IPython Rpy2 passing parameters

rpy2_from_ipython.py

# Quick summary to access stuff in R from ipython
# Useful link but summary somehwat buried
# http://rpy.sourceforge.net/rpy2/doc-2.4/html/interactive.html

import numpy as np

%load_ext rpy2.ipython

# %R [-i INPUT] [-o OUTPUT] [-n] [-w WIDTH] [-h HEIGHT] [-p POINTSIZE]
# [-b BG] [–noisolation] [-u {px,in,cm,mm}] [-r RES] [code [code ...]]

# %R - returns a value 
# %%R - doesn't return a value (cell magic)

x = np.random.uniform(size=100)
y = np.random.uniform(size=100)
A = np.array([x, y])

# covariance matrix in python
Cpy = np.cov(A)
Cpy.shape
# (2, 2)

# Try the same in R, sending 'A' to R and getting variable 'C' back
%R -i A -o C C <- cov(A) 

# Ouch, Matrixes have different shapes
print(C.shape)
# (100, 100)

%R -i A -o C C <- cov(t(A)) 
print(C.shape)
# (2, 2)

# PUSH and PULL variables to and from R
X = np.random.uniform(size=5)

%Rpush X
%R mean(X)
# array([ 0.5623669])

%R Y <- rbeta(10, 5, 15)
%Rpull Y

print(Y.shape)
# (10,)

# GET to return the value, rather than create a variable (PULL)
Y2 = %Rget Y
print(Y2)
# [ 0.28380814  0.30925777  0.45507836  0.26457303  0.14935679  0.32539179
#   0.41532512  0.26216892  0.25340409  0.36681474]

# prop.test(..) for a more complex example
# Returned results in mapped types
res = %R prop.test(c(21, 29), c(150, 170))

type(res)
# rpy2.robjects.vectors.ListVector

# Printing works nicely
print(res)
# 	2-sample test for equality of proportions with continuity correction

# data:  c(21, 29) out of c(150, 170)
# X-squared = 0.35732, df = 1, p-value = 0.55
# alternative hypothesis: two.sided
# 95 percent confidence interval:
#  -0.11611290  0.05493643
# sample estimates:
#   prop 1    prop 2
# 0.1400000 0.1705882
# Access Items in the List

[s for s in res.names]

# ['statistic',
# 'parameter',
# 'p.value',
# 'estimate',
# 'null.value',
# 'conf.int',
# 'alternative',
# 'method',
# 'data.name']

res.names.index('p.value')
# 2
res[res.names.index('p.value')]
# <FloatVector - Python:0x10d3cc488 / R:0x7f9848809a08>
# [0.549997]