[SPARK-40322][DOCS] Fix all dead links in the docs

### What changes were proposed in this pull request?

This PR fixes any dead links in the documentation.

### Why are the changes needed?

Correct the document.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

manual test.

Closes apache#37981 from wangyum/SPARK-40322.

Lead-authored-by: Yuming Wang <[email protected]>
Co-authored-by: Yuming Wang <[email protected]>
Signed-off-by: Dongjoon Hyun <[email protected]>

Author: wangyum

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.
 

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>

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>

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>

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),

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 ‘-‘.