From fa8bc929ea0f1c24f3754fbaa9ddb47ac89e458f Mon Sep 17 00:00:00 2001 From: Holden Karau Date: Wed, 24 Jun 2015 10:59:20 -0700 Subject: [PATCH] typo: sparm -> spark --- docs/sparkr.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/sparkr.md b/docs/sparkr.md index cd7f3572b59d4..3501d4589f5e1 100644 --- a/docs/sparkr.md +++ b/docs/sparkr.md @@ -63,7 +63,7 @@ head(df) SparkR supports operating on a variety of data sources through the `DataFrame` interface. This section describes the general methods for loading and saving data using Data Sources. You can check the Spark SQL programming guide for more [specific options](sql-programming-guide.html#manually-specifying-options) that are available for the built-in data sources. The general method for creating DataFrames from data sources is `read.df`. This method takes in the `SQLContext`, the path for the file to load and the type of data source. SparkR supports reading JSON and Parquet files natively and through [Spark Packages](http://spark-packages.org/) you can find data source connectors for popular file formats like [CSV](http://spark-packages.org/package/databricks/spark-csv) and [Avro](http://spark-packages.org/package/databricks/spark-avro). These packages can either be added by -specifying `--packages` with `sparm-submit` or `sparkR` commands, or if creating context through `init` +specifying `--packages` with `spark-submit` or `sparkR` commands, or if creating context through `init` you can specify the packages with the `packages` argument. We can see how to use data sources using an example JSON input file. Note that the file that is used here is _not_ a typical JSON file. Each line in the file must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail.