

In [2]:

    
# pip install rpy2



In [26]:

    
%load_ext rpy2.ipython









    



The rpy2.ipython extension is already loaded. To reload it, use:
  %reload_ext rpy2.ipython

Rpush: push Python object to R



In [27]:

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









    Out[27]:





array([ 7.25])



In [7]:

    
%%R
Y = c(2,4,3,9)
summary(lm(Y~X))









    





Call:
lm(formula = Y ~ X)

Residuals:
      1       2       3       4 
 0.5388  0.5498 -2.2183  1.1297 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)   -3.511      3.265  -1.076    0.395
X              1.105      0.432   2.558    0.125

Residual standard error: 1.842 on 2 degrees of freedom
Multiple R-squared:  0.7659,	Adjusted R-squared:  0.6488 
F-statistic: 6.543 on 1 and 2 DF,  p-value: 0.1249



In [8]:

    
%R plot(X, Y)



In [9]:

    
%R dat = data.frame(X, Y)

Rpull: pull data from R to python



In [10]:

    
%Rpull dat



In [11]:

    
dat



In [24]:

    
dat['X']









    Out[24]:





1     4.5
2     6.3
3     7.9
4    10.3
Name: X, dtype: float64



In [12]:

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









    Out[12]:





rpy2.robjects.packages.Package as a <module 'ggplot2' (built-in)>



In [13]:

    
%%R 
p = ggplot(data = dat, mapping = aes(x = X, y =Y))
p + geom_point()



In [14]:

    
%%R
library(lattice)
attach(mtcars)

# scatterplot matrix
splom(mtcars[c(1,3,4,5,6)], main="MTCARS Data")









    



/Users/chengjun/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: The following object is masked from package:ggplot2:

    mpg


  res = super(Function, self).__call__(*new_args, **new_kwargs)



In [15]:

    
%%R
data(diamonds) 
set.seed(42) 
small = diamonds[sample(nrow(diamonds), 1000), ] 
head(small)

p = ggplot(data = small, mapping = aes(x = carat, y = price))
p + geom_point()



In [16]:

    
%%R 
p = ggplot(data=small, mapping=aes(x=carat, y=price, shape=cut, colour=color)) 
p+geom_point()



In [17]:

    
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))









    



Call:
lm(formula = "mpg ~ wt + cyl", data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2893 -1.5512 -0.4684  1.5743  6.1004 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  39.6863     1.7150  23.141  < 2e-16 ***
wt           -3.1910     0.7569  -4.216 0.000222 ***
cyl          -1.5078     0.4147  -3.636 0.001064 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.568 on 29 degrees of freedom
Multiple R-squared:  0.8302,	Adjusted R-squared:  0.8185 
F-statistic: 70.91 on 2 and 29 DF,  p-value: 6.809e-12



In [18]:

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



In [19]:

    
%R head(diamonds)



In [21]:

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



In [22]:

    
print(base.summary(fit_dia))









    



Call:
lm(formula = "price ~ carat + cut + color + clarity + depth", 
    data = diamonds)

Residuals:
     Min       1Q   Median       3Q      Max 
-16805.0   -680.3   -197.9    466.2  10393.4 

Coefficients:
             Estimate Std. Error  t value Pr(>|t|)    
(Intercept) -3264.660    232.513  -14.041  < 2e-16 ***
carat        8885.816     12.034  738.362  < 2e-16 ***
cut.L         686.238     21.377   32.102  < 2e-16 ***
cut.Q        -319.729     18.383  -17.393  < 2e-16 ***
cut.C         180.446     15.556   11.600  < 2e-16 ***
cut^4           0.679     12.496    0.054   0.9567    
color.L     -1908.788     17.729 -107.667  < 2e-16 ***
color.Q      -627.976     16.121  -38.955  < 2e-16 ***
color.C      -172.431     15.072  -11.440  < 2e-16 ***
color^4        21.905     13.840    1.583   0.1135    
color^5       -85.781     13.076   -6.560 5.43e-11 ***
color^6       -50.112     11.889   -4.215 2.50e-05 ***
clarity.L    4214.426     30.873  136.508  < 2e-16 ***
clarity.Q   -1831.631     28.829  -63.533  < 2e-16 ***
clarity.C     922.123     24.686   37.354  < 2e-16 ***
clarity^4    -361.446     19.741  -18.310  < 2e-16 ***
clarity^5     215.655     16.117   13.381  < 2e-16 ***
clarity^6       2.606     14.039    0.186   0.8528    
clarity^7     110.305     12.383    8.908  < 2e-16 ***
depth          -7.160      3.727   -1.921   0.0547 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1157 on 53920 degrees of freedom
Multiple R-squared:  0.9159,	Adjusted R-squared:  0.9159 
F-statistic: 3.092e+04 on 19 and 53920 DF,  p-value: < 2.2e-16



In [ ]:

	carat	cut	color	clarity	depth	table	price	x	y	z
1	0.23	5	2	2	61.5	55	326	3.95	3.98	2.43
2	0.21	4	2	3	59.8	61	326	3.89	3.84	2.31
3	0.23	2	2	5	56.9	65	327	4.05	4.07	2.31
4	0.29	4	6	4	62.4	58	334	4.20	4.23	2.63
5	0.31	2	7	2	63.3	58	335	4.34	4.35	2.75
6	0.24	3	7	6	62.8	57	336	3.94	3.96	2.48