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Posted to commits@spark.apache.org by yu...@apache.org on 2022/09/23 08:05:36 UTC
[spark] branch SPARK-40322 created (now 455bbdad931)
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at 455bbdad931 Fix docs
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new 455bbdad931 Fix docs
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[spark] 01/01: Fix docs
Posted by yu...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
yumwang pushed a commit to branch SPARK-40322
in repository https://gitbox.apache.org/repos/asf/spark.git
commit 455bbdad931154c4ab1f8fd5db0abd94d935f340
Author: Yuming Wang <yu...@ebay.com>
AuthorDate: Fri Sep 23 16:01:58 2022 +0800
Fix docs
---
docs/ml-classification-regression.md | 34 +-
docs/ml-clustering.md | 10 +-
docs/ml-collaborative-filtering.md | 2 +-
docs/ml-frequent-pattern-mining.md | 4 +-
docs/rdd-programming-guide.md | 4 +-
docs/sparkr.md | 48 +-
docs/sql-getting-started.md | 8 +-
docs/structured-streaming-programming-guide.md | 4 +-
.../source/getting_started/quickstart_ps.ipynb | 11809 +------------------
9 files changed, 61 insertions(+), 11862 deletions(-)
diff --git a/docs/ml-classification-regression.md b/docs/ml-classification-regression.md
index bad74cbcf6c..c3e1b6b4390 100644
--- a/docs/ml-classification-regression.md
+++ b/docs/ml-classification-regression.md
@@ -92,7 +92,7 @@ More details on parameters can be found in the [Python API documentation](api/py
<div data-lang="r" markdown="1">
-More details on parameters can be found in the [R API documentation](api/R/spark.logit.html).
+More details on parameters can be found in the [R API documentation](api/R/reference/spark.logit.html).
{% include_example binomial r/ml/logit.R %}
</div>
@@ -195,7 +195,7 @@ training summary for evaluating the model.
<div data-lang="r" markdown="1">
-More details on parameters can be found in the [R API documentation](api/R/spark.logit.html).
+More details on parameters can be found in the [R API documentation](api/R/reference/spark.logit.html).
{% include_example multinomial r/ml/logit.R %}
</div>
@@ -240,7 +240,7 @@ More details on parameters can be found in the [Python API documentation](api/py
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.decisionTree.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.decisionTree.html) for more details.
{% include_example classification r/ml/decisionTree.R %}
@@ -282,7 +282,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.classificatio
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.randomForest.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.randomForest.html) for more details.
{% include_example classification r/ml/randomForest.R %}
</div>
@@ -323,7 +323,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.classificatio
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.gbt.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.gbt.html) for more details.
{% include_example classification r/ml/gbt.R %}
</div>
@@ -379,7 +379,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.classificatio
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.mlp.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.mlp.html) for more details.
{% include_example r/ml/mlp.R %}
</div>
@@ -424,7 +424,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.classificatio
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.svmLinear.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.svmLinear.html) for more details.
{% include_example r/ml/svmLinear.R %}
</div>
@@ -522,7 +522,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.classificatio
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.naiveBayes.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.naiveBayes.html) for more details.
{% include_example r/ml/naiveBayes.R %}
</div>
@@ -565,7 +565,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.classificatio
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.fmClassifier.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.fmClassifier.html) for more details.
Note: At the moment SparkR doesn't support feature scaling.
@@ -616,7 +616,7 @@ More details on parameters can be found in the [Python API documentation](api/py
<div data-lang="r" markdown="1">
-More details on parameters can be found in the [R API documentation](api/R/spark.lm.html).
+More details on parameters can be found in the [R API documentation](api/R/reference/spark.lm.html).
{% include_example r/ml/lm_with_elastic_net.R %}
</div>
@@ -763,7 +763,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.regression.Ge
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.glm.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.glm.html) for more details.
{% include_example r/ml/glm.R %}
</div>
@@ -805,7 +805,7 @@ More details on parameters can be found in the [Python API documentation](api/py
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.decisionTree.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.decisionTree.html) for more details.
{% include_example regression r/ml/decisionTree.R %}
</div>
@@ -847,7 +847,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.regression.Ra
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.randomForest.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.randomForest.html) for more details.
{% include_example regression r/ml/randomForest.R %}
</div>
@@ -888,7 +888,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.regression.GB
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.gbt.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.gbt.html) for more details.
{% include_example regression r/ml/gbt.R %}
</div>
@@ -982,7 +982,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.regression.AF
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.survreg.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.survreg.html) for more details.
{% include_example r/ml/survreg.R %}
</div>
@@ -1060,7 +1060,7 @@ Refer to the [`IsotonicRegression` Python docs](api/python/reference/api/pyspark
<div data-lang="r" markdown="1">
-Refer to the [`IsotonicRegression` R API docs](api/R/spark.isoreg.html) for more details on the API.
+Refer to the [`IsotonicRegression` R API docs](api/R/reference/spark.isoreg.html) for more details on the API.
{% include_example r/ml/isoreg.R %}
</div>
@@ -1103,7 +1103,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.regression.FM
<div data-lang="r" markdown="1">
-Refer to the [R API documentation](api/R/spark.fmRegressor.html) for more details.
+Refer to the [R API documentation](api/R/reference/spark.fmRegressor.html) for more details.
Note: At the moment SparkR doesn't support feature scaling.
diff --git a/docs/ml-clustering.md b/docs/ml-clustering.md
index f478776196d..1d15f61a29d 100644
--- a/docs/ml-clustering.md
+++ b/docs/ml-clustering.md
@@ -104,7 +104,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.clustering.KM
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.kmeans.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.kmeans.html) for more details.
{% include_example r/ml/kmeans.R %}
</div>
@@ -144,7 +144,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.clustering.LD
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.lda.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.lda.html) for more details.
{% include_example r/ml/lda.R %}
</div>
@@ -185,7 +185,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.clustering.Bi
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.bisectingKmeans.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.bisectingKmeans.html) for more details.
{% include_example r/ml/bisectingKmeans.R %}
</div>
@@ -274,7 +274,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.clustering.Ga
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.gaussianMixture.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.gaussianMixture.html) for more details.
{% include_example r/ml/gaussianMixture.R %}
</div>
@@ -321,7 +321,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.clustering.Po
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.powerIterationClustering.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.powerIterationClustering.html) for more details.
{% include_example r/ml/powerIterationClustering.R %}
</div>
diff --git a/docs/ml-collaborative-filtering.md b/docs/ml-collaborative-filtering.md
index ddc90406648..8b6d2a1d14c 100644
--- a/docs/ml-collaborative-filtering.md
+++ b/docs/ml-collaborative-filtering.md
@@ -195,7 +195,7 @@ als = ALS(maxIter=5, regParam=0.01, implicitPrefs=True,
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.als.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.als.html) for more details.
{% include_example r/ml/als.R %}
</div>
diff --git a/docs/ml-frequent-pattern-mining.md b/docs/ml-frequent-pattern-mining.md
index 6e6ae410cb7..58cd29fd8f6 100644
--- a/docs/ml-frequent-pattern-mining.md
+++ b/docs/ml-frequent-pattern-mining.md
@@ -102,7 +102,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.fpm.FPGrowth.
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.fpGrowth.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.fpGrowth.html) for more details.
{% include_example r/ml/fpm.R %}
</div>
@@ -155,7 +155,7 @@ Refer to the [Python API docs](api/python/reference/api/pyspark.ml.fpm.PrefixSpa
<div data-lang="r" markdown="1">
-Refer to the [R API docs](api/R/spark.prefixSpan.html) for more details.
+Refer to the [R API docs](api/R/reference/spark.prefixSpan.html) for more details.
{% include_example r/ml/prefixSpan.R %}
</div>
diff --git a/docs/rdd-programming-guide.md b/docs/rdd-programming-guide.md
index 275c5ccf433..09af00aa9db 100644
--- a/docs/rdd-programming-guide.md
+++ b/docs/rdd-programming-guide.md
@@ -951,7 +951,7 @@ RDD API doc
([Scala](api/scala/org/apache/spark/rdd/RDD.html),
[Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html),
[Python](api/python/reference/api/pyspark.RDD.html#pyspark.RDD),
- [R](api/R/index.html))
+ [R](api/R/reference/index.html))
and pair RDD functions doc
([Scala](api/scala/org/apache/spark/rdd/PairRDDFunctions.html),
[Java](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html))
@@ -1065,7 +1065,7 @@ RDD API doc
([Scala](api/scala/org/apache/spark/rdd/RDD.html),
[Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html),
[Python](api/python/reference/api/pyspark.RDD.html#pyspark.RDD),
- [R](api/R/index.html))
+ [R](api/R/reference/index.html))
and pair RDD functions doc
([Scala](api/scala/org/apache/spark/rdd/PairRDDFunctions.html),
diff --git a/docs/sparkr.md b/docs/sparkr.md
index 002da5a56fa..2e55c7a20c0 100644
--- a/docs/sparkr.md
+++ b/docs/sparkr.md
@@ -175,7 +175,7 @@ people <- read.json(c("./examples/src/main/resources/people.json", "./examples/s
{% endhighlight %}
</div>
-The data sources API natively supports CSV formatted input files. For more information please refer to SparkR [read.df](api/R/read.df.html) API documentation.
+The data sources API natively supports CSV formatted input files. For more information please refer to SparkR [read.df](api/R/reference/read.df.html) API documentation.
<div data-lang="r" markdown="1">
{% highlight r %}
@@ -536,49 +536,49 @@ SparkR supports the following machine learning algorithms currently:
#### Classification
-* [`spark.logit`](api/R/spark.logit.html): [`Logistic Regression`](ml-classification-regression.html#logistic-regression)
-* [`spark.mlp`](api/R/spark.mlp.html): [`Multilayer Perceptron (MLP)`](ml-classification-regression.html#multilayer-perceptron-classifier)
-* [`spark.naiveBayes`](api/R/spark.naiveBayes.html): [`Naive Bayes`](ml-classification-regression.html#naive-bayes)
-* [`spark.svmLinear`](api/R/spark.svmLinear.html): [`Linear Support Vector Machine`](ml-classification-regression.html#linear-support-vector-machine)
-* [`spark.fmClassifier`](api/R/fmClassifier.html): [`Factorization Machines classifier`](ml-classification-regression.html#factorization-machines-classifier)
+* [`spark.logit`](api/R/reference/spark.logit.html): [`Logistic Regression`](ml-classification-regression.html#logistic-regression)
+* [`spark.mlp`](api/R/reference/spark.mlp.html): [`Multilayer Perceptron (MLP)`](ml-classification-regression.html#multilayer-perceptron-classifier)
+* [`spark.naiveBayes`](api/R/reference/spark.naiveBayes.html): [`Naive Bayes`](ml-classification-regression.html#naive-bayes)
+* [`spark.svmLinear`](api/R/reference/spark.svmLinear.html): [`Linear Support Vector Machine`](ml-classification-regression.html#linear-support-vector-machine)
+* [`spark.fmClassifier`](api/R/reference/fmClassifier.html): [`Factorization Machines classifier`](ml-classification-regression.html#factorization-machines-classifier)
#### Regression
-* [`spark.survreg`](api/R/spark.survreg.html): [`Accelerated Failure Time (AFT) Survival Model`](ml-classification-regression.html#survival-regression)
-* [`spark.glm`](api/R/spark.glm.html) or [`glm`](api/R/glm.html): [`Generalized Linear Model (GLM)`](ml-classification-regression.html#generalized-linear-regression)
-* [`spark.isoreg`](api/R/spark.isoreg.html): [`Isotonic Regression`](ml-classification-regression.html#isotonic-regression)
-* [`spark.lm`](api/R/spark.lm.html): [`Linear Regression`](ml-classification-regression.html#linear-regression)
-* [`spark.fmRegressor`](api/R/spark.fmRegressor.html): [`Factorization Machines regressor`](ml-classification-regression.html#factorization-machines-regressor)
+* [`spark.survreg`](api/R/reference/spark.survreg.html): [`Accelerated Failure Time (AFT) Survival Model`](ml-classification-regression.html#survival-regression)
+* [`spark.glm`](api/R/reference/spark.glm.html) or [`glm`](api/R/reference/glm.html): [`Generalized Linear Model (GLM)`](ml-classification-regression.html#generalized-linear-regression)
+* [`spark.isoreg`](api/R/reference/spark.isoreg.html): [`Isotonic Regression`](ml-classification-regression.html#isotonic-regression)
+* [`spark.lm`](api/R/reference/spark.lm.html): [`Linear Regression`](ml-classification-regression.html#linear-regression)
+* [`spark.fmRegressor`](api/R/reference/spark.fmRegressor.html): [`Factorization Machines regressor`](ml-classification-regression.html#factorization-machines-regressor)
#### Tree
-* [`spark.decisionTree`](api/R/spark.decisionTree.html): `Decision Tree for` [`Regression`](ml-classification-regression.html#decision-tree-regression) `and` [`Classification`](ml-classification-regression.html#decision-tree-classifier)
-* [`spark.gbt`](api/R/spark.gbt.html): `Gradient Boosted Trees for` [`Regression`](ml-classification-regression.html#gradient-boosted-tree-regression) `and` [`Classification`](ml-classification-regression.html#gradient-boosted-tree-classifier)
-* [`spark.randomForest`](api/R/spark.randomForest.html): `Random Forest for` [`Regression`](ml-classification-regression.html#random-forest-regression) `and` [`Classification`](ml-classification-regression.html#random-forest-classifier)
+* [`spark.decisionTree`](api/R/reference/spark.decisionTree.html): `Decision Tree for` [`Regression`](ml-classification-regression.html#decision-tree-regression) `and` [`Classification`](ml-classification-regression.html#decision-tree-classifier)
+* [`spark.gbt`](api/R/reference/spark.gbt.html): `Gradient Boosted Trees for` [`Regression`](ml-classification-regression.html#gradient-boosted-tree-regression) `and` [`Classification`](ml-classification-regression.html#gradient-boosted-tree-classifier)
+* [`spark.randomForest`](api/R/reference/spark.randomForest.html): `Random Forest for` [`Regression`](ml-classification-regression.html#random-forest-regression) `and` [`Classification`](ml-classification-regression.html#random-forest-classifier)
#### Clustering
-* [`spark.bisectingKmeans`](api/R/spark.bisectingKmeans.html): [`Bisecting k-means`](ml-clustering.html#bisecting-k-means)
-* [`spark.gaussianMixture`](api/R/spark.gaussianMixture.html): [`Gaussian Mixture Model (GMM)`](ml-clustering.html#gaussian-mixture-model-gmm)
-* [`spark.kmeans`](api/R/spark.kmeans.html): [`K-Means`](ml-clustering.html#k-means)
-* [`spark.lda`](api/R/spark.lda.html): [`Latent Dirichlet Allocation (LDA)`](ml-clustering.html#latent-dirichlet-allocation-lda)
-* [`spark.powerIterationClustering (PIC)`](api/R/spark.powerIterationClustering.html): [`Power Iteration Clustering (PIC)`](ml-clustering.html#power-iteration-clustering-pic)
+* [`spark.bisectingKmeans`](api/R/reference/spark.bisectingKmeans.html): [`Bisecting k-means`](ml-clustering.html#bisecting-k-means)
+* [`spark.gaussianMixture`](api/R/reference/spark.gaussianMixture.html): [`Gaussian Mixture Model (GMM)`](ml-clustering.html#gaussian-mixture-model-gmm)
+* [`spark.kmeans`](api/R/reference/spark.kmeans.html): [`K-Means`](ml-clustering.html#k-means)
+* [`spark.lda`](api/R/reference/spark.lda.html): [`Latent Dirichlet Allocation (LDA)`](ml-clustering.html#latent-dirichlet-allocation-lda)
+* [`spark.powerIterationClustering (PIC)`](api/R/reference/spark.powerIterationClustering.html): [`Power Iteration Clustering (PIC)`](ml-clustering.html#power-iteration-clustering-pic)
#### Collaborative Filtering
-* [`spark.als`](api/R/spark.als.html): [`Alternating Least Squares (ALS)`](ml-collaborative-filtering.html#collaborative-filtering)
+* [`spark.als`](api/R/reference/spark.als.html): [`Alternating Least Squares (ALS)`](ml-collaborative-filtering.html#collaborative-filtering)
#### Frequent Pattern Mining
-* [`spark.fpGrowth`](api/R/spark.fpGrowth.html) : [`FP-growth`](ml-frequent-pattern-mining.html#fp-growth)
-* [`spark.prefixSpan`](api/R/spark.prefixSpan.html) : [`PrefixSpan`](ml-frequent-pattern-mining.html#prefixSpan)
+* [`spark.fpGrowth`](api/R/reference/spark.fpGrowth.html) : [`FP-growth`](ml-frequent-pattern-mining.html#fp-growth)
+* [`spark.prefixSpan`](api/R/reference/spark.prefixSpan.html) : [`PrefixSpan`](ml-frequent-pattern-mining.html#prefixSpan)
#### Statistics
-* [`spark.kstest`](api/R/spark.kstest.html): `Kolmogorov-Smirnov Test`
+* [`spark.kstest`](api/R/reference/spark.kstest.html): `Kolmogorov-Smirnov Test`
Under the hood, SparkR uses MLlib to train the model. Please refer to the corresponding section of MLlib user guide for example code.
-Users can call `summary` to print a summary of the fitted model, [predict](api/R/predict.html) to make predictions on new data, and [write.ml](api/R/write.ml.html)/[read.ml](api/R/read.ml.html) to save/load fitted models.
+Users can call `summary` to print a summary of the fitted model, [predict](api/R/reference/predict.html) to make predictions on new data, and [write.ml](api/R/reference/write.ml.html)/[read.ml](api/R/reference/read.ml.html) to save/load fitted models.
SparkR supports a subset of the available R formula operators for model fitting, including ‘~’, ‘.’, ‘:’, ‘+’, and ‘-‘.
diff --git a/docs/sql-getting-started.md b/docs/sql-getting-started.md
index 355d5f7f488..69396924e35 100644
--- a/docs/sql-getting-started.md
+++ b/docs/sql-getting-started.md
@@ -48,7 +48,7 @@ The entry point into all functionality in Spark is the [`SparkSession`](api/pyth
<div data-lang="r" markdown="1">
-The entry point into all functionality in Spark is the [`SparkSession`](api/R/sparkR.session.html) class. To initialize a basic `SparkSession`, just call `sparkR.session()`:
+The entry point into all functionality in Spark is the [`SparkSession`](api/R/reference/sparkR.session.html) class. To initialize a basic `SparkSession`, just call `sparkR.session()`:
{% include_example init_session r/RSparkSQLExample.R %}
@@ -104,7 +104,7 @@ As an example, the following creates a DataFrame based on the content of a JSON
## Untyped Dataset Operations (aka DataFrame Operations)
-DataFrames provide a domain-specific language for structured data manipulation in [Scala](api/scala/org/apache/spark/sql/Dataset.html), [Java](api/java/index.html?org/apache/spark/sql/Dataset.html), [Python](api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) and [R](api/R/SparkDataFrame.html).
+DataFrames provide a domain-specific language for structured data manipulation in [Scala](api/scala/org/apache/spark/sql/Dataset.html), [Java](api/java/index.html?org/apache/spark/sql/Dataset.html), [Python](api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) and [R](api/R/reference/SparkDataFrame.html).
As mentioned above, in Spark 2.0, DataFrames are just Dataset of `Row`s in Scala and Java API. These operations are also referred as "untyped transformations" in contrast to "typed transformations" come with strongly typed Scala/Java Datasets.
@@ -146,9 +146,9 @@ In addition to simple column references and expressions, DataFrames also have a
{% include_example untyped_ops r/RSparkSQLExample.R %}
-For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/R/index.html).
+For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/R/reference/index.html).
-In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the [DataFrame Function Reference](api/R/SparkDataFrame.html).
+In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the [DataFrame Function Reference](api/R/reference/SparkDataFrame.html).
</div>
diff --git a/docs/structured-streaming-programming-guide.md b/docs/structured-streaming-programming-guide.md
index c3b88a6d165..447e08bcb7f 100644
--- a/docs/structured-streaming-programming-guide.md
+++ b/docs/structured-streaming-programming-guide.md
@@ -498,14 +498,14 @@ to track the read position in the stream. The engine uses checkpointing and writ
# API using Datasets and DataFrames
Since Spark 2.0, DataFrames and Datasets can represent static, bounded data, as well as streaming, unbounded data. Similar to static Datasets/DataFrames, you can use the common entry point `SparkSession`
-([Scala](api/scala/org/apache/spark/sql/SparkSession.html)/[Java](api/java/org/apache/spark/sql/SparkSession.html)/[Python](api/python/reference/pyspark.sql/api/pyspark.sql.SparkSession.html#pyspark.sql.SparkSession)/[R](api/R/sparkR.session.html) docs)
+([Scala](api/scala/org/apache/spark/sql/SparkSession.html)/[Java](api/java/org/apache/spark/sql/SparkSession.html)/[Python](api/python/reference/pyspark.sql/api/pyspark.sql.SparkSession.html#pyspark.sql.SparkSession)/[R](api/R/reference/sparkR.session.html) docs)
to create streaming DataFrames/Datasets from streaming sources, and apply the same operations on them as static DataFrames/Datasets. If you are not familiar with Datasets/DataFrames, you are strongly advised to familiarize yourself with them using the
[DataFrame/Dataset Programming Guide](sql-programming-guide.html).
## Creating streaming DataFrames and streaming Datasets
Streaming DataFrames can be created through the `DataStreamReader` interface
([Scala](api/scala/org/apache/spark/sql/streaming/DataStreamReader.html)/[Java](api/java/org/apache/spark/sql/streaming/DataStreamReader.html)/[Python](api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamReader.html#pyspark.sql.streaming.DataStreamReader) docs)
-returned by `SparkSession.readStream()`. In [R](api/R/read.stream.html), with the `read.stream()` method. Similar to the read interface for creating static DataFrame, you can specify the details of the source – data format, schema, options, etc.
+returned by `SparkSession.readStream()`. In [R](api/R/reference/read.stream.html), with the `read.stream()` method. Similar to the read interface for creating static DataFrame, you can specify the details of the source – data format, schema, options, etc.
#### Input Sources
There are a few built-in sources.
diff --git a/python/docs/source/getting_started/quickstart_ps.ipynb b/python/docs/source/getting_started/quickstart_ps.ipynb
index 494e08da9ee..95555ca0a84 100644
--- a/python/docs/source/getting_started/quickstart_ps.ipynb
+++ b/python/docs/source/getting_started/quickstart_ps.ipynb
@@ -2104,2884 +2104,10 @@
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