Rmagic, A Handy Interface Bridging Python and R
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Rmagic (http://ipython.org/ipython-doc/dev/config/extensions/rmagic.html) is the ipython extension that utilizes rpy2 in the back-end and provides a convenient interface accessing R from ipython. Compared with the generic use of rpy2, the rmagic extension allows users to exchange objects between ipython and R in a more flexible way and to run a single R function or a block of R code conveniently.
Below is an example demonstrating a simple use case how to push a pandas DataFrame object into R, convert it to a R data.frame, and then transfer back to a new pandas DataFrame object again.
In [1]: import pandas as pd
In [2]: # READ DATA INTO PANDAS DATAFRAME
In [3]: pydf1 = pd.read_table('../data/csdata.txt', header = 0)
In [4]: print pydf1.describe()
LEV_LT3 TAX_NDEB COLLAT1 SIZE1 PROF2 \
count 4421.000000 4421.000000 4421.000000 4421.000000 4421.000000
mean 0.090832 0.824537 0.317354 13.510870 0.144593
std 0.193872 2.884129 0.227150 1.692520 0.110908
min 0.000000 0.000000 0.000000 7.738052 0.000016
25% 0.000000 0.349381 0.124094 12.316970 0.072123
50% 0.000000 0.566577 0.287613 13.539574 0.120344
75% 0.011689 0.789128 0.472355 14.751119 0.187515
max 0.998372 102.149483 0.995346 18.586632 1.590201
GROWTH2 AGE LIQ IND2A IND3A \
count 4421.000000 4421.000000 4421.000000 4421.000000 4421.000000
mean 13.619633 20.366433 0.202813 0.611626 0.190228
std 36.517739 14.538997 0.233256 0.487435 0.392526
min -81.247627 6.000000 0.000000 0.000000 0.000000
25% -3.563235 11.000000 0.034834 0.000000 0.000000
50% 6.164303 17.000000 0.108544 1.000000 0.000000
75% 21.951632 25.000000 0.291366 1.000000 0.000000
max 681.354187 210.000000 1.000182 1.000000 1.000000
IND4A IND5A
count 4421.000000 4421.000000
mean 0.026917 0.099073
std 0.161859 0.298793
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 0.000000 0.000000
max 1.000000 1.000000
In [5]: # CONVERT PANDAS DATAFRAME TO R DATA.FRAME
In [6]: %load_ext rmagic
In [7]: col = pydf1.columns
In [8]: %R -i pydf1,col colnames(pydf1) <- unlist(col); print(is.matrix(pydf1))
[1] TRUE
In [9]: %R rdf <- data.frame(pydf1); print(is.data.frame(rdf))
[1] TRUE
In [10]: %R print(summary(rdf))
LEV_LT3 TAX_NDEB COLLAT1 SIZE1
Min. :0.00000 Min. : 0.0000 Min. :0.0000 Min. : 7.738
1st Qu.:0.00000 1st Qu.: 0.3494 1st Qu.:0.1241 1st Qu.:12.317
Median :0.00000 Median : 0.5666 Median :0.2876 Median :13.540
Mean :0.09083 Mean : 0.8245 Mean :0.3174 Mean :13.511
3rd Qu.:0.01169 3rd Qu.: 0.7891 3rd Qu.:0.4724 3rd Qu.:14.751
Max. :0.99837 Max. :102.1495 Max. :0.9953 Max. :18.587
PROF2 GROWTH2 AGE LIQ
Min. :0.0000158 Min. :-81.248 Min. : 6.00 Min. :0.00000
1st Qu.:0.0721233 1st Qu.: -3.563 1st Qu.: 11.00 1st Qu.:0.03483
Median :0.1203435 Median : 6.164 Median : 17.00 Median :0.10854
Mean :0.1445929 Mean : 13.620 Mean : 20.37 Mean :0.20281
3rd Qu.:0.1875148 3rd Qu.: 21.952 3rd Qu.: 25.00 3rd Qu.:0.29137
Max. :1.5902009 Max. :681.354 Max. :210.00 Max. :1.00018
IND2A IND3A IND4A IND5A
Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.00000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
Median :1.0000 Median :0.0000 Median :0.00000 Median :0.00000
Mean :0.6116 Mean :0.1902 Mean :0.02692 Mean :0.09907
3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.00000
In [11]: # CONVER R DATA.FRAME BACK TO PANDAS DATAFRAME
In [12]: %R -d rdf
In [13]: pydf2 = pd.DataFrame(rdf)
In [14]: print pydf2.describe()
LEV_LT3 TAX_NDEB COLLAT1 SIZE1 PROF2 \
count 4421.000000 4421.000000 4421.000000 4421.000000 4421.000000
mean 0.090832 0.824537 0.317354 13.510870 0.144593
std 0.193872 2.884129 0.227150 1.692520 0.110908
min 0.000000 0.000000 0.000000 7.738052 0.000016
25% 0.000000 0.349381 0.124094 12.316970 0.072123
50% 0.000000 0.566577 0.287613 13.539574 0.120344
75% 0.011689 0.789128 0.472355 14.751119 0.187515
max 0.998372 102.149483 0.995346 18.586632 1.590201
GROWTH2 AGE LIQ IND2A IND3A \
count 4421.000000 4421.000000 4421.000000 4421.000000 4421.000000
mean 13.619633 20.366433 0.202813 0.611626 0.190228
std 36.517739 14.538997 0.233256 0.487435 0.392526
min -81.247627 6.000000 0.000000 0.000000 0.000000
25% -3.563235 11.000000 0.034834 0.000000 0.000000
50% 6.164303 17.000000 0.108544 1.000000 0.000000
75% 21.951632 25.000000 0.291366 1.000000 0.000000
max 681.354187 210.000000 1.000182 1.000000 1.000000
IND4A IND5A
count 4421.000000 4421.000000
mean 0.026917 0.099073
std 0.161859 0.298793
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 0.000000 0.000000
max 1.000000 1.000000
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