The Apache Arrow C++ library provides rich, powerful features for working with columnar data. The arrow R package provides both a low-level interface to the C++ library and some higher-level, R-flavored tools for working with it. This vignette provides an overview of how the pieces fit together, and it describes the conventions that the classes and methods follow in R.

Multi-file datasets

The arrow package lets you work efficiently with large, multi-file datasets using dplyr methods. See vignette("dataset", package = "arrow") for an overview.

Reading and writing files

arrow provides some simple functions for using the Arrow C++ library to read and write files. These functions are designed to drop into your normal R workflow without requiring any knowledge of the Arrow C++ library and use naming conventions and arguments that follow popular R packages, particularly readr. The readers return data.frames (or if you use the tibble package, they will act like tbl_dfs), and the writers take data.frames.

Importantly, arrow provides basic read and write support for the Apache Parquet columnar data file format, without having to set up a database.

library(arrow)
df <- read_parquet("path/to/file.parquet")

This function, along with the other readers in the package, takes an optional col_select argument, inspired by the vroom package. This argument lets you use the “tidyselect” helper functions, as you can do in dplyr::select(), to specify that you only want to keep certain columns. You may also provide a character vector of column names to keep, as in the “select” argument to data.table::fread(). By narrowing your selection at read time, you can load a data.frame with less memory overhead.

For example, suppose you had written the iris dataset to Parquet. You could read a data.frame with only the columns c("Sepal.Length", "Sepal.Width") by doing

df <- read_parquet("iris.parquet", col_select = starts_with("Sepal"))

Just as you can read, you can write Parquet files:

write_parquet(df, "path/to/different_file.parquet")

The arrow package also includes a faster and more robust implementation of the Feather file format, providing read_feather() and write_feather(). This implementation depends on the same underlying C++ library as the Python version does, resulting in more reliable and consistent behavior across the two languages, as well as improved performance.

In addition to these readers and writers, the arrow package has wrappers for other readers in the C++ library; see ?read_csv_arrow and ?read_json_arrow. These readers are being developed to optimize for the memory layout of the Arrow columnar format and are not intended as a direct replacement for existing R CSV readers (base::read.csv, readr::read_csv, data.table::fread) that return an R data.frame.

Access to Arrow messages, buffers, and streams

The arrow package also provides many lower-level bindings to the C++ library, which enable you to access and manipulate Arrow objects. You can use these to build connectors to other applications and services that use Arrow. One example is Spark: the sparklyr package has support for using Arrow to move data to and from Spark, yielding significant performance gains.

Class structure and package conventions

C++ is an object-oriented language, so the core logic of the Arrow library is encapsulated in classes and methods. In the R package, these classes are implemented as R6 reference classes, most of which are exported from the namespace.

In order to match the C++ naming conventions, the R6 classes are in TitleCase, e.g. RecordBatch. This makes it easy to look up the relevant C++ implementations in the code or documentation. To simplify things in R, the C++ library namespaces are generally dropped or flattened; that is, where the C++ library has arrow::io::FileOutputStream, it is just FileOutputStream in the R package. One exception is for the file readers, where the namespace is necessary to disambiguate. So arrow::csv::TableReader becomes CsvTableReader, and arrow::json::TableReader becomes JsonTableReader.

Some of these classes are not meant to be instantiated directly; they may be base classes or other kinds of helpers. For those that you should be able to create, use the $create() method to instantiate an object. For example, rb <- RecordBatch$create(int = 1:10, dbl = as.numeric(1:10)) will create a RecordBatch. Many of these factory methods that an R user might most often encounter also have a snake_case alias, in order to be more familiar for contemporary R users. So record_batch(int = 1:10, dbl = as.numeric(1:10)) would do the same as RecordBatch$create() above.

The typical user of the arrow R package may never deal directly with the R6 objects. We provide more R-friendly wrapper functions as a higher-level interface to the C++ library. An R user can call read_parquet() without knowing or caring that they're instantiating a ParquetFileReader object and calling the $ReadFile() method on it. The classes are there and available to the advanced programmer who wants fine-grained control over how the C++ library is used.