Rpy2，pyrserve和PypeR如何比较?

问题描述

我想从Python程序中访问R.我知道Rpy2，pyrserve和PypeR.

I would like to access R from within a Python program. I am aware of Rpy2, pyrserve and PypeR.

这三种选择的优缺点是什么?

What are the advantages or disadvantages of these three options?

推荐答案

我比其他人更了解3种，但是按问题中给出的顺序:

I know one of the 3 better than the others, but in the order given in the question:

rpy2:

• Python和R之间的C级接口(R作为嵌入式进程运行)
• 暴露给Python的R对象，无需复制数据
• 相反，Python的numpy数组可以暴露给R而无需复制
• 低级接口(靠近R C-API)和高级接口(为方便起见)
• 可以对向量和数组进行就地修改
• R回调函数可以在Python中实现
• 可能具有带有Python标签的匿名R对象
• 可以进行Python酸洗
• 使用其控制台完全自定义R的行为(以便实现完整的R GUI)
• 支持有限的MSWindows

• C-level interface between Python and R (R running as an embedded process)
• R objects exposed to Python without the need to copy the data over
• Conversely, Python's numpy arrays can be exposed to R without making a copy
• Low-level interface (close to the R C-API) and high-level interface (for convenience)
• In-place modification for vectors and arrays possible
• R callback functions can be implemented in Python
• Possible to have anonymous R objects with a Python label
• Python pickling possible
• Full customization of R's behavior with its console (so possible to implement a full R GUI)
• MSWindows with limited support

pyrserve:

• 原生Python代码(将/应该/可与CPython，Jython，IronPython一起使用)
• 使用R's Rserve
• 与远程计算和RServe相关的优点和不便之处

佩珀:

• 原生Python代码(将/应该/可与CPython，Jython，IronPython一起使用)
• 使用管道使Python与R通信(具有与之相关的优点和不便之处)

Windows对rpy2的支持

edit: Windows support for rpy2

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