rPithon vs. rPython

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Similar to rPython, the rPithon package (http://rpithon.r-forge.r-project.org) allows users to execute Python code from R and exchange the data between Python and R. However, the underlying mechanisms between these two packages are fundamentally different. Wihle rPithon communicates with Python from R through pipes, rPython accomplishes the same task with json. A major advantage of rPithon over rPython is that multiple Python processes can be started within a R session. However, rPithon is not very robust while exchanging large data objects between R and Python.

rPython Session

library(sqldf)
df_in <- sqldf('select Year, Month, DayofMonth from tbl2008 limit 5000', dbname = '/home/liuwensui/Documents/data/flights.db')
library(rPython)
### R DATA.FRAME TO PYTHON DICTIONARY ###
python.assign('py_dict', df_in)
### PASS PYTHON DICTIONARY BACK TO R LIST
r_list <- python.get('py_dict')
### CONVERT R LIST TO DATA.FRAME
df_out <- data.frame(r_list)
dim(df_out)
# [1] 5000    3
#
# real	0m0.973s
# user	0m0.797s
# sys	0m0.186s

rPithon Session

library(sqldf)
df_in <- sqldf('select Year, Month, DayofMonth from tbl2008 limit 5000', dbname = '/home/liuwensui/Documents/data/flights.db')
library(rPithon)
### R DATA.FRAME TO PYTHON DICTIONARY ###
pithon.assign('py_dict', df_in)
### PASS PYTHON DICTIONARY BACK TO R LIST
r_list <- pithon.get('py_dict')
### CONVERT R LIST TO DATA.FRAME
df_out <- data.frame(r_list)
dim(df_out)
# [1] 5000    3
#
# real	0m0.984s
# user	0m0.771s
# sys	0m0.187s

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This article is originally published at https://statcompute.wordpress.com
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