \"1969-06-30\",\n\"pce\":          604.3,\n\"pop\": 202677,\n\"psavert\":            8.7,\n\"uempmed\":            4.4,\n\"unemploy\": 2868 \n},\n{\n \"date\": \"1969-07-31\",\n\"pce\":          611.5,\n\"pop\": 202877,\n\"psavert\":            8.5,\n\"uempmed\":            4.4,\n\"unemploy\": 2856 \n},\n{\n \"date\": \"1969-08-31\",\n\"pce\":          614.9,\n\"pop\": 203090,\n\"psavert\":            8.5,\n\"uempmed\":            4.7,\n\"unemploy\": 3040 \n},\n{\n \"date\": \"1969-09-30\",\n\"pce\":          620.2,\n\"pop\": 203302,\n\"psavert\":            8.3,\n\"uempmed\":            4.5,\n\"unemploy\": 3049 \n},\n{\n \"date\": \"1969-10-31\",\n\"pce\":          622.1,\n\"pop\": 203500,\n\"psavert\":            8.5,\n\"uempmed\":            4.8,\n\"unemploy\": 2856 \n},\n{\n \"date\": \"1969-11-30\",\n\"pce\":          624.4,\n\"pop\": 203675,\n\"psavert\":            8.6,\n\"uempmed\":            4.6,\n\"unemploy\": 2884 \n},\n{\n \"date\": \"1969-12-31\",\n\"pce\":          630.4,\n\"pop\": 203849,\n\"psavert\":            8.3,\n\"uempmed\":            4.6,\n\"unemploy\": 3201 \n},\n{\n \"date\": \"1970-01-31\",\n\"pce\":          635.7,\n\"pop\": 204008,\n\"psavert\":            8.1,\n\"uempmed\":            4.5,\n\"unemploy\": 3453 \n},\n{\n \"date\": \"1970-02-28\",\n\"pce\":            634,\n\"pop\": 204156,\n\"psavert\":            8.8,\n\"uempmed\":            4.6,\n\"unemploy\": 3635 \n},\n{\n \"date\": \"1970-03-31\",\n\"pce\":          637.7,\n\"pop\": 204401,\n\"psavert\":           10.5,\n\"uempmed\":            4.1,\n\"unemploy\": 3797 \n},\n{\n \"date\": \"1970-04-30\",\n\"pce\":          644.1,\n\"pop\": 204607,\n\"psavert\":            9.4,\n\"uempmed\":            4.7,\n\"unemploy\": 3919 \n},\n{\n \"date\": \"1970-05-31\",\n\"pce\":            648,\n\"pop\": 204830,\n\"psavert\":            8.7,\n\"uempmed\":            4.9,\n\"unemploy\": 4071 \n},\n{\n \"date\": \"1970-06-30\",\n\"pce\":          650.2,\n\"pop\": 205052,\n\"psavert\":             10,\n\"uempmed\":            5.1,\n\"unemploy\": 4175 \n},\n{\n \"date\": \"1970-07-31\",\n\"pce\":          654.7,\n\"pop\": 205295,\n\"psavert\":             10,\n\"uempmed\":            5.4,\n\"unemploy\": 4256 \n},\n{\n \"date\": \"1970-08-31\",\n\"pce\":          660.9,\n\"pop\": 205540,\n\"psavert\":            9.8,\n\"uempmed\":            5.2,\n\"unemploy\": 4456 \n},\n{\n \"date\": \"1970-09-30\",\n\"pce\":          660.1,\n\"pop\": 205788,\n\"psavert\":            9.8,\n\"uempmed\":            5.2,\n\"unemploy\": 4591 \n},\n{\n \"date\": \"1970-10-31\",\n\"pce\":          658.4,\n\"pop\": 206024,\n\"psavert\":           10.1,\n\"uempmed\":            5.6,\n\"unemploy\": 4898 \n},\n{\n \"date\": \"1970-11-30\",\n\"pce\":          667.4,\n\"pop\": 206238,\n\"psavert\":            9.7,\n\"uempmed\":            5.9,\n\"unemploy\": 5076 \n},\n{\n \"date\": \"1970-12-31\",\n\"pce\":            678,\n\"pop\": 206466,\n\"psavert\":             10,\n\"uempmed\":            6.2,\n\"unemploy\": 4986 \n},\n{\n \"date\": \"1971-01-31\",\n\"pce\":          681.3,\n\"pop\": 206668,\n\"psavert\":            9.9,\n\"uempmed\":            6.3,\n\"unemploy\": 4903 \n},\n{\n \"date\": \"1971-02-28\",\n\"pce\":          683.9,\n\"pop\": 206855,\n\"psavert\":           10.2,\n\"uempmed\":            6.4,\n\"unemploy\": 4987 \n},\n{\n \"date\": \"1971-03-31\",\n\"pce\":          690.6,\n\"pop\": 207065,\n\"psavert\":            9.9,\n\"uempmed\":            6.5,\n\"unemploy\": 4959 \n},\n{\n \"date\": \"1971-04-30\",\n\"pce\":            693,\n\"pop\": 207260,\n\"psavert\":           10.2,\n\"uempmed\":            6.7,\n\"unemploy\": 4996 \n},\n{\n \"date\": \"1971-05-31\",\n\"pce\":          701.7,\n\"pop\": 207462,\n\"psavert\":           11.4,\n\"uempmed\":            5.7,\n\"unemploy\": 4949 \n},\n{\n \"date\": \"1971-06-30\",\n\"pce\":          700.8,\n\"pop\": 207661,\n\"psavert\":           10.4,\n\"uempmed\":            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\u003d\" alt\u003d\"plot of chunk unnamed-chunk-1\" width\u003d\"300px\" /\u003e \u003c/p\u003e"
+      },
+      "dateCreated": "Aug 7, 2015 9:01:20 AM",
+      "dateStarted": "Feb 23, 2016 2:50:32 PM",
+      "dateFinished": "Feb 23, 2016 2:50:33 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    },
+    {
+      "dateUpdated": "Feb 11, 2016 12:16:42 PM",
+      "config": {
+        "colWidth": 12.0,
+        "graph": {
+          "mode": "table",
+          "height": 300.0,
+          "optionOpen": false,
+          "keys": [],
+          "values": [],
+          "groups": [],
+          "scatter": {}
+        },
+        "enabled": true
+      },
+      "settings": {
+        "params": {},
+        "forms": {}
+      },
+      "jobName": "paragraph_1455138857313_92355963",
+      "id": "20160210-221417_1400405266",
+      "result": {
+        "code": "SUCCESS",
+        "type": "TEXT",
+        "msg": ""
+      },
+      "dateCreated": "Feb 10, 2016 10:14:17 PM",
+      "dateStarted": "Feb 11, 2016 12:16:57 PM",
+      "dateFinished": "Feb 11, 2016 12:16:57 PM",
+      "status": "FINISHED",
+      "progressUpdateIntervalMs": 500
+    }
+  ],
+  "name": "R Tutorial",
+  "id": "r",
+  "angularObjects": {
+    "2BGNYGX62": [],
+    "2BH79J543": [],
+    "2BGEQ12SH": [],
+    "2BHEC1U86": [],
+    "2BFC8VSCU": [],
+    "2BEFFGZ94": [],
+    "2BHXPBPPB": [],
+    "2BEUFCFMV": [],
+    "2BEFB8NYY": [],
+    "2BE83RBH6": [],
+    "2BGHNUYS3": [],
+    "2BHWPPHUF": [],
+    "2BHZGMJB4": [],
+    "2BHRGU5SV": [],
+    "2BE19PPM5": [],
+    "2BF3RSAR9": [],
+    "2BFBDB2V5": [],
+    "2BHBNXNS5": []
+  },
+  "config": {
+    "looknfeel": "default"
+  },
+  "info": {}
+}
\ No newline at end of file
diff --git a/pom.xml b/pom.xml
index e5f7d9ac4ae..642037d727f 100755
--- a/pom.xml
+++ b/pom.xml
@@ -17,7 +17,7 @@
   -->
 
 <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.maven-v4_0_0.xsd">
 
   <!-- Licensed to the Apache Software Foundation (ASF) under one or more
     contributor license agreements. See the NOTICE file distributed with this
diff --git a/spark-dependencies/pom.xml b/spark-dependencies/pom.xml
index 02d24e2df54..8e23f22e3e8 100644
--- a/spark-dependencies/pom.xml
+++ b/spark-dependencies/pom.xml
@@ -54,6 +54,7 @@
     <spark.download.url>
       http://archive.apache.org/dist/spark/${spark.archive}/${spark.archive}.tgz
     </spark.download.url>
+    <spark.bin.download.url>http://archive.apache.org/dist/spark/spark-${spark.version}/spark-${spark.version}-bin-without-hadoop.tgz</spark.bin.download.url>
     <spark.dist.cache>${project.build.directory}/../../.spark-dist</spark.dist.cache>
     <py4j.version>0.8.2.1</py4j.version>
   </properties>
@@ -802,10 +803,10 @@
             <configuration>
               <filesets>
                 <fileset>
-                  <directory>${basedir}/../python/build</directory>
+                  <directory>${project.build.directory}/spark-dist</directory>
                 </fileset>
                 <fileset>
-                  <directory>${project.build.directory}/spark-dist</directory>
+                  <directory>${basedir}/../python/build</directory>
                 </fileset>
               </filesets>
             </configuration>
@@ -844,7 +845,65 @@
                     <zip destfile="${project.build.directory}/../../interpreter/spark/pyspark/pyspark.zip"
                          basedir="${project.build.directory}/spark-dist/${spark.archive}/python"
                          includes="pyspark/*.py,pyspark/**/*.py"/>
-                  </target>
+                  </target>                
+                </configuration>
+              </execution>
+            </executions>
+          </plugin>
+        </plugins>
+      </build>
+    </profile>
+
+    <profile>
+      <id>sparkr</id>
+      <build>
+        <plugins>
+          <plugin>
+            <groupId>com.googlecode.maven-download-plugin</groupId>
+            <artifactId>download-maven-plugin</artifactId>
+            <version>1.2.1</version>
+            <executions>
+              <execution>
+                <id>download-sparkr-files</id>
+                <phase>validate</phase>
+                <goals>
+                  <goal>wget</goal>
+                </goals>
+                <configuration>
+                  <url>${spark.bin.download.url}</url>
+                  <unpack>true</unpack>
+                  <outputDirectory>${project.build.directory}/spark-bin-dist</outputDirectory>
+                </configuration>
+              </execution>
+            </executions>
+          </plugin>
+          <plugin>
+            <artifactId>maven-clean-plugin</artifactId>
+            <configuration>
+              <filesets>
+                <fileset>
+                  <directory>${project.build.directory}/spark-bin-dist</directory>
+                </fileset>
+              </filesets>
+            </configuration>
+          </plugin>
+          <plugin>
+            <artifactId>maven-resources-plugin</artifactId>
+            <version>2.7</version>
+            <executions>
+              <execution>
+                <id>copy-sparkr-files</id>
+                <phase>generate-resources</phase>
+                <goals>
+                  <goal>copy-resources</goal>
+                </goals>
+                <configuration>
+                  <outputDirectory>${project.build.directory}/../../interpreter/spark/R/lib</outputDirectory>
+                  <resources>
+                    <resource>
+                      <directory>${project.build.directory}/spark-bin-dist/spark-${spark.version}-bin-without-hadoop/R/lib</directory>
+                    </resource>
+                  </resources>
                 </configuration>
               </execution>
             </executions>
@@ -852,6 +911,7 @@
         </plugins>
       </build>
     </profile>
+    
   </profiles>
 
   <build>
diff --git a/spark/pom.xml b/spark/pom.xml
index a1c28af7832..3b88f3b82a8 100644
--- a/spark/pom.xml
+++ b/spark/pom.xml
@@ -35,12 +35,14 @@
   <url>http://zeppelin.incubator.apache.org</url>
 
   <properties>
+    <jsoup.version>1.8.2</jsoup.version>
+    <mockito.version>1.10.19</mockito.version>
+    <powermock.version>1.6.4</powermock.version>
     <spark.version>1.4.1</spark.version>
     <scala.version>2.10.4</scala.version>
     <scala.binary.version>2.10</scala.binary.version>
   </properties>
 
-
   <dependencies>
     <dependency>
       <groupId>org.slf4j</groupId>
@@ -231,6 +233,11 @@
       <scope>provided</scope>
     </dependency>
 
+    <dependency>
+      <groupId>org.jsoup</groupId>
+      <artifactId>jsoup</artifactId>
+      <version>${jsoup.version}</version>
+    </dependency>
 
     <!--TEST-->
     <dependency>
@@ -267,6 +274,25 @@
       <scope>test</scope>
     </dependency>
 
+    <dependency>
+      <groupId>org.mockito</groupId>
+      <artifactId>mockito-core</artifactId>
+      <version>${mockito.version}</version>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.powermock</groupId>
+      <artifactId>powermock-api-mockito</artifactId>
+      <version>${powermock.version}</version>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.powermock</groupId>
+      <artifactId>powermock-module-junit4</artifactId>
+      <version>${powermock.version}</version>
+      <scope>test</scope>
+    </dependency>
+
   </dependencies>
 
   <build>
@@ -286,7 +312,7 @@
             <exclude>**/derby.log</exclude>
             <exclude>**/metastore_db/</exclude>
             <exclude>**/README.md</exclude>
-            <exclude>dependency-reduced-pom.xml</exclude>
+            <exclude>**/dependency-reduced-pom.xml</exclude>
           </excludes>
         </configuration>
       </plugin>
@@ -404,4 +430,56 @@
 
     </plugins>
   </build>
+
+  <profiles>
+
+    <!-- to deactivate 'exclude-sparkr' automatically when 'spark' is activated -->
+    <profile>
+      <id>sparkr</id>
+    </profile>
+
+    <profile>
+      <id>exclude-sparkr</id>
+      <activation>
+        <activeByDefault>true</activeByDefault>
+      </activation>
+      <build>
+        <plugins>
+          <plugin>
+            <groupId>org.apache.maven.plugins</groupId>
+            <artifactId>maven-compiler-plugin</artifactId>
+            <configuration>
+              <excludes>
+                <exclude>**/SparkRInterpreter.java</exclude>
+              </excludes>
+              <testExcludes>
+                <testExclude>**/SparkRInterpreterTest.java</testExclude>
+                <testExclude>**/ZeppelinRTest.java</testExclude>
+              </testExcludes>
+            </configuration>
+          </plugin>
+          <plugin>
+            <groupId>org.scala-tools</groupId>
+            <artifactId>maven-scala-plugin</artifactId>
+            <configuration>
+              <excludes>
+                <exclude>**/ZeppelinR.scala</exclude>
+                <exclude>**/SparkRBackend.scala</exclude>
+              </excludes>
+            </configuration>
+          </plugin>
+          <plugin>
+            <groupId>org.apache.maven.plugins</groupId>
+            <artifactId>maven-surefire-plugin</artifactId>
+            <configuration>
+              <excludes>
+                <exclude>**/SparkRInterpreterTest.java</exclude>
+              </excludes>
+            </configuration>
+          </plugin>
+        </plugins>
+      </build>
+    </profile>
+  </profiles>
+
 </project>
diff --git a/spark/src/main/java/org/apache/zeppelin/spark/SparkRInterpreter.java b/spark/src/main/java/org/apache/zeppelin/spark/SparkRInterpreter.java
new file mode 100644
index 00000000000..e0ea766212f
--- /dev/null
+++ b/spark/src/main/java/org/apache/zeppelin/spark/SparkRInterpreter.java
@@ -0,0 +1,226 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.zeppelin.spark;
+
+import static org.apache.zeppelin.spark.ZeppelinRDisplay.render;
+
+import com.fasterxml.jackson.databind.JsonNode;
+import com.fasterxml.jackson.databind.ObjectMapper;
+import org.apache.spark.SparkRBackend;
+import org.apache.zeppelin.interpreter.*;
+import org.apache.zeppelin.scheduler.Scheduler;
+import org.apache.zeppelin.scheduler.SchedulerFactory;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Properties;
+
+/**
+ * R and SparkR interpreter with visualization support.
+ */
+public class SparkRInterpreter extends Interpreter {
+  private static final Logger logger = LoggerFactory.getLogger(SparkRInterpreter.class);
+
+  private static String renderOptions;
+  private ZeppelinR zeppelinR;
+
+  static {
+    Interpreter.register(
+      "r",
+      "spark",
+      SparkRInterpreter.class.getName(),
+      new InterpreterPropertyBuilder()
+          .add("zeppelin.R.cmd",
+              SparkInterpreter.getSystemDefault("ZEPPELIN_R_CMD", "zeppelin.R.cmd", "R"),
+              "R repl path")
+          .add("zeppelin.R.knitr",
+              SparkInterpreter.getSystemDefault("ZEPPELIN_R_KNITR", "zeppelin.R.knitr", "true"),
+              "whether use knitr or not")
+          .add("zeppelin.R.image.width",
+              SparkInterpreter.getSystemDefault("ZEPPELIN_R_IMAGE_WIDTH",
+                  "zeppelin.R.image.width", "100%"),
+              "")
+          .add("zeppelin.R.render.options",
+              SparkInterpreter.getSystemDefault("ZEPPELIN_R_RENDER_OPTIONS",
+                  "zeppelin.R.render.options",
+                  "out.format = 'html', comment = NA, "
+                      + "echo = FALSE, results = 'asis', message = F, warning = F"),
+              "")
+          .build());
+  }
+
+
+  public SparkRInterpreter(Properties property) {
+    super(property);
+  }
+
+  @Override
+  public void open() {
+    String rCmdPath = getProperty("zeppelin.R.cmd");
+    String sparkRLibPath;
+
+    if (System.getenv("SPARK_HOME") != null) {
+      sparkRLibPath = System.getenv("SPARK_HOME") + "/R/lib";
+    } else {
+      sparkRLibPath = System.getenv("ZEPPELIN_HOME") + "/interpreter/spark/R/lib";
+      // workaround to make sparkr work without SPARK_HOME
+      System.setProperty("spark.test.home", System.getenv("ZEPPELIN_HOME") + "/interpreter/spark");
+    }
+
+    synchronized (SparkRBackend.backend()) {
+      if (!SparkRBackend.isStarted()) {
+        SparkRBackend.init();
+        SparkRBackend.start();
+      }
+    }
+
+    int port = SparkRBackend.port();
+
+    SparkInterpreter sparkInterpreter = getSparkInterpreter();
+    ZeppelinRContext.setSparkContext(sparkInterpreter.getSparkContext());
+    ZeppelinRContext.setSqlContext(sparkInterpreter.getSQLContext());
+    ZeppelinRContext.setZepplinContext(sparkInterpreter.getZeppelinContext());
+
+    zeppelinR = new ZeppelinR(rCmdPath, sparkRLibPath, port);
+    try {
+      zeppelinR.open();
+    } catch (IOException e) {
+      logger.error("Exception while opening SparkRInterpreter", e);
+      throw new InterpreterException(e);
+    }
+
+    if (useKnitr()) {
+      zeppelinR.eval("library('knitr')");
+    }
+    renderOptions = getProperty("zeppelin.R.render.options");
+  }
+
+  @Override
+  public InterpreterResult interpret(String lines, InterpreterContext interpreterContext) {
+
+    String imageWidth = getProperty("zeppelin.R.image.width");
+
+    String[] sl = lines.split("\n");
+    if (sl[0].contains("{") && sl[0].contains("}")) {
+      String jsonConfig = sl[0].substring(sl[0].indexOf("{"), sl[0].indexOf("}") + 1);
+      ObjectMapper m = new ObjectMapper();
+      try {
+        JsonNode rootNode = m.readTree(jsonConfig);
+        JsonNode imageWidthNode = rootNode.path("imageWidth");
+        if (!imageWidthNode.isMissingNode()) imageWidth = imageWidthNode.textValue();
+      }
+      catch (Exception e) {
+        logger.warn("Can not parse json config: " + jsonConfig, e);
+      }
+      finally {
+        lines = lines.replace(jsonConfig, "");
+      }
+    }
+
+    try {
+      // render output with knitr
+      if (useKnitr()) {
+        zeppelinR.setInterpreterOutput(null);
+        zeppelinR.set(".zcmd", "\n```{r " + renderOptions + "}\n" + lines + "\n```");
+        zeppelinR.eval(".zres <- knit2html(text=.zcmd)");
+        String html = zeppelinR.getS0(".zres");
+
+        RDisplay rDisplay = render(html, imageWidth);
+
+        return new InterpreterResult(
+            rDisplay.code(),
+            rDisplay.type(),
+            rDisplay.content()
+        );
+      } else {
+        // alternatively, stream the output (without knitr)
+        zeppelinR.setInterpreterOutput(interpreterContext.out);
+        zeppelinR.eval(lines);
+        return new InterpreterResult(InterpreterResult.Code.SUCCESS, "");
+      }
+    } catch (Exception e) {
+      logger.error("Exception while connecting to R", e);
+      return new InterpreterResult(InterpreterResult.Code.ERROR, e.getMessage());
+    } finally {
+      try {
+      } catch (Exception e) {
+        // Do nothing...
+      }
+    }
+  }
+
+  @Override
+  public void close() {
+    zeppelinR.close();
+  }
+
+  @Override
+  public void cancel(InterpreterContext context) {}
+
+  @Override
+  public FormType getFormType() {
+    return FormType.NONE;
+  }
+
+  @Override
+  public int getProgress(InterpreterContext context) {
+    return 0;
+  }
+
+  @Override
+  public Scheduler getScheduler() {
+    return SchedulerFactory.singleton().createOrGetFIFOScheduler(
+            SparkRInterpreter.class.getName() + this.hashCode());
+  }
+
+  @Override
+  public List<String> completion(String buf, int cursor) {
+    return new ArrayList<String>();
+  }
+
+  private SparkInterpreter getSparkInterpreter() {
+    LazyOpenInterpreter lazy = null;
+    SparkInterpreter spark = null;
+    Interpreter p = getInterpreterInTheSameSessionByClassName(SparkInterpreter.class.getName());
+
+    while (p instanceof WrappedInterpreter) {
+      if (p instanceof LazyOpenInterpreter) {
+        lazy = (LazyOpenInterpreter) p;
+      }
+      p = ((WrappedInterpreter) p).getInnerInterpreter();
+    }
+    spark = (SparkInterpreter) p;
+
+    if (lazy != null) {
+      lazy.open();
+    }
+    return spark;
+  }
+
+  private boolean useKnitr() {
+    try {
+      return Boolean.parseBoolean(getProperty("zeppelin.R.knitr"));
+    } catch (Exception e) {
+      return false;
+    }
+  }
+
+}
diff --git a/spark/src/main/java/org/apache/zeppelin/spark/ZeppelinR.java b/spark/src/main/java/org/apache/zeppelin/spark/ZeppelinR.java
new file mode 100644
index 00000000000..8d92c968f50
--- /dev/null
+++ b/spark/src/main/java/org/apache/zeppelin/spark/ZeppelinR.java
@@ -0,0 +1,404 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.zeppelin.spark;
+
+import org.apache.commons.exec.*;
+import org.apache.commons.exec.environment.EnvironmentUtils;
+import org.apache.commons.io.IOUtils;
+import org.apache.zeppelin.interpreter.InterpreterException;
+import org.apache.zeppelin.interpreter.InterpreterOutput;
+import org.apache.zeppelin.interpreter.InterpreterOutputListener;
+import parquet.org.slf4j.Logger;
+import parquet.org.slf4j.LoggerFactory;
+
+import java.io.*;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * R repl interaction
+ */
+public class ZeppelinR implements ExecuteResultHandler {
+  Logger logger = LoggerFactory.getLogger(ZeppelinR.class);
+  private final String rCmdPath;
+  private DefaultExecutor executor;
+  private SparkOutputStream outputStream;
+  private PipedOutputStream input;
+  private final String scriptPath;
+  private final String libPath;
+  static Map<Integer, ZeppelinR> zeppelinR = Collections.synchronizedMap(
+      new HashMap<Integer, ZeppelinR>());
+
+  private InterpreterOutput initialOutput;
+  private final int port;
+  private boolean rScriptRunning;
+
+  /**
+   * To be notified R repl initialization
+   */
+  boolean rScriptInitialized = false;
+  Integer rScriptInitializeNotifier = new Integer(0);
+
+
+  /**
+   * Request to R repl
+   */
+  Request rRequestObject = null;
+  Integer rRequestNotifier = new Integer(0);
+
+  /**
+   * Request object
+   *
+   * type : "eval", "set", "get"
+   * stmt : statement to evaluate when type is "eval"
+   *        key when type is "set" or "get"
+   * value : value object when type is "put"
+   */
+  public static class Request {
+    String type;
+    String stmt;
+    Object value;
+
+    public Request(String type, String stmt, Object value) {
+      this.type = type;
+      this.stmt = stmt;
+      this.value = value;
+    }
+
+    public String getType() {
+      return type;
+    }
+
+    public String getStmt() {
+      return stmt;
+    }
+
+    public Object getValue() {
+      return value;
+    }
+  }
+
+  /**
+   * Response from R repl
+   */
+  Object rResponseValue = null;
+  boolean rResponseError = false;
+  Integer rResponseNotifier = new Integer(0);
+
+
+
+  /**
+   * Create ZeppelinR instance
+   * @param rCmdPath R repl commandline path
+   * @param libPath sparkr library path
+   */
+  public ZeppelinR(String rCmdPath, String libPath, int sparkRBackendPort) {
+    this.rCmdPath = rCmdPath;
+    this.libPath = libPath;
+    this.port = sparkRBackendPort;
+    scriptPath = System.getProperty("java.io.tmpdir") + "/zeppelin_sparkr.R";
+
+  }
+
+  /**
+   * Start R repl
+   * @throws IOException
+   */
+  public void open() throws IOException {
+    createRScript();
+
+    zeppelinR.put(hashCode(), this);
+
+    CommandLine cmd = CommandLine.parse(rCmdPath);
+    cmd.addArgument("--no-save");
+    cmd.addArgument("--no-restore");
+    cmd.addArgument("-f");
+    cmd.addArgument(scriptPath);
+    cmd.addArgument("--args");
+    cmd.addArgument(Integer.toString(hashCode()));
+    cmd.addArgument(Integer.toString(port));
+    cmd.addArgument(libPath);
+
+    executor = new DefaultExecutor();
+    outputStream = new SparkOutputStream();
+
+    input = new PipedOutputStream();
+    PipedInputStream in = new PipedInputStream(input);
+
+    PumpStreamHandler streamHandler = new PumpStreamHandler(outputStream, outputStream, in);
+    executor.setWatchdog(new ExecuteWatchdog(ExecuteWatchdog.INFINITE_TIMEOUT));
+    executor.setStreamHandler(streamHandler);
+    Map env = EnvironmentUtils.getProcEnvironment();
+
+
+    initialOutput = new InterpreterOutput(new InterpreterOutputListener() {
+      @Override
+      public void onAppend(InterpreterOutput out, byte[] line) {
+        logger.debug(new String(line));
+      }
+
+      @Override
+      public void onUpdate(InterpreterOutput out, byte[] output) {
+      }
+    });
+    outputStream.setInterpreterOutput(initialOutput);
+    executor.execute(cmd, env, this);
+    rScriptRunning = true;
+
+    // flush output
+    eval("cat('')");
+  }
+
+  /**
+   * Evaluate expression
+   * @param expr
+   * @return
+   */
+  public Object eval(String expr) {
+    synchronized (this) {
+      rRequestObject = new Request("eval", expr, null);
+      return request();
+    }
+  }
+
+  /**
+   * assign value to key
+   * @param key
+   * @param value
+   */
+  public void set(String key, Object value) {
+    synchronized (this) {
+      rRequestObject = new Request("set", key, value);
+      request();
+    }
+  }
+
+  /**
+   * get value of key
+   * @param key
+   * @return
+   */
+  public Object get(String key) {
+    synchronized (this) {
+      rRequestObject = new Request("get", key, null);
+      return request();
+    }
+  }
+
+  /**
+   * get value of key, as a string
+   * @param key
+   * @return
+   */
+  public String getS0(String key) {
+    synchronized (this) {
+      rRequestObject = new Request("getS", key, null);
+      return (String) request();
+    }
+  }
+
+
+  /**
+   * Send request to r repl and return response
+   * @return responseValue
+   */
+  private Object request() throws RuntimeException {
+    if (!rScriptRunning) {
+      throw new RuntimeException("r repl is not running");
+    }
+
+    // wait for rscript initialized
+    if (!rScriptInitialized) {
+      waitForRScriptInitialized();
+    }
+
+    rResponseValue = null;
+
+    synchronized (rRequestNotifier) {
+      rRequestNotifier.notify();
+    }
+
+    Object respValue = null;
+    synchronized (rResponseNotifier) {
+      while (rResponseValue == null && rScriptRunning) {
+        try {
+          rResponseNotifier.wait(1000);
+        } catch (InterruptedException e) {
+          logger.error(e.getMessage(), e);
+        }
+      }
+      respValue = rResponseValue;
+      rResponseValue = null;
+    }
+
+    if (rResponseError) {
+      throw new RuntimeException(respValue.toString());
+    } else {
+      return respValue;
+    }
+  }
+
+
+  /**
+   * Wait until src/main/resources/R/zeppelin_sparkr.R is initialized
+   * and call onScriptInitialized()
+   *
+   * @throws InterpreterException
+   */
+  private void waitForRScriptInitialized() throws InterpreterException {
+    synchronized (rScriptInitializeNotifier) {
+      long startTime = System.nanoTime();
+      while (rScriptInitialized == false &&
+          rScriptRunning &&
+          System.nanoTime() - startTime < 10L * 1000 * 1000000) {
+        try {
+          rScriptInitializeNotifier.wait(1000);
+        } catch (InterruptedException e) {
+          logger.error(e.getMessage(), e);
+        }
+      }
+    }
+
+    String errorMessage = "";
+    try {
+      initialOutput.flush();
+      errorMessage = new String(initialOutput.toByteArray());
+    } catch (IOException e) {
+      e.printStackTrace();
+    }
+
+
+    if (rScriptInitialized == false) {
+      throw new InterpreterException("sparkr is not responding " + errorMessage);
+    }
+  }
+
+
+
+  /**
+   * invoked by src/main/resources/R/zeppelin_sparkr.R
+   * @return
+   */
+  public Request getRequest() {
+    synchronized (rRequestNotifier) {
+      while (rRequestObject == null) {
+        try {
+          rRequestNotifier.wait(1000);
+        } catch (InterruptedException e) {
+          logger.error(e.getMessage(), e);
+        }
+      }
+
+      Request req = rRequestObject;
+      rRequestObject = null;
+      return req;
+    }
+  }
+
+  /**
+   * invoked by src/main/resources/R/zeppelin_sparkr.R
+   * @param value
+   * @param error
+   */
+  public void setResponse(Object value, boolean error) {
+    synchronized (rResponseNotifier) {
+      rResponseValue = value;
+      rResponseError = error;
+      rResponseNotifier.notify();
+    }
+  }
+
+  /**
+   * invoked by src/main/resources/R/zeppelin_sparkr.R
+   */
+  public void onScriptInitialized() {
+    synchronized (rScriptInitializeNotifier) {
+      rScriptInitialized = true;
+      rScriptInitializeNotifier.notifyAll();
+    }
+  }
+
+
+  /**
+   * Create R script in tmp dir
+   */
+  private void createRScript() {
+    ClassLoader classLoader = getClass().getClassLoader();
+    File out = new File(scriptPath);
+
+    if (out.exists() && out.isDirectory()) {
+      throw new InterpreterException("Can't create r script " + out.getAbsolutePath());
+    }
+
+    try {
+      FileOutputStream outStream = new FileOutputStream(out);
+      IOUtils.copy(
+          classLoader.getResourceAsStream("R/zeppelin_sparkr.R"),
+          outStream);
+      outStream.close();
+    } catch (IOException e) {
+      throw new InterpreterException(e);
+    }
+
+    logger.info("File {} created", scriptPath);
+  }
+
+  /**
+   * Terminate this R repl
+   */
+  public void close() {
+    executor.getWatchdog().destroyProcess();
+    new File(scriptPath).delete();
+    zeppelinR.remove(hashCode());
+  }
+
+  /**
+   * Get instance
+   * This method will be invoded from zeppelin_sparkr.R
+   * @param hashcode
+   * @return
+   */
+  public static ZeppelinR getZeppelinR(int hashcode) {
+    return zeppelinR.get(hashcode);
+  }
+
+
+  /**
+   * Pass InterpreterOutput to capture the repl output
+   * @param out
+   */
+  public void setInterpreterOutput(InterpreterOutput out) {
+    outputStream.setInterpreterOutput(out);
+  }
+
+
+
+  @Override
+  public void onProcessComplete(int i) {
+    logger.info("process complete {}", i);
+    rScriptRunning = false;
+  }
+
+  @Override
+  public void onProcessFailed(ExecuteException e) {
+    logger.error(e.getMessage(), e);
+    rScriptRunning = false;
+  }
+
+
+}
diff --git a/spark/src/main/java/org/apache/zeppelin/spark/ZeppelinRContext.java b/spark/src/main/java/org/apache/zeppelin/spark/ZeppelinRContext.java
new file mode 100644
index 00000000000..82c320d7f21
--- /dev/null
+++ b/spark/src/main/java/org/apache/zeppelin/spark/ZeppelinRContext.java
@@ -0,0 +1,55 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.zeppelin.spark;
+
+import org.apache.spark.SparkContext;
+import org.apache.spark.sql.SQLContext;
+
+/**
+ * Contains the Spark and Zeppelin Contexts made available to SparkR.
+ */
+public class ZeppelinRContext {
+  private static SparkContext sparkContext;
+  private static SQLContext sqlContext;
+  private static ZeppelinContext zeppelinContext;
+
+  public static void setSparkContext(SparkContext sparkContext) {
+    ZeppelinRContext.sparkContext = sparkContext;
+  }
+
+  public static void setZepplinContext(ZeppelinContext zeppelinContext) {
+    ZeppelinRContext.zeppelinContext = zeppelinContext;
+  }
+
+  public static void setSqlContext(SQLContext sqlContext) {
+    ZeppelinRContext.sqlContext = sqlContext;
+  }
+
+  public static SparkContext getSparkContext() {
+    return sparkContext;
+  }
+
+  public static SQLContext getSqlContext() {
+    return sqlContext;
+  }
+
+  public static ZeppelinContext getZeppelinContext() {
+    return zeppelinContext;
+  }
+
+}
diff --git a/spark/src/main/resources/R/zeppelin_sparkr.R b/spark/src/main/resources/R/zeppelin_sparkr.R
new file mode 100644
index 00000000000..fe2a16b973f
--- /dev/null
+++ b/spark/src/main/resources/R/zeppelin_sparkr.R
@@ -0,0 +1,99 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+args <- commandArgs(trailingOnly = TRUE)
+
+hashCode <- as.integer(args[1])
+port <- as.integer(args[2])
+libPath <- args[3]
+rm(args)
+
+print(paste("Port ", toString(port)))
+print(paste("LibPath ", libPath))
+
+.libPaths(c(file.path(libPath), .libPaths()))
+library(SparkR)
+
+
+SparkR:::connectBackend("localhost", port)
+
+# scStartTime is needed by R/pkg/R/sparkR.R
+assign(".scStartTime", as.integer(Sys.time()), envir = SparkR:::.sparkREnv)
+
+# getZeppelinR
+.zeppelinR = SparkR:::callJStatic("org.apache.zeppelin.spark.ZeppelinR", "getZeppelinR", hashCode)
+
+# setup spark env
+assign(".sc", SparkR:::callJStatic("org.apache.zeppelin.spark.ZeppelinRContext", "getSparkContext"), envir = SparkR:::.sparkREnv)
+assign("sc", get(".sc", envir = SparkR:::.sparkREnv), envir=.GlobalEnv)
+assign(".sqlc", SparkR:::callJStatic("org.apache.zeppelin.spark.ZeppelinRContext", "getSqlContext"), envir = SparkR:::.sparkREnv)
+assign("sqlContext", get(".sqlc", envir = SparkR:::.sparkREnv), envir = .GlobalEnv)
+assign(".zeppelinContext", SparkR:::callJStatic("org.apache.zeppelin.spark.ZeppelinRContext", "getZeppelinContext"), envir = .GlobalEnv)
+
+z.put <- function(name, object) {
+  SparkR:::callJMethod(.zeppelinContext, "put", name, object)
+}
+z.get <- function(name) {
+  SparkR:::callJMethod(.zeppelinContext, "get", name)
+}
+z.input <- function(name, value) {
+  SparkR:::callJMethod(.zeppelinContext, "input", name, value)
+}
+
+# notify script is initialized
+SparkR:::callJMethod(.zeppelinR, "onScriptInitialized")
+
+while (TRUE) {
+  req <- SparkR:::callJMethod(.zeppelinR, "getRequest")
+  type <-  SparkR:::callJMethod(req, "getType")
+  stmt <- SparkR:::callJMethod(req, "getStmt")
+  value <- SparkR:::callJMethod(req, "getValue")
+  
+  if (type == "eval") {
+    tryCatch({
+      ret <- eval(parse(text=stmt))
+      SparkR:::callJMethod(.zeppelinR, "setResponse", "", FALSE)
+    }, error = function(e) {
+      SparkR:::callJMethod(.zeppelinR, "setResponse", toString(e), TRUE)
+    })    
+  } else if (type == "set") {
+    tryCatch({
+      ret <- assign(stmt, value)
+      SparkR:::callJMethod(.zeppelinR, "setResponse", "", FALSE)
+    }, error = function(e) {
+      SparkR:::callJMethod(.zeppelinR, "setResponse", toString(e), TRUE)
+    })
+  } else if (type == "get") {
+    tryCatch({      
+      ret <- eval(parse(text=stmt))
+      SparkR:::callJMethod(.zeppelinR, "setResponse", ret, FALSE)
+    }, error = function(e) {
+      SparkR:::callJMethod(.zeppelinR, "setResponse", toString(e), TRUE)
+    })
+  } else if (type == "getS") {
+    tryCatch({
+      ret <- eval(parse(text=stmt))
+      SparkR:::callJMethod(.zeppelinR, "setResponse", toString(ret), FALSE)
+    }, error = function(e) {
+      SparkR:::callJMethod(.zeppelinR, "setResponse", toString(e), TRUE)
+    })
+  } else {
+    # unsupported type
+    SparkR:::callJMethod(.zeppelinR, "setResponse", paste("Unsupported type ", type), TRUE)
+  }
+}
diff --git a/spark/src/main/scala/org/apache/spark/SparkRBackend.scala b/spark/src/main/scala/org/apache/spark/SparkRBackend.scala
new file mode 100644
index 00000000000..05f1ac0e3e2
--- /dev/null
+++ b/spark/src/main/scala/org/apache/spark/SparkRBackend.scala
@@ -0,0 +1,54 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark
+
+import org.apache.spark.api.r.RBackend
+
+object SparkRBackend {
+  val backend : RBackend = new RBackend()
+  private var started = false;
+  private var portNumber = 0;
+
+  val backendThread : Thread = new Thread("SparkRBackend") {
+    override def run() {
+      backend.run()
+    }
+  }
+
+  def init() : Int = {
+    portNumber = backend.init()
+    portNumber
+  }
+
+  def start() : Unit = {
+    backendThread.start()
+    started = true
+  }
+
+  def close() : Unit = {
+    backend.close()
+    backendThread.join()
+  }
+
+  def isStarted() : Boolean = {
+    started
+  }
+
+  def port(): Int = {
+    return portNumber
+  }
+}
diff --git a/spark/src/main/scala/org/apache/zeppelin/spark/ZeppelinRDisplay.scala b/spark/src/main/scala/org/apache/zeppelin/spark/ZeppelinRDisplay.scala
new file mode 100644
index 00000000000..8607226ef2e
--- /dev/null
+++ b/spark/src/main/scala/org/apache/zeppelin/spark/ZeppelinRDisplay.scala
@@ -0,0 +1,119 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.zeppelin.spark
+
+import org.apache.zeppelin.interpreter.InterpreterResult.Code
+import org.apache.zeppelin.interpreter.InterpreterResult.Code.{SUCCESS, ERROR}
+import org.apache.zeppelin.interpreter.InterpreterResult.Type
+import org.apache.zeppelin.interpreter.InterpreterResult.Type.{TEXT, HTML, TABLE, IMG}
+import org.jsoup.Jsoup
+import org.jsoup.nodes.Element
+import org.jsoup.nodes.Document
+
+import scala.collection.JavaConversions._
+
+import scala.util.matching.Regex
+
+case class RDisplay(content: String, `type`: Type, code: Code)
+
+object ZeppelinRDisplay {
+
+  val pattern = new Regex("""^ *\[\d*\] """)
+
+  def render(html: String, imageWidth: String): RDisplay = {
+
+    val document = Jsoup.parse(html)
+    document.outputSettings().prettyPrint(false)
+
+    val body = document.body()
+
+    if (body.getElementsByTag("p").isEmpty) return RDisplay(body.html(), HTML, SUCCESS)
+
+    val bodyHtml = body.html()
+
+    if (! bodyHtml.contains("<img")
+      &&  ! bodyHtml.contains("<script")
+      && ! bodyHtml.contains("%html ")
+      && ! bodyHtml.contains("%table ")
+      && ! bodyHtml.contains("%img ")
+    ) {
+      return textDisplay(body)
+    }
+
+    if (bodyHtml.contains("%table")) {
+      return tableDisplay(body)
+    }
+
+    if (bodyHtml.contains("%img")) {
+      return imgDisplay(body)
+    }
+
+    return htmlDisplay(body, imageWidth)
+
+  }
+
+  private def textDisplay(body: Element): RDisplay = {
+    RDisplay(body.getElementsByTag("p").get(0).html(), TEXT, SUCCESS)
+  }
+
+  private def tableDisplay(body: Element): RDisplay = {
+    val p = body.getElementsByTag("p").get(0).html.replace("“%table " , "").replace("”", "")
+    val r = (pattern findFirstIn p).getOrElse("")
+    val table = p.replace(r, "").replace("\\t", "\t").replace("\\n", "\n")
+    RDisplay(table, TABLE, SUCCESS)
+  }
+
+  private def imgDisplay(body: Element): RDisplay = {
+    val p = body.getElementsByTag("p").get(0).html.replace("“%img " , "").replace("”", "")
+    val r = (pattern findFirstIn p).getOrElse("")
+    val img = p.replace(r, "")
+    RDisplay(img, IMG, SUCCESS)
+  }
+
+  private def htmlDisplay(body: Element, imageWidth: String): RDisplay = {
+
+    var div = new String()
+
+    for (element <- body.children) {
+
+      val eHtml = element.html()
+      var eOuterHtml = element.outerHtml()
+
+      eOuterHtml = eOuterHtml.replace("“%html " , "").replace("”", "")
+
+      val r = (pattern findFirstIn eHtml).getOrElse("")
+
+      div = div + eOuterHtml.replace(r, "")
+
+    }
+
+    val content =  div
+      .replaceAll("src=\"//", "src=\"http://")
+      .replaceAll("href=\"//", "href=\"http://")
+
+    body.html(content)
+
+    for (image <- body.getElementsByTag("img")) {
+      image.attr("width", imageWidth)
+    }
+
+    RDisplay(body.html, HTML, SUCCESS)
+
+  }
+
+}
diff --git a/zeppelin-server/src/test/java/org/apache/zeppelin/rest/AbstractTestRestApi.java b/zeppelin-server/src/test/java/org/apache/zeppelin/rest/AbstractTestRestApi.java
index 81f161af4f6..4c5cc5e8c89 100644
--- a/zeppelin-server/src/test/java/org/apache/zeppelin/rest/AbstractTestRestApi.java
+++ b/zeppelin-server/src/test/java/org/apache/zeppelin/rest/AbstractTestRestApi.java
@@ -60,6 +60,7 @@ public abstract class AbstractTestRestApi {
   static final String url = getUrlToTest();
   protected static final boolean wasRunning = checkIfServerIsRunning();
   static boolean pySpark = false;
+  static boolean sparkR = false;
 
   private String getUrl(String path) {
     String url;
@@ -132,7 +133,7 @@ protected static void startUp() throws Exception {
         // set spark home for pyspark
         sparkIntpSetting.getProperties().setProperty("spark.home", getSparkHome());
         pySpark = true;
-
+        sparkR = true;
         ZeppelinServer.notebook.getInterpreterFactory().restart(sparkIntpSetting.id());
       } else {
         // assume first one is spark
@@ -148,6 +149,7 @@ protected static void startUp() throws Exception {
           // set spark home for pyspark
           sparkIntpSetting.getProperties().setProperty("spark.home", sparkHome);
           pySpark = true;
+          sparkR = true;
         }
 
         ZeppelinServer.notebook.getInterpreterFactory().restart(sparkIntpSetting.id());
@@ -174,6 +176,10 @@ boolean isPyspark() {
     return pySpark;
   }
 
+  boolean isSparkR() {
+    return sparkR;
+  }
+
   private static String getSparkHomeRecursively(File dir) {
     if (dir == null) return null;
     File files []  = dir.listFiles();
diff --git a/zeppelin-server/src/test/java/org/apache/zeppelin/rest/ZeppelinSparkClusterTest.java b/zeppelin-server/src/test/java/org/apache/zeppelin/rest/ZeppelinSparkClusterTest.java
index 5a489fa2957..e9037b1f8b6 100644
--- a/zeppelin-server/src/test/java/org/apache/zeppelin/rest/ZeppelinSparkClusterTest.java
+++ b/zeppelin-server/src/test/java/org/apache/zeppelin/rest/ZeppelinSparkClusterTest.java
@@ -83,6 +83,30 @@ public void basicRDDTransformationAndActionTest() throws IOException {
     ZeppelinServer.notebook.removeNote(note.id());
   }
 
+  @Test
+  public void sparkRTest() throws IOException {
+    // create new note
+    Note note = ZeppelinServer.notebook.createNote();
+    int sparkVersion = getSparkVersionNumber(note);
+
+    if (isSparkR() && sparkVersion >= 14) {   // sparkr supported from 1.4.0
+      // run markdown paragraph, again
+      Paragraph p = note.addParagraph();
+      Map config = p.getConfig();
+      config.put("enabled", true);
+      p.setConfig(config);
+      p.setText("%r localDF <- data.frame(name=c(\"a\", \"b\", \"c\"), age=c(19, 23, 18))\n" +
+          "df <- createDataFrame(sqlContext, localDF)\n" +
+          "count(df)"
+      );
+      note.run(p.getId());
+      waitForFinish(p);
+      assertEquals(Status.FINISHED, p.getStatus());
+      assertEquals("[1] 3", p.getResult().message());
+    }
+    ZeppelinServer.notebook.removeNote(note.id());
+  }
+
   @Test
   public void pySparkTest() throws IOException {
     // create new note
diff --git a/zeppelin-zengine/pom.xml b/zeppelin-zengine/pom.xml
index 10a69a443f1..6c0275b344a 100644
--- a/zeppelin-zengine/pom.xml
+++ b/zeppelin-zengine/pom.xml
@@ -52,11 +52,11 @@
       <version>${project.version}</version>
     </dependency>
 
-	<dependency>
+    <dependency>
       <groupId>com.amazonaws</groupId>
       <artifactId>aws-java-sdk-s3</artifactId>
       <version>1.10.62</version>
-	</dependency>
+    </dependency>
 
     <dependency>
       <groupId>com.microsoft.azure</groupId>
diff --git a/zeppelin-zengine/src/main/java/org/apache/zeppelin/conf/ZeppelinConfiguration.java b/zeppelin-zengine/src/main/java/org/apache/zeppelin/conf/ZeppelinConfiguration.java
index b33391a7d02..cbf80a4d7d8 100755
--- a/zeppelin-zengine/src/main/java/org/apache/zeppelin/conf/ZeppelinConfiguration.java
+++ b/zeppelin-zengine/src/main/java/org/apache/zeppelin/conf/ZeppelinConfiguration.java
@@ -451,6 +451,7 @@ public static enum ConfVars {
     ZEPPELIN_WAR_TEMPDIR("zeppelin.war.tempdir", "webapps"),
     ZEPPELIN_INTERPRETERS("zeppelin.interpreters", "org.apache.zeppelin.spark.SparkInterpreter,"
         + "org.apache.zeppelin.spark.PySparkInterpreter,"
+        + "org.apache.zeppelin.spark.SparkRInterpreter,"
         + "org.apache.zeppelin.spark.SparkSqlInterpreter,"
         + "org.apache.zeppelin.spark.DepInterpreter,"
         + "org.apache.zeppelin.markdown.Markdown,"
diff --git a/zeppelin-zengine/src/main/java/org/apache/zeppelin/interpreter/InterpreterFactory.java b/zeppelin-zengine/src/main/java/org/apache/zeppelin/interpreter/InterpreterFactory.java
index 7df7bb39e50..269b54a1fce 100644
--- a/zeppelin-zengine/src/main/java/org/apache/zeppelin/interpreter/InterpreterFactory.java
+++ b/zeppelin-zengine/src/main/java/org/apache/zeppelin/interpreter/InterpreterFactory.java
@@ -379,10 +379,10 @@ public InterpreterGroup add(String name, String groupName,
       List<InterpreterSetting.InterpreterInfo> interpreterInfos =
           new LinkedList<InterpreterSetting.InterpreterInfo>();
 
-      for (RegisteredInterpreter registeredInterpreter :
-          Interpreter.registeredInterpreters.values()) {
-        if (registeredInterpreter.getGroup().equals(groupName)) {
-          for (String className : interpreterClassList) {
+      for (String className : interpreterClassList) {
+        for (RegisteredInterpreter registeredInterpreter :
+            Interpreter.registeredInterpreters.values()) {
+          if (registeredInterpreter.getGroup().equals(groupName)) {
             if (registeredInterpreter.getClassName().equals(className)) {
               interpreterInfos.add(
                   new InterpreterSetting.InterpreterInfo(