#!/usr/bin/env python
# coding: utf-8

# ### 计算新闻传播学课程
# 
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# # rpy2: Using R within Jupyter Notebook
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# 王成军
# 
# wangchengjun@nju.edu.cn
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# 计算传播网 http://computational-communication.com

# > conda install rpy2

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get_ipython().run_line_magic('load_ext', 'rpy2.ipython')
# conda install rpy2


# # Rpush: push Python object to R

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import numpy as np
X = np.array([4.5,6.3,7.9, 10.3])
get_ipython().run_line_magic('Rpush', 'X')
get_ipython().run_line_magic('R', 'mean(X)')


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get_ipython().run_cell_magic('R', '', 'Y = c(2,4,3,9)\nsummary(lm(Y~X))\n')


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get_ipython().run_line_magic('R', 'plot(X, Y)')


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get_ipython().run_line_magic('R', 'dat = data.frame(X, Y)')


# # Rpull: pull data from R to python
# 
# https://rpy2.github.io/doc/latest/html/interactive.html?highlight=rpull#rpy2.ipython.rmagic.RMagics.Rpull
# 
# Not work for Python 3.X

# In[90]:


get_ipython().run_line_magic('R', "x = c(3,4,6.7); y = c(4,6,7); z = c('a',3,4)")


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get_ipython().run_line_magic('R', 'x')


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x


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get_ipython().run_line_magic('Rpull', 'dat')


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dat


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# import rpy2's package module
import rpy2.robjects.packages as rpackages
# import R's utility package
utils = rpackages.importr('utils') 


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# select a mirror for R packages
utils.chooseCRANmirror() 


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# R package names
packnames = ('ggplot2', 'hexbin')
# R vector of strings 
from rpy2.robjects.vectors import StrVector
# Selectively install what needs to be install.
# We are fancy, just because we can.
names_to_install = packnames 
if len(names_to_install) > 0:
    utils.install_packages(StrVector(names_to_install))


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import rpy2.interactive as r
import rpy2.interactive.packages # this can take few seconds
r.packages.importr('ggplot2')


# In[80]:


get_ipython().run_cell_magic('R', '', 'p = ggplot(data = dat, mapping = aes(x = X, y =Y))\np + geom_point()\n')


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get_ipython().run_cell_magic('R', '', 'library(lattice)\nattach(mtcars)\n\n# scatterplot matrix\nsplom(mtcars[c(1,3,4,5,6)], main="MTCARS Data")\n')


# In[82]:


get_ipython().run_cell_magic('R', '', 'data(diamonds) \nset.seed(42) \nsmall = diamonds[sample(nrow(diamonds), 1000), ] \nhead(small)\n\np = ggplot(data = small, mapping = aes(x = carat, y = price))\np + geom_point()\n')


# In[83]:


get_ipython().run_cell_magic('R', '', 'p = ggplot(data=small, mapping=aes(x=carat, y=price, shape=cut, colour=color)) \np+geom_point()\n')


# In[84]:


import rpy2.robjects as ro
from rpy2.robjects.packages import importr

base = importr('base')

fit_full = ro.r("lm('mpg ~ wt + cyl', data=mtcars)")
print(base.summary(fit_full))


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diamonds = ro.r("data(diamonds)")


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get_ipython().run_line_magic('R', 'head(diamonds)')


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fit_dia = ro.r("lm('price ~ carat + cut + color + clarity + depth', data=diamonds)")


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print(base.summary(fit_dia))


# # END

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