# toJSON

Converts a SparkDataFrame into a SparkDataFrame of JSON string. Each row
is turned into a JSON document with columns as different fields. The
returned SparkDataFrame has a single character column with the name
`value`

## Usage

``` r
# S4 method for class 'SparkDataFrame'
toJSON(x)
```

## Arguments

  - x:
    
    a SparkDataFrame

## Value

a SparkDataFrame

## Note

toJSON since 2.2.0

## See also

Other SparkDataFrame functions: `SparkDataFrame-class`, `agg()`,
`alias()`, `arrange()`, `as.data.frame()`,
`attach,SparkDataFrame-method`, `broadcast()`, `cache()`,
`checkpoint()`, `coalesce()`, `collect()`, `colnames()`, `coltypes()`,
`createOrReplaceTempView()`, `crossJoin()`, `cube()`, `dapply()`,
`dapplyCollect()`, `describe()`, `dim()`, `distinct()`, `drop()`,
`dropDuplicates()`, `dropna()`, `dtypes()`, `except()`, `exceptAll()`,
`explain()`, `filter()`, `first()`, `gapply()`, `gapplyCollect()`,
`getNumPartitions()`, `group_by()`, `head()`, `hint()`, `histogram()`,
`insertInto()`, `intersect()`, `intersectAll()`, `isLocal()`,
`isStreaming()`, `join()`, `limit()`, `localCheckpoint()`, `merge()`,
`mutate()`, `ncol()`, `nrow()`, `persist()`, `printSchema()`,
`randomSplit()`, `rbind()`, `rename()`, `repartition()`,
`repartitionByRange()`, `rollup()`, `sample()`, `saveAsTable()`,
`schema()`, `select()`, `selectExpr()`, `show()`, `showDF()`,
`storageLevel()`, `str()`, `subset()`, `summary()`, `take()`, `union()`,
`unionAll()`, `unionByName()`, `unpersist()`, `unpivot()`, `with()`,
`withColumn()`, `withWatermark()`, `write.df()`, `write.jdbc()`,
`write.json()`, `write.orc()`, `write.parquet()`, `write.stream()`,
`write.text()`

## Examples

``` r
if (FALSE) { # \dontrun{
sparkR.session()
path <- "path/to/file.parquet"
df <- read.parquet(path)
df_json <- toJSON(df)
} # }
```