Start vignette draft explaining the class and naming conventions

r/vignettes/arrow.Rmd

@@ -0,0 +1,21 @@
+---
+title: "Using the Arrow C++ Library in R"
+description: "This document describes the low-level interface to the Apache Arrow C++ library in R and reviews the patterns and conventions of the R package."
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Using the Arrow C++ Library in R}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+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.
+
+# Class structure
+
+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](https://github.com/apache/arrow/tree/master/cpp) or [documentation](https://arrow.apache.org/docs/cpp/). 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.