r/README.Rmd

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output: github_document
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```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# nanoarrow

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The goal of nanoarrow is to provide minimal useful bindings to the [Arrow C Data](https://arrow.apache.org/docs/format/CDataInterface.html) and [Arrow C Stream](https://arrow.apache.org/docs/format/CStreamInterface.html) interfaces using the [nanoarrow C library](https://arrow.apache.org/nanoarrow).

## Installation

You can install the released version of nanoarrow from [CRAN](https://cran.r-project.org/) with:

```r
install.packages("nanoarrow")
```

You can install the development version of nanoarrow from [GitHub](https://github.com/) with:

``` r
# install.packages("remotes")
remotes::install_github("apache/arrow-nanoarrow/r")
```

If you can load the package, you're good to go!

```{r}
library(nanoarrow)
```

## Example

The Arrow C Data and Arrow C Stream interfaces are comprised of three structures: the `ArrowSchema` which represents a data type of an array, the `ArrowArray` which represents the values of an array, and an `ArrowArrayStream`, which represents zero or more `ArrowArray`s with a common `ArrowSchema`. All three can be wrapped by R objects using the nanoarrow R package.

### Schemas

Use `infer_nanoarrow_schema()` to get the ArrowSchema object that corresponds to a given R vector type; use `as_nanoarrow_schema()` to convert an object from some other data type representation (e.g., an arrow R package `DataType` like `arrow::int32()`); or use `na_XXX()` functions to construct them.

```{r}
infer_nanoarrow_schema(1:5)
as_nanoarrow_schema(arrow::schema(col1 = arrow::float64()))
na_int64()
```

### Arrays

Use `as_nanoarrow_array()` to convert an object to an ArrowArray object:

```{r}
as_nanoarrow_array(1:5)
as_nanoarrow_array(data.frame(col1 = c(1.1, 2.2)))
```

You can use `as.vector()` or `as.data.frame()` to get the R representation of the object back:

```{r}
array <- as_nanoarrow_array(data.frame(col1 = c(1.1, 2.2)))
as.data.frame(array)
```

Even though at the C level the ArrowArray is distinct from the ArrowSchema, at the R level we attach a schema wherever possible. You can access the attached schema using `infer_nanoarrow_schema()`:

```{r}
infer_nanoarrow_schema(array)
```

### Array Streams

The easiest way to create an ArrowArrayStream is from a list of arrays or objects that can be converted to an array using `as_nanoarrow_array()`:

```{r}
stream <- basic_array_stream(
  list(
    data.frame(col1 = c(1.1, 2.2)),
    data.frame(col1 = c(3.3, 4.4))
  )
)
```

You can pull batches from the stream using the `$get_next()` method. The last batch will return `NULL`.

```{r}
stream$get_next()
stream$get_next()
stream$get_next()
```

You can pull all the batches into a `data.frame()` by calling `as.data.frame()` or `as.vector()`:

```{r}
stream <- basic_array_stream(
  list(
    data.frame(col1 = c(1.1, 2.2)),
    data.frame(col1 = c(3.3, 4.4))
  )
)

as.data.frame(stream)
```

After consuming a stream, you should call the release method as soon as you can. This lets the implementation of the stream release any resources (like open files) it may be holding in a more predictable way than waiting for the garbage collector to clean up the object.

## Integration with the arrow package

The nanoarrow package implements `as_nanoarrow_schema()`, `as_nanoarrow_array()`, and `as_nanoarrow_array_stream()` for most arrow package types. Similarly, it implements `arrow::as_arrow_array()`, `arrow::as_record_batch()`, `arrow::as_arrow_table()`, `arrow::as_record_batch_reader()`, `arrow::infer_type()`, `arrow::as_data_type()`, and `arrow::as_schema()` for nanoarrow objects such that you can pass equivalent nanoarrow objects into many arrow functions and vice versa.