import rpy2.robjects as robj
import matplotlib.pylab as plt

R = robj.r

#Enclose R commands in strings
print(R('1+2'))

#Import and use libraries
R.library('poilog')
print(R('poilogMLE(c(1,1,1,1,2,2,3,5,10))'))

#Store functions
rnorm = R.rnorm
randomnumbers = rnorm(100)
plt.hist(randomnumbers)

#Create R objects
##In Python
myRvector = robj.FloatVector([1, 2.2, 33, 27, 42])
print myRvector

##Inside R
robj.globalenv["x"] = 10
R('print(x)')

#Combine these abilities to run complex statistical analyses
ctl = robj.FloatVector([4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14])
trt = robj.FloatVector([4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69])
group = R.gl(2, 10, 20, labels = ["Ctl","Trt"])
weight = ctl + trt

robj.globalenv["weight"] = weight
robj.globalenv["group"] = group
lm_D9 = R.lm("weight ~ group")
print(R.summary(lm_D9))

#Plot from R (if you really want to)
rplot = robj.r.plot
rplot(rnorm(100), ylab="random")