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https://github.com/wainberg/ryp
R inside Python
https://github.com/wainberg/ryp
bioinformatics data-science python python-to-r r r-to-python rstats statistics
Last synced: 3 days ago
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R inside Python
- Host: GitHub
- URL: https://github.com/wainberg/ryp
- Owner: Wainberg
- License: mit
- Created: 2024-09-29T01:43:04.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-12-12T00:37:31.000Z (11 days ago)
- Last Synced: 2024-12-12T01:28:12.226Z (11 days ago)
- Topics: bioinformatics, data-science, python, python-to-r, r, r-to-python, rstats, statistics
- Language: Python
- Homepage:
- Size: 236 KB
- Stars: 165
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ryp: R inside Python
ryp is a minimalist, powerful Python library for:
- running R code inside Python
- quickly transferring huge datasets between Python (NumPy/pandas/polars) and R
without writing to disk
- interactively working in both languages at the same time
ryp is an alternative to the widely used [rpy2](https://github.com/rpy2/rpy2)
library. Compared to rpy2, ryp provides:
- increased stability
- a much simpler API, with less of a learning curve
- interactive printouts of R variables that match what you'd see in R
- a full-featured R terminal inside Python for interactive work
- inline plotting in Jupyter notebooks (requires the `svglite` R package)
- much faster data conversion with [Arrow](https://arrow.apache.org) (also
provided by [rpy2-arrow](https://github.com/rpy2/rpy2-arrow))
- support for *every* NumPy, pandas and polars data type representable in base
R, no matter how obscure
- support for sparse arrays/matrices
- recursive conversion of containers like R lists, Python tuples/lists/dicts,
and S3/S4/R6 objects
- full Windows support
ryp does the opposite of the
[reticulate](https://rstudio.github.io/reticulate) R library, which runs Python
inside R.
## Table of Contents
- [Installation](#installation)
- [Functionality](#functionality)
- [`r()`](#r)
- [`to_r()`](#to_r)
- [The `format` argument](#the-format-argument)
- [The `rownames` and `colnames` arguments](#the-rownames-and-colnames-arguments)
- [`to_py()`](#to_py)
- [The `format` argument](#the-format-argument-1)
- [The `index` argument](#the-index-argument)
- [The `squeeze` argument](#the-squeeze-argument)
- [`options()`](#options)
- [Conversion rules](#conversion-rules)
- [Python to R (`to_r()`)](#python-to-r-to_r)
- [NumPy data types](#numpy-data-types)
- [pandas-specific data types](#pandas-specific-data-types)
- [pandas Arrow data types (`pd.ArrowDtype`)](#pandas-arrow-data-types-pdarrowdtype)
- [Polars data types](#polars-data-types)
- [Notes](#notes)
- [R to Python (`to_py()`)](#r-to-python-to_py)
- [Data types](#data-types)
- [Examples](#examples)
## Installation
Install ryp via pip:
```bash
pip install ryp
```
conda:
```bash
conda install ryp
```
or mamba:
```bash
mamba install ryp
```
Or, install the development version via pip:
```bash
pip install git+https://github.com/Wainberg/ryp
```
ryp's only mandatory dependencies are:
- Python 3.7+
- R
- the [cffi](https://cffi.readthedocs.io/en/stable) Python package
- the [pyarrow](https://arrow.apache.org/docs/python) Python package, which
includes [NumPy](https://numpy.org) as a dependency
- the [arrow](https://arrow.apache.org/docs/r) R library
R and the arrow R library are automatically installed when installing ryp via
conda or mamba, but not via pip. ryp uses the R installation pointed to by the
environment variable `R_HOME`, or if `R_HOME` is not defined or not a
directory, by running `R RHOME` through `subprocess.run()`.
ryp also has several optional dependencies, which are not installed
automatically with pip, conda or mamba. These are:
- [pandas](https://pandas.pydata.org), for `format='pandas'`
- [polars](https://pola.rs), for `format='polars'`
- [SciPy](https://scipy.org) and the
[Matrix](https://cran.r-project.org/web/packages/Matrix) R library, for sparse
matrices
- the [svglite](https://cran.r-project.org/web/packages/svglite) R library, for
inline plotting in Jupyter notebooks
## Functionality
ryp consists of just four functions:
1. [`r(R_code)`](#r) runs a string of R code. [`r()`](#r) with no arguments
opens up an R terminal inside your Python terminal for interactive work.
2. [`to_r(python_object, R_variable_name)`](#to_r) converts a Python object
into an R object named `R_variable_name`.
3. [`to_py(R_statement)`](#to_py) converts the R object produced by evaluating
`R_statement` to Python. `R_statement` may be a single variable name, or a
more complex code snippet that evaluates to the R object you'd like to
convert.
4. [`options()`](#options), for getting or setting ryp's configuration options.
### `r()`
```python
r(R_code: str = ...) -> None
```
`r(R_code)` runs a string of R code inside ryp's R interpreter, which is
embedded inside Python. It can contain multiple statements separated by
semicolons or newlines (e.g. within a triple-quoted Python string). It returns
`None`; use `to_py()` instead if you would like to convert the result back to
Python.
`r()` with no arguments opens up an R terminal inside your Python terminal
for interactive debugging. Press `Ctrl + D` to exit back to the Python
terminal. R variables defined from Python will be available in the R terminal,
and variables defined in the R terminal will be available from Python once you
exit:
```python
>>> from ryp import r
>>> r('a = 1')
>>> r()
> a
[1]
1
> b <- 2
>
>>> r('b')
[1]
2
```
Note that the default value for `R_code` is the special sentinel value `...`
(`Ellipsis`) rather than `None`. This stops users from inadvertently opening
the terminal when passing a variable that is supposed to be a string but is
unexpectedly `None`.
### `to_r()`
```python
to_r(python_object: object, R_variable_name: str, *,
format: Literal['keep', 'matrix', 'data.frame'] | None = None,
rownames: object = None, colnames: object = None) -> None
```
`to_r(python_object, R_variable_name)` converts `python_object` to R, adding it
to R's global namespace (`globalenv`) as a variable named `R_variable_name`.
If `python_object` is a container (`list`, `tuple`, or `dict`), `to_r()`
recursively converts each element and returns an R named list (if
`python_object` is a `dict`) or unnamed list (if `python_object` is a `list` or
`tuple`).
#### The `format` argument
By default (`format='keep'`), ryp converts polars and pandas DataFrames (and
pandas MultiIndexes) into R data frames, and 2D NumPy arrays into R matrices.
Specify `format='matrix'` to convert everything (even DataFrames) to R matrices
(in which case all DataFrame columns must have the same type), and
`format='data.frame'` to convert everything (even 2D NumPy arrays) to R
data frames.
`format` must be `None` unless `python_object` is a DataFrame, MultiIndex or 2D
NumPy array – or unless `python_object` is a `list`, `tuple`, or `dict`, in
which case the `format` will apply recursively to any DataFrames, MultiIndexes
or 2D NumPy arrays it contains.
#### The `rownames` and `colnames` arguments
Since NumPy arrays, polars DataFrames and Series, and scipy sparse arrays and
matrices lack row and column names, you can specify these separately via the
`rownames` and/or `colnames` arguments, and they will be added to the converted
R object. `rownames` and `colnames` can be lists, tuples, string Series, or
categorical Series with string categories, and will be automatically converted
to R character vectors.
`rownames` and `colnames` must match the length or `shape[1]`, respectively, of
the object being converted. The one exception is that rownames of any length
may be added to a 0 × 0 polars DataFrame, since polars does not have the
concept of an `N` × 0 DataFrame for nonzero `N`. (Dropping all the
columns of a polars DataFrame always results in a 0 × 0 DataFrame, even
if the original DataFrame had more than 0 rows.)
Because Python `bool`, `int`, `float`, and `str` convert to length-1 R vectors
that support names, you can pass length-1 `rownames` when converting objects of
these types. You can also pass `rownames` and/or `colnames` when
`python_object` is a `list`, `tuple`, or `dict`, in which case row and column
names will only be added to elements that support them. All elements that
support `rownames` must have the same length as the `rownames`, and similarly
for the `colnames`.
`rownames` cannot be specified if `python_object` is a pandas Series or
DataFrame (since they already have rownames, i.e. an index), or
`bytes`/`bytearray` (since these convert to `raw` vectors, which lack
rownames). `colnames` cannot be specified unless `python_object` is a
multidimensional NumPy array or scipy sparse array or matrix, or something that
might contain one (`list`, `tuple`, or `dict`).
### `to_py()`
```python
to_py(R_statement: str, *,
format: Literal['polars', 'pandas', 'pandas-pyarrow', 'numpy'] |
dict[Literal['vector', 'matrix', 'data.frame'],
Literal['polars', 'pandas', 'pandas-pyarrow',
'numpy']] | None = None,
index: str | Literal[False] | None = None,
squeeze: bool | None = None) -> Any
```
`to_py(R_statement)` runs a single statement of R code (which can be as simple
as a single variable name) and converts the resulting R object to Python.
If the object is a list/S3 object, S4 object, or environment/R6 object, it
recursively converts each attribute/slot/field and returns a Python `dict` (or
`list`, if the object is an unnamed list). For R6 objects, only public fields
will be converted.
#### The `format` argument
By default, or when `format='polars'`, R vectors will be converted to polars
Series, and R data frames and matrices will be converted to polars DataFrames.
You can change this by setting the `format` argument to `'pandas'`,
`'pandas-pyarrow'` (like `'pandas'`, but converting to pyarrow dtypes wherever
possible) or `'numpy'`. (You can also change the default format, e.g. with
`options(to_py_format='pandas')`.)
For finer-grained control, you can set `format` for only certain R variable
types by specifying a dictionary with `'vector'`, `'matrix'`, and/or
`'data.frame'` as keys and `'polars'`, `'pandas'`, `'pandas-pyarrow'` and/or
`'numpy'` as values.
`format` must be `None` when `R_statement` evaluates to `NULL`, when it
evaluates to an array of 3 or more dimensions (these are always converted to
NumPy arrays), or when the final result would be a Python scalar (see `squeeze`
below).
#### The `index` argument
By default, the R object's `names` or `rownames` will become the index (for
pandas) or the first column (for polars) of the output Python object, named
`'index'`. Set the `index` argument to a different string to change this name,
or set `index=False` to not convert the `names`/`rownames`.
Note that for polars, the output will be a two-column DataFrame (not a Series!)
when the input is an R vector, unless `index=False`.
When the output is a NumPy array, `names` and `rownames` will always be
discarded, since numeric NumPy arrays cannot store string indexes except with
the inefficient `dtype=object`.
`index` must be `None` when `format='numpy'`, or when the final result would be
a Python scalar (see `squeeze` below).
#### The `squeeze` argument
By default, length-1 R vectors, matrices and arrays will be converted to Python
scalars instead of Python arrays, Series or DataFrames. Set `squeeze=False` to
disable this special case. (R data frames are never converted to Python scalars
even if `squeeze=True`.)
`squeeze` must be `None` unless the R object is a vector, matrix or array
(`raw` vectors don't count, because they always convert to Python scalars).
### `options()`
```python
options(*, to_r_format=None, to_py_format=None, index=None, squeeze=None,
plot_width: int | float | None = None,
plot_height: int | float | None = None) -> None
```
`options` gets or sets ryp's configuration settings:
- `to_r_format`: the default value for the `format` parameter in `to_r()`;
must be `'keep'` (the default), `'matrix'`, or `'data.frame'`.
- `to_py_format`: the default value for the `format` parameter in `to_py()`;
must be `'polars'` (the default), `'pandas'`, `'pandas-pyarrow'`, `'numpy'`,
or a dictionary with one of those four Python formats and/or `None` as values
and `'vector'`, `'matrix'` and/or `'data.frame'` as keys. If certain keys are
missing or have `None` as the format, leave their format unchanged.
- `index`: the default value for the `index` parameter in to_py(); must be a
string (default: `'index'`) or `False`.
- `squeeze`: the default value for the `squeeze` parameter in `to_py()`; must
be `True` (the default) or `False`.
- `plot_width`: the width, in inches, of inline plots in Jupyter notebooks;
must be a positive number. Defaults to 6.4 inches, to match Matplotlib's
default.
- `plot_height`: the height, in inches, of inline plots in Jupyter notebooks;
must be a positive number. Defaults to 4.8 inches, to match Matplotlib's
default.
For instance, to set pandas as the default format in `to_py()`, run
`options(to_py_format='pandas')`. This leaves the other options unchanged.
`options()` with no arguments returns the current configuration options as a
dictionary, with keys `to_r_format`, `to_py_format`, `index`, `squeeze`,
`plot_width`, and `plot_height`.
For additional customization, users can specify ryp-specific settings in their
`.Rprofile`:
```R
if ("ryp" %in% commandArgs()) {
# Custom settings for running R within ryp
} else {
# Custom settings for native R
}
```
## Conversion rules
### Python to R (`to_r()`)
| Python | R |
|-------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|
| `None` | `NULL` (if scalar) or `NA` (if inside NumPy, pandas or polars) |
| `nan` | `NaN` (if scalar or inside polars) or `NA` (if inside NumPy or pandas) |
| `pd.NA` | `NA` |
| `pd.NaT`, `np.datetime64('NaT')`, `np.timedelta64('NaT')` | `NA` |
| `bool` | length-1 `logical` vector |
| `int` | length-1 `integer` (if `abs(x) <= 2_147_483_647`) or `bit64::integer64` vector |
| `float` | length-1 `numeric` vector |
| `str` | length-1 `character` vector |
| `complex` | length-1 `complex` vector |
| `datetime.date` | length-1 `Date` vector |
| `datetime.datetime` | length-1 `POSIXct` vector |
| `datetime.timedelta` | length-1 `difftime(units='secs')` vector |
| `datetime.time` (`tzinfo` must be `None`) | length-1 `hms::hms` vector |
| `bytes`, `bytearray` | `raw` vector |
| `list`, `tuple` | unnamed list |
| `dict` (all keys must be strings) | named list |
| polars Series, pandas Series*, pandas `Index` | vector |
| polars DataFrame, pandas DataFrame*, pandas `MultiIndex` | matrix† (if `format == 'matrix'`; all columns must have same data type) or `data.frame` |
| 1D NumPy array | vector |
| 2D NumPy array | `data.frame` (if `format == 'data.frame'`) or matrix† |
| ≥ 3D NumPy array | array† |
| 0D NumPy array (e.g. `np.array(1)`), NumPy generic (e.g. `np.int32(1)`) | length-1 vector |
| `csr_array`, `csr_matrix` | `dgRMatrix` (if int or float), `lgRMatrix` (if boolean), -- (if complex) |
| `csc_array`, `csc_matrix` | `dgCMatrix` (if int or float), `lgCMatrix` (if boolean), -- (if complex) |
| `coo_array`, `coo_matrix` | `dgTMatrix` (if int or float), `lgTMatrix` (if boolean), -- (if complex) |
#### NumPy data types
| Python | R |
|---------------------------------------------|----------------------------------------------------------------|
| `bool` | `logical` |
| `int8`, `uint8`, `int16`, `uint16`, `int32` | `integer` |
| `uint32`, `uint64` | `integer` (if `x <= 2_147_483_647`) or `numeric` |
| `int64` | `integer` (if `abs(x) <= 2_147_483_647`) or `bit64::integer64` |
| `float16`, `float32`, `float64`, `float128` | `numeric` (note: `float128` loses precision) |
| `complex64`, `complex128` | `complex` |
| `bytes` (e.g. `'S1'`) | -- |
| `str`/`unicode` (e.g. `'U1'`) | `character` |
| `datetime64` | `POSIXct` |
| `timedelta64` | `difftime(units='secs')` |
| `void` (unstructured) | `raw` |
| `void` (structured) | -- |
| `object` | depends on the contents‡ |
#### pandas-specific data types
| Python | R |
|----------------------------------------------------------------------|----------------------------------------------------------------|
| `BooleanDtype` | `logical` |
| `Int8Dtype`, `UInt8Dtype`, `Int16Dtype`, `UInt16Dtype`, `Int32Dtype` | `integer` |
| `UInt32Dtype`, `UInt64Dtype` | `integer` (if `x <= 2_147_483_647`) or `numeric` |
| `Int64Dtype` | `integer` (if `abs(x) <= 2_147_483_647`) or `bit64::integer64` |
| `Float32Dtype`, `Float64Dtype` | `numeric` |
| `StringDtype` | `character` |
| `CategoricalDtype(ordered=False)` | unordered `factor` |
| `CategoricalDtype(ordered=True)` | ordered `factor` |
| `DatetimeTZDtype`, `PeriodDtype` | `POSIXct` |
| `IntervalDtype`, `SparseDtype` | -- |
#### pandas Arrow data types (`pd.ArrowDtype`)
| Python | R |
|------------------------------------------------------------|----------------------------------------------------------------|
| `pa.bool_` | `logical` |
| `pa.int8`, `pa.uint8`, `pa.int16`, `pa.uint16`, `pa.int32` | `integer` |
| `pa.uint32`, `pa.uint64` | `integer` (if `x <= 2_147_483_647`) or `numeric` |
| `pa.int64` | `integer` (if `abs(x) <= 2_147_483_647`) or `bit64::integer64` |
| `pa.float32`, `pa.float64` | `numeric` |
| `pa.string`, `pa.large_string` | `character` |
| `pa.date32` | `Date` |
| `pa.date64`, `pa.timestamp` | `POSIXct` |
| `pa.duration` | `difftime(units='secs')` |
| `pa.time32`, `pa.time64` | `hms::hms` |
| `pa.dictionary(any integer type, pa.string(), ordered=0)` | unordered `factor` |
| `pa.dictionary(any integer type, pa.string(), ordered=1)` | ordered `factor` |
| `pa.null()` | `vctrs::unspecified` |
#### Polars data types
| Python | R |
|---------------------------------------------|----------------------------------------------------------------|
| `Boolean` | `logical` |
| `Int8`, `UInt8`, `Int16`, `UInt16`, `Int32` | `integer` |
| `UInt32`, `UInt64` | `integer` (if `x <= 2_147_483_647`) or `numeric` |
| `Int64` | `integer` (if `abs(x) <= 2_147_483_647`) or `bit64::integer64` |
| `Float32`, `Float64` | `numeric` |
| `Date` | `Date` |
| `Datetime` | `POSIXct` |
| `Duration` | `difftime(units='secs')` |
| `Time` | `hms::hms` |
| `String` | `character` |
| `Categorical` | unordered `factor` |
| `Enum` | ordered `factor` |
| `Object` | depends on the contents‡ |
| `Null` | `vctrs::unspecified` |
| `Binary`, `Decimal`, `List`, `Array` | -- |
#### Notes
* For pandas Series and DataFrames, string indexes (and
categorical indexes where the categories are strings) will be automatically
converted to `names`/`rownames`. The default index
(`pd.RangeIndex(len(python_object))`) will be ignored. All other indexes are
disallowed.
† Because R does not support `POSIXct` and `Date` matrices or
arrays, dates and datetimes cannot be converted to R matrices or arrays.
‡ For `dtype=object` and `dtype=pl.Object`, the output R type
depends on the contents, e.g. `'character'` if all elements are strings. Some
additional notes on ryp's handling of object data types:
- `None`, `np.nan`, `pd.NA`, `pd.NaT`, `np.datetime64('NaT')`, and
`np.timedelta64('NaT')` are all treated as missing values – even for
polars, where `np.nan` is ordinarily treated as a floating-point number
rather than a missing value.
- Length-0 and all-missing data will be converted to the `vctrs::unspecified` R
type (`vctrs` is part of the tidyverse).
- If the elements are objects with a mix of types (or datetimes with a mix of
time zones), Arrow will generally cause the conversion to fail, though mixes
of related types (e.g. int and float) will be automatically cast to the
common supertype and succeed.
- Conversion will also fail if the contents are objects that are not
representable as R vector elements. This includes `bytes`/`bytearray` (which
are only representable in R when scalar, as a `raw` vector) and Python
containers (`list`, `tuple`, and `dict`).
- pandas `Timedelta` objects will be rounded down to the nearest microsecond,
following the behavior of Arrow.
### R to Python (`to_py()`)
| R | Python |
|------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `NULL` | `None` |
| `NA` | `None` (if scalar or `format='polars'`), `None`/`nan`/`pd.NA`/`pd.NaT`/`np.datetime64('NaT', 'us')`/`np.timedelta64('NaT', 'ns')`/etc. (if `format='numpy'` `'pandas'` or `'pandas-pyarrow'`) |
| `NaN` | `nan` |
| length-1 vector, matrix or array, `squeeze == False` | scalar |
| vector or 1D array, `format == 'numpy'` | 1D NumPy array |
| vector or 1D array, `format == 'pandas'` or `format == 'pandas-pyarrow'` | pandas Series |
| vector or 1D array, `format == 'polars'` | polars Series (if `index=False`) or two-column DataFrame |
| matrix or `data.frame`, `format == 'numpy'` | 2D NumPy array |
| matrix or `data.frame`, `format == 'pandas'` or `format == 'pandas-pyarrow'` | pandas DataFrame |
| matrix or `data.frame`, `format == 'polars'` | polars DataFrame |
| ≥ 3D array | NumPy array |
| unnamed list | `list` |
| named list, S3 object, S4 object, environment, S6 object | `dict` |
| `dgRMatrix` | `csr_array(dtype='int32')` |
| `dgCMatrix` | `csc_array(dtype='int32')` |
| `dgTMatrix` | `coo_array(dtype='int32')` |
| `lgRMatrix`, `ngRMatrix` | `csr_array(dtype=bool)` |
| `lgCMatrix`, `ngCMatrix` | `csc_array(dtype=bool)` |
| `lgTMatrix`, `ngTMatrix` | `coo_array(dtype=bool)` |
| formula (`~`) | -- |
#### Data types
| R | Python scalar | NumPy | pandas | pandas-pyarrow | polars |
|-----------------------------|--------------------------------------|----------------------------------------------------------|----------------------------------------------------------|----------------------------------------------------------------|--------------------------------------------|
| `logical` | `bool` | `bool` | `bool` | `ArrowDtype(pa.bool_())` | `Boolean` |
| `integer` | `int` | `int32` | `int32` | `ArrowDtype(pa.int32())` | `Int32` |
| `bit64::integer64` | `int` | `int64` | `int64` | `ArrowDtype(pa.int64())` | `Int64` |
| `numeric` | `float` | `float` | `float` | `ArrowDtype(pa.float64())` | `Float64` |
| `character` | `str` | `object` (with `str` elements) | `object` (with `str` elements) | `ArrowDtype(pa.string())` | `String` |
| `complex` | `complex` | `complex128` | `complex128` | `complex128` | -- |
| `raw` | `bytearray` | -- | -- | -- | -- |
| unordered `factor` | `str` | `object` (with `str` elements) | `CategoricalDtype(ordered=False)` | `ArrowDtype(pa.dictionary(pa.int8(), pa.string(), ordered=0))` | `Categorical` |
| ordered `factor` | `str` | `object` (with `str` elements) | `CategoricalDtype(ordered=True)` | `ArrowDtype(pa.dictionary(pa.int8(), pa.string(), ordered=1))` | `Enum` |
| `POSIXct` without time zone | `datetime.datetime`* | `datetime64[us]`* | `datetime64[us]`* | `ArrowDtype(pa.timestamp('us'))`* | `Datetime('us')`* |
| `POSIXct` with time zone | `datetime.datetime`* | `datetime64[us]`* (time zone discarded) | `DatetimeTZDtype('us', time_zone)`* | `ArrowDtype(pa.timestamp('us', time_zone))`* | `Datetime('us, time_zone)`* |
| `POSIXlt` | `dict` of scalars | `dict` of NumPy arrays | `dict` of pandas Series | `dict` of pandas Series | `dict` of polars Series |
| `Date` | `datetime.date` | `datetime64[D]` | `datetime64[ms]` | `ArrowDtype(pa.date32('day'))` | `Date` |
| `difftime` | `datetime.timedelta`* | `timedelta64[ns]` | `timedelta64[ns]` | `ArrowDtype(pa.duration('ns'))` | `Duration(time_unit='ns')` |
| `hms::hms` | `datetime.time`* | `object` (with `datetime.time` elements)* | `object` (with `datetime.time` elements)* | `ArrowDtype(pa.time64('ns'))`* | `Time` |
| `vctrs::unspecified` | `None` | `object` (with `None` elements) | `object` (with `None` elements) | `ArrowDtype(pa.null())` | `Null` |
* Due to the limitations of conversion with Arrow, `POSIXct` and
`hms::hms` values are rounded down to the nearest microsecond when converting
to Python, except for `hms::hms` when converting to polars. `difftime` values
are also rounded down to the nearest microsecond, but only when converting to
scalar `datetime.timedelta` values (which cannot represent nanoseconds).
## Examples
1. Apply R's `scale()` function to a pandas DataFrame:
```python
import pandas as pd
from ryp import r, to_py, to_r
data = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 3, 4]})
to_r(data, 'data')
r('data')
# a b
# 1 1 1
# 2 2 3
# 3 3 4
r('data <- scale(data)') # scale the R data.frame
scaled_data = to_py('data', format='pandas') # convert the R data.frame to Python
scaled_data
# a b
# 0 -1.0 -1.091089
# 1 0.0 0.218218
# 2 1.0 0.872872
```
Note: we could have just written `to_py('scale(data)')` instead of
`r('data <- scale(data)')` followed by `to_py('data')`. We could also have
run `options(to_py_format='pandas')` at the top, to avoid having to specify
`format='pandas'` in each `to_py()` call.
2. Run a linear model on a polars DataFrame:
```python
import polars as pl
from ryp import r, to_py, to_r
data = pl.DataFrame({'y': [7, 1, 2, 3, 6], 'x': [5, 2, 3, 2, 5]})
to_r(data, 'data')
r('model <- lm(y ~ x, data=data)')
coef = to_py('summary(model)$coefficients', index='variable')
p_value = coef.filter(variable='x').select('Pr(>|t|)')[0, 0]
p_value
# 0.02831035772841884
```
3. Recursive conversion, showcasing all the keyword arguments of `to_r()` and
`to_py()`:
```python
import numpy as np
from ryp import r, to_py, to_r
arrays = {'ints': np.array([[1, 2], [3, 4]]),
'floats': np.array([[0.5, 1.5], [2.5, 3.5]])}
to_r(arrays, 'arrays', format='data.frame',
rownames = ['row1', 'row2'], colnames = ['col1', 'col2'])
r('arrays')
# $ints
# col1 col2
# row1 1 2
# row2 3 4
#
# $floats
# col1 col2
# row1 0.5 1.5
# row2 2.5 3.5
arrays = to_py('arrays', format='pandas', index='foo')
arrays['ints']
# col1 col2
# foo
# row1 1 2
# row2 3 4
arrays['floats']
# col1 col2
# foo
# row1 0.5 1.5
# row2 2.5 3.5
```