R: Read a CSV file into a Spark DataFrame

spark_read_csv {sparklyr} | R Documentation

## Read a CSV file into a Spark DataFrame

### Description

Read a tabular data file into a Spark DataFrame.

### Usage

    spark_read_csv(sc, name, path, header = TRUE, columns = NULL,
      infer_schema = TRUE, delimiter = ",", quote = "\"", escape = "\\",
      charset = "UTF-8", null_value = NULL, options = list(),
      repartition = 0, memory = TRUE, overwrite = TRUE)

### Arguments

`sc` | A `spark_connection`.
`name` | The name to assign to the newly generated table.
`path` | The path to the file. Needs to be accessible from the cluster. Supports the "hdfs://", "s3n://" and "file://" protocols.
`header` | Boolean; should the first row of data be used as a header? Defaults to `TRUE`.
`columns` | A named vector specifying column types.
`infer_schema` | Boolean; should column types be automatically inferred? Requires one extra pass over the data. Defaults to `TRUE`.
`delimiter` | The character used to delimit each column. Defaults to ','.
`quote` | The character used as a quote. Defaults to '"'.
`escape` | The character used to escape other characters. Defaults to '\'.
`charset` | The character set. Defaults to "UTF-8".
`null_value` | The character to use for null, or missing, values. Defaults to `NULL`.
`options` | A list of strings with additional options.
`repartition` | The number of partitions used to distribute the generated table. Use 0 (the default) to avoid partitioning.
`memory` | Boolean; should the data be loaded eagerly into memory? (That is, should the table be cached?)
`overwrite` | Boolean; overwrite the table with the given name if it already exists?

### Details

You can read data from HDFS (`hdfs://`), S3 (`s3n://`), as well as the local file system (`file://`).

If you are reading from a secure S3 bucket be sure that the `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` environment variables are both defined.

When `header` is `FALSE`, the column names are generated with a `V` prefix; e.g. `V1, V2, ...`.

### See Also

Other Spark serialization routines: `spark_load_table`, `spark_read_json`, `spark_read_parquet`, `spark_save_table`, `spark_write_csv`, `spark_write_json`, `spark_write_parquet`

[Package sparklyr version 0.5.4 Index]