IPython integration

IPython integration

IPython integration is provided by the ipy_rnumpy module.

To use, start IPython, and simply import this module:

$ ipython
[...]
In [2]: import ipy_rnumpy

In [3]: 

This has two effects. First, R GUI/plotting functions will now work (of course, you can get the same effect in the normal python interpreter by calling rnumpy.set_interactive, which is what ipy_rnumpy does behind the scenes).

Secondly, a magic word R is added, which allows you to enter R code directly.

For short snippets you can simply prefix the expression with R:

In [4]: R c("a", "b")
Out[4]: [1] "a" "b"

Multi-line expressions work as well:

In [5]: R c("a", "b",
   ....: "c", "d")
Out[5]: [1] "a" "b" "c" "d"

IPython's normal history mechanisms continue to work:

In [5]: type(Out[4])
Out[5]: <class 'rnumpy.RWrapper'>

And it's a bit hard to show on a web page, but of course there's full support for tab completion. Try typing:

In [6]: R summ<tab>
summary             summary.data.frame  summary.matrix
summary.Date        summary.default     summary.mlm
summary.POSIXct     summary.factor      summary.stepfun
summary.POSIXlt     summary.glm         summary.table
summary.aov         summary.infl        summaryRprof
summary.aovlist     summary.lm          
summary.connection  summary.manova      

In [6]: R summary

For longer interactions, type R on or just R on a line by itself to enter "persistent R mode". Let's generate some fake data from a linear model, and then plot it:

In [7]: R
Entered R mode (use 'R' or 'R off' to switch back).
Tab completion enabled.
In [8] R> x <- 1:20
Out[8] R>  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

In [9] R> y <- 1 + 2 * x + rnorm(20)
Out[9] R> 
 [1]  1.585116  4.192806  7.407136  8.346777 10.992404 12.789048 18.272418
 [8] 17.230247 18.053606 21.727818 24.427297 26.304289 27.066721 28.809142
[15] 29.106337 34.080334 33.626247 37.849710 41.036069 41.147020

In [10] R> plot(y ~ x)
Out[10] R> NULL

In [11] R> R
Returned to Python mode (use 'R' or 'R on' to switch back).

In [12]: 

Note that one returns to Python mode by simply entering 'R' or 'R off'.

If you gave the -pylab option to ipython, to enable matplotlib support, then you can compare R's plotting to Matplotlib's, either by pulling x and y out of the output history:

In [13]: plot(_8, _9, "o")
Out[13]: [<matplotlib.lines.Line2D object at 0x2aaaab59a310>]

Or through the rnumpy.r object:

In [14]: from rnumpy import *

In [15]: plot(r.x, r.y, "o")
Out[15]: [<matplotlib.lines.Line2D object at 0x2aaaab596890>]

There are, of course, some known limitations, and you'll probably discover more -- let us know at Limitations.

Updated

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