From 702d85af2df9433254af6fa029683aa19c52a276 Mon Sep 17 00:00:00 2001
From: zero323 <zero323@users.noreply.github.com>
Date: Tue, 18 Apr 2017 19:59:18 -0700
Subject: [SPARK-20208][R][DOCS] Document R fpGrowth support

## What changes were proposed in this pull request?

Document  fpGrowth in:

- vignettes
- programming guide
- code example

## How was this patch tested?

Manual tests.

Author: zero323 <zero323@users.noreply.github.com>

Closes #17557 from zero323/SPARK-20208.
---
 R/pkg/vignettes/sparkr-vignettes.Rmd | 37 +++++++++++++++++++++++++++++++++++-
 1 file changed, 36 insertions(+), 1 deletion(-)

(limited to 'R')

diff --git a/R/pkg/vignettes/sparkr-vignettes.Rmd b/R/pkg/vignettes/sparkr-vignettes.Rmd
index a6ff650c33..f81dbab10b 100644
--- a/R/pkg/vignettes/sparkr-vignettes.Rmd
+++ b/R/pkg/vignettes/sparkr-vignettes.Rmd
@@ -505,6 +505,10 @@ SparkR supports the following machine learning models and algorithms.
 
 * Alternating Least Squares (ALS)
 
+#### Frequent Pattern Mining
+
+* FP-growth
+
 #### Statistics
 
 * Kolmogorov-Smirnov Test
@@ -707,7 +711,7 @@ summary(tweedieGLM1)
 ```
 We can try other distributions in the tweedie family, for example, a compound Poisson distribution with a log link:
 ```{r}
-tweedieGLM2 <- spark.glm(carsDF, mpg ~ wt + hp, family = "tweedie", 
+tweedieGLM2 <- spark.glm(carsDF, mpg ~ wt + hp, family = "tweedie",
                          var.power = 1.2, link.power = 0.0)
 summary(tweedieGLM2)
 ```
@@ -906,6 +910,37 @@ predicted <- predict(model, df)
 head(predicted)
 ```
 
+#### FP-growth
+
+`spark.fpGrowth` executes FP-growth algorithm to mine frequent itemsets on a `SparkDataFrame`. `itemsCol` should be an array of values.
+
+```{r}
+df <- selectExpr(createDataFrame(data.frame(rawItems = c(
+  "T,R,U", "T,S", "V,R", "R,U,T,V", "R,S", "V,S,U", "U,R", "S,T", "V,R", "V,U,S",
+  "T,V,U", "R,V", "T,S", "T,S", "S,T", "S,U", "T,R", "V,R", "S,V", "T,S,U"
+))), "split(rawItems, ',') AS items")
+
+fpm <- spark.fpGrowth(df, minSupport = 0.2, minConfidence = 0.5)
+```
+
+`spark.freqItemsets` method can be used to retrieve a `SparkDataFrame` with the frequent itemsets.
+
+```{r}
+head(spark.freqItemsets(fpm))
+```
+
+`spark.associationRules` returns a `SparkDataFrame` with the association rules.
+
+```{r}
+head(spark.associationRules(fpm))
+```
+
+We can make predictions based on the `antecedent`.
+
+```{r}
+head(predict(fpm, df))
+```
+
 #### Kolmogorov-Smirnov Test
 
 `spark.kstest` runs a two-sided, one-sample [Kolmogorov-Smirnov (KS) test](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test).
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