In [1]:
from functools import partial
from rpy2.ipython import html
html.html_rdataframe=partial(html.html_rdataframe, table_class="docutils")
/opt/hostedtoolcache/Python/3.10.17/x64/lib/python3.10/site-packages/rpy2/rinterface/__init__.py:1185: UserWarning: Environment variable "LD_LIBRARY_PATH" redefined by R and overriding existing variable. Current: "/opt/R/4.5.0/lib/R/lib:/usr/local/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/temurin-17-jdk-amd64/lib/server:/opt/hostedtoolcache/Python/3.10.17/x64/lib:/opt/hostedtoolcache/Python/3.10.17/x64/lib", R: "/opt/R/4.5.0/lib/R/lib:/usr/local/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/temurin-17-jdk-amd64/lib/server:/opt/R/4.5.0/lib/R/lib:/usr/local/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/temurin-17-jdk-amd64/lib/server:/opt/hostedtoolcache/Python/3.10.17/x64/lib:/opt/hostedtoolcache/Python/3.10.17/x64/lib" warnings.warn( /opt/hostedtoolcache/Python/3.10.17/x64/lib/python3.10/site-packages/rpy2/rinterface/__init__.py:1185: UserWarning: Environment variable "PWD" redefined by R and overriding existing variable. Current: "/home/runner/work/rpy2/rpy2/doc", R: "/home/runner/work/rpy2/rpy2/doc/notebooks" warnings.warn(
/opt/hostedtoolcache/Python/3.10.17/x64/lib/python3.10/site-packages/rpy2/rinterface/__init__.py:1185: UserWarning: Environment variable "LD_LIBRARY_PATH" redefined by R and overriding existing variable. Current: "/opt/R/4.5.0/lib/R/lib:/usr/local/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/temurin-17-jdk-amd64/lib/server:/opt/R/4.5.0/lib/R/lib:/usr/local/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/temurin-17-jdk-amd64/lib/server:/opt/hostedtoolcache/Python/3.10.17/x64/lib:/opt/hostedtoolcache/Python/3.10.17/x64/lib", R: "/opt/R/4.5.0/lib/R/lib:/usr/local/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/temurin-17-jdk-amd64/lib/server:/opt/R/4.5.0/lib/R/lib:/usr/local/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/temurin-17-jdk-amd64/lib/server:/opt/R/4.5.0/lib/R/lib:/usr/local/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/temurin-17-jdk-amd64/lib/server:/opt/hostedtoolcache/Python/3.10.17/x64/lib:/opt/hostedtoolcache/Python/3.10.17/x64/lib" warnings.warn( /opt/hostedtoolcache/Python/3.10.17/x64/lib/python3.10/site-packages/rpy2/rinterface/__init__.py:1185: UserWarning: Environment variable "R_SESSION_TMPDIR" redefined by R and overriding existing variable. Current: "/tmp/Rtmp2r0D3H", R: "/tmp/RtmpBedMOF" warnings.warn(
In [2]:
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).
In [3]:
import rpy2.ipython.html
rpy2.ipython.html.init_printing()
2- dplyr
In [4]:
from rpy2.robjects.lib.dplyr import DataFrame
from rpy2.robjects import rl
With this we have the choice of chaining (D3-style)
In [5]:
dataf = (
DataFrame(mtcars)
.filter(rl('gear>3'))
.mutate(powertoweight=rl('hp*36/wt'))
.group_by(rl('gear'))
.summarize(mean_ptw=rl('mean(powertoweight)'))
)
dataf
Out[5]:
| gear | mean_ptw | ||
|---|---|---|---|
| 0 | 1 | 4.0 | 1237.1266499803169 |
| 1 | 2 | 5.0 | 2574.0331639315027 |
or with pipes (magrittr style).
In [6]:
# currently no longer working
from rpy2.robjects.lib.dplyr import (filter,
mutate,
group_by,
summarize)
if False:
dataf = (DataFrame(mtcars) >>
filter(rl('gear>3')) >>
mutate(powertoweight=rl('hp*36/wt')) >>
group_by(rl('gear')) >>
summarize(mean_ptw=rl('mean(powertoweight)')))
dataf
The function rl creates unevaluated R language objects, which
are then consummed by the dplyr function, just like it would be
happening when using dplyr in R itself. 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.
In [7]:
# Define a python function, and make
# it a function R can use through `rternalize`
from rpy2.rinterface import rternalize
@rternalize(signature=False)
def mean_np(x):
import statistics
return statistics.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(rl('gear>3'))
.mutate(powertoweight=rl('hp*36/wt'))
.group_by(rl('gear'))
.summarize(mean_ptw=rl('mean(powertoweight)'),
mean_np_ptw=rl('mean_np(powertoweight)'))
)
dataf
Out[7]:
| gear | mean_ptw | mean_np_ptw | ||
|---|---|---|---|---|
| 0 | 1 | 4.0 | 1237.1266499803169 | 1237.1266499803169 |
| 1 | 2 | 5.0 | 2574.0331639315027 | 2574.0331639315027 |
It is also possible to carry this out without having to place the custom function in R's global environment, although this is not straightforward.
In [8]:
# First we delete our Python callback in globalenv to
# ensure that we are picking up our callback in our
# specific environment rather than this one.
del(globalenv['mean_np'])
from rpy2.robjects import Environment
my_env = Environment()
my_env['mean_np'] = mean_np
# Create an rlang "quosure" object within
# a given environment. We use the R package
# rlang used by dplyr.
from rpy2.robjects.lib.dplyr import rlang
myquo = rlang.quo.rcall(
[(None, rl('mean_np(rlang::enexpr(powertoweight))'))],
environment=my_env
)
dataf = (
DataFrame(mtcars)
.filter(rl('gear>3'))
.mutate(powertoweight=rl('hp*36/wt'))
.group_by(rl('gear'))
.summarize(
mean_ptw=rl('mean(powertoweight)'),
mean_np_ptw=myquo)
)
dataf
Out[8]:
| gear | mean_ptw | mean_np_ptw | ||
|---|---|---|---|---|
| 0 | 1 | 4.0 | 1237.1266499803169 | 1237.1266499803169 |
| 1 | 2 | 5.0 | 2574.0331639315027 | 2574.0331639315027 |
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.
In [9]:
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(rl('gear>3'))
.mutate(powertoweight=rl('hp*36/wt'))
.group_by(rl('gear'))
.summarize(mean_ptw=rl('mean(powertoweight)')))
print(res)
#
# Source: SQL [?? x 2] # Database: sqlite 3.49.1 [/tmp/tmp8qyxtji0] gear mean_ptw <dbl> <dbl> 1 4 1237. 2 5 2574.
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:
In [10]:
silent = dplyr.show_query(res)
<SQL>
SELECT `gear`, AVG(`powertoweight`) AS `mean_ptw` FROM ( SELECT `mtcars`.*, (`hp` * 36.0) / `wt` AS `powertoweight` FROM `mtcars` WHERE (`gear` > 3.0) ) AS `q01` GROUP BY `gear`
The conversion rules in rpy2 make the above easily applicable to pandas data frames, completing the "lexical loan" of the dplyr vocabulary from R.
In [11]:
from rpy2.robjects import pandas2ri
from rpy2.robjects import default_converter
# 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 (default_converter + pandas2ri.converter).context() as cv:
pd_mtcars = mtcars_env['mtcars']
print(type(pd_mtcars))
<class 'pandas.core.frame.DataFrame'>
Using a local conversion context lets us also go from the pandas data frame to our dplyr-augmented R data frame and use the dplyr transformations on it.
In [12]:
with (default_converter + pandas2ri.converter).context() as cv:
dataf = (DataFrame(pd_mtcars)
.filter(rl('gear>=3'))
.mutate(powertoweight=rl('hp*36/wt'))
.group_by(rl('gear'))
.summarize(mean_ptw=rl('mean(powertoweight)')))
dataf
Out[12]:
| mean_ptw | ||
|---|---|---|
| 0 | 1 | 1632.0477884748632 |
Reuse. Get things done. Don't reimplement.