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 piping (magrittr style).
from rpy2.robjects.lib.dplyr import (filter,
mutate,
group_by,
summarize)
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:
from rpy2.rinterface import rternalize
@rternalize
def mean_np(x):
import numpy
return numpy.mean(x)
from rpy2.robjects import globalenv
globalenv['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='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:
print(res.rx2("query")["sql"])
And if the starting point is a pandas data frame, do the following and start over again.
from rpy2.robjects import pandas2ri
from rpy2.robjects import default_converter
from rpy2.robjects.conversion import localconverter
with localconverter(default_converter + pandas2ri.converter) as cv:
mtcars = mtcars_env['mtcars']
mtcars = pandas2ri.ri2py(mtcars)
print(type(mtcars))
Using a local converter lets us also go from the pandas data frame to our dplyr-augmented R data frame.
with localconverter(default_converter + pandas2ri.converter) as cv:
dataf = (DataFrame(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.