from functools import partial
from rpy2.ipython import html
html.html_rdataframe=partial(html.html_rdataframe, table_class="docutils")
from rpy2.robjects.packages import importr, data
datasets = importr('datasets')
mtcars_env = data(datasets).fetch('mtcars')
mtcars = mtcars_env['mtcars']
In addition to that, and because this tutorial is in a notebook, we initialize HTML rendering for R objects (pretty display of R data frames).
import rpy2.ipython.html
rpy2.ipython.html.init_printing()
2- dplyr
from rpy2.robjects.lib.dplyr import DataFrame
With this we have the choice of chaining (D3-style)
dataf = (DataFrame(mtcars).
filter('gear>3').
mutate(powertoweight='hp*36/wt').
group_by('gear').
summarize(mean_ptw='mean(powertoweight)'))
dataf
or with pipes (magrittr style).
# currently no longer working
from rpy2.robjects.lib.dplyr import (filter,
mutate,
group_by,
summarize)
if False:
dataf = (DataFrame(mtcars) >>
filter('gear>3') >>
mutate(powertoweight='hp*36/wt') >>
group_by('gear') >>
summarize(mean_ptw='mean(powertoweight)'))
dataf
The strings passed to the dplyr function are evaluated as expression,
just like this is happening when using dplyr in R. This means that
when writing mean(powertoweight) the R function mean() is used.
Using a Python function is not too difficult though. We can just
call Python back from R. To achieve this we simply
use the decorator rternalize.
# Define a python function, and make
# it a function R can use through `rternalize`
from rpy2.rinterface import rternalize
@rternalize
def mean_np(x):
import numpy
return numpy.mean(x)
# Bind that function to a symbol in R's
# global environment
from rpy2.robjects import globalenv
globalenv['mean_np'] = mean_np
# Write a dplyr chain of operations,
# using our Python function `mean_np`
dataf = (DataFrame(mtcars).
filter('gear>3').
mutate(powertoweight='hp*36/wt').
group_by('gear').
summarize(mean_ptw='mean(powertoweight)',
mean_np_ptw='mean_np(powertoweight)'))
dataf
It is also possible to carry this out without having to place the custom function in R's global environment.
del(globalenv['mean_np'])
from rpy2.robjects.lib.dplyr import StringInEnv
from rpy2.robjects import Environment
my_env = Environment()
my_env['mean_np'] = mean_np
dataf = (DataFrame(mtcars).
filter('gear>3').
mutate(powertoweight='hp*36/wt').
group_by('gear').
summarize(mean_ptw='mean(powertoweight)',
mean_np_ptw=StringInEnv('mean_np(powertoweight)',
my_env)))
dataf
note: rpy2's interface to dplyr is implementing a fix to the (non-?)issue 1323 (https://github.com/hadley/dplyr/issues/1323)
The seamless translation of transformations to SQL whenever the
data are in a table can be used directly. Since we are lifting
the original implementation of dplyr, it just works.
from rpy2.robjects.lib.dplyr import dplyr
# in-memory SQLite database broken in dplyr's src_sqlite
# db = dplyr.src_sqlite(":memory:")
import tempfile
with tempfile.NamedTemporaryFile() as db_fh:
db = dplyr.src_sqlite(db_fh.name)
# copy the table to that database
dataf_db = DataFrame(mtcars).copy_to(db, name="mtcars")
res = (dataf_db.
filter('gear>3').
mutate(powertoweight='hp*36/wt').
group_by('gear').
summarize(mean_ptw='mean(powertoweight)'))
print(res)
#
Since we are manipulating R objects, anything available to R is also available to us. If we want to see the SQL code generated that's:
silent = dplyr.show_query(res)
The conversion rules in rpy2 make the above easily applicable to pandas data frames, completing the "lexical loan" of the dplyr vocabulary from R.
from rpy2.robjects import pandas2ri
from rpy2.robjects import default_converter
from rpy2.robjects.conversion import localconverter
# Using a conversion context in which the pandas conversion is
# added to the default conversion rules, the rpy2 object
# `mtcars` (an R data frame) is converted to a pandas data frame.
with localconverter(default_converter + pandas2ri.converter) as cv:
mtcars = mtcars_env['mtcars']
pd_mtcars = pandas2ri.ri2py(mtcars)
print(type(pd_mtcars))
Using a local converter lets us also go from the pandas data frame to our dplyr-augmented R data frame and use the dplyr transformations on it.
with localconverter(default_converter + pandas2ri.converter) as cv:
dataf = (DataFrame(pd_mtcars).
filter('gear>=3').
mutate(powertoweight='hp*36/wt').
group_by('gear').
summarize(mean_ptw='mean(powertoweight)'))
dataf
Reuse. Get things done. Don't reimplement.