One of:

• a string path or URI to a directory containing data files

• a FileSystem that references a directory containing data files (such as what is returned by s3_bucket())

• a string path or URI to a single file

• a character vector of paths or URIs to individual data files

• a list of Dataset objects as created by this function

• a list of DatasetFactory objects as created by dataset_factory().

When sources is a vector of file URIs, they must all use the same protocol and point to files located in the same file system and having the same format.

Schema for the Dataset. If NULL (the default), the schema will be inferred from the data sources.

When sources is a directory path/URI, one of:

• a Schema, in which case the file paths relative to sources will be parsed, and path segments will be matched with the schema fields.

• a character vector that defines the field names corresponding to those path segments (that is, you're providing the names that would correspond to a Schema but the types will be autodetected)

• a Partitioning or PartitioningFactory, such as returned by hive_partition()

• NULL for no partitioning

The default is to autodetect Hive-style partitions unless hive_style = FALSE. See the "Partitioning" section for details. When sources is not a directory path/URI, partitioning is ignored.

Logical: should partitioning be interpreted as Hive-style? Default is NA, which means to inspect the file paths for Hive-style partitioning and behave accordingly.

logical: should all data fragments (files, Datasets) be scanned in order to create a unified schema from them? If FALSE, only the first fragment will be inspected for its schema. Use this fast path when you know and trust that all fragments have an identical schema. The default is FALSE when creating a dataset from a directory path/URI or vector of file paths/URIs (because there may be many files and scanning may be slow) but TRUE when sources is a list of Datasets (because there should be few Datasets in the list and their Schemas are already in memory).

list of optional FileSystemFactoryOptions:

• partition_base_dir: string path segment prefix to ignore when discovering partition information with DirectoryPartitioning. Not meaningful (ignored with a warning) for HivePartitioning, nor is it valid when providing a vector of file paths.

• exclude_invalid_files: logical: should files that are not valid data files be excluded? Default is FALSE because checking all files up front incurs I/O and thus will be slower, especially on remote filesystems. If false and there are invalid files, there will be an error at scan time. This is the only FileSystemFactoryOption that is valid for both when providing a directory path in which to discover files and when providing a vector of file paths.

• selector_ignore_prefixes: character vector of file prefixes to ignore when discovering files in a directory. If invalid files can be excluded by a common filename prefix this way, you can avoid the I/O cost of exclude_invalid_files. Not valid when providing a vector of file paths (but if you're providing the file list, you can filter invalid files yourself).

Single character used to separate fields within a record.

Single character used to quote strings.

Does the file escape quotes by doubling them? i.e. If this option is TRUE, the value ⁠""""⁠ represents a single quote, ⁠\"⁠.

Does the file use backslashes to escape special characters? This is more general than escape_double as backslashes can be used to escape the delimiter character, the quote character, or to add special characters like ⁠\\n⁠.

If TRUE, the first row of the input will be used as the column names and will not be included in the data frame. If FALSE, column names will be generated by Arrow, starting with "f0", "f1", ..., "fN". Alternatively, you can specify a character vector of column names.

A compact string representation of the column types, an Arrow Schema, or NULL (the default) to infer types from the data.

A character vector of strings to interpret as missing values.

Should blank rows be ignored altogether? If TRUE, blank rows will not be represented at all. If FALSE, they will be filled with missings.

Number of lines to skip before reading data.

see file reader options

see file reader options

User-defined timestamp parsers. If more than one parser is specified, the CSV conversion logic will try parsing values starting from the beginning of this vector. Possible values are:

• NULL: the default, which uses the ISO-8601 parser

• a character vector of strptime parse strings

• a list of TimestampParser objects