Run Python from R

March 27, 2018

(This article was first published on ListenData, and kindly contributed to R-bloggers)

This article explains how to call or run python from R. Both the tools have its own advantages and disadvantages. It’s always a good idea to use the best packages and functions from both the tools and combine it. In data science world, these tools have a good market share in terms of usage. R is mainly known for data analysis, statistical modeling and visualization. While python is popular for deep learning and natural language processing.

In recent KDnuggets Analytics software survey poll, Python and R were ranked top 2 tools for data science and machine learning. If you really want to boost your career in data science world, these are the languages you need to focus on.

Combine Python and R

RStudio developed a package called reticulate which provides a medium to run Python packages and functions from R.

Install and Load Reticulate Package

Run the command below to get this package installed and imported to your system.

# Install reticulate package

# Load reticulate package

Check whether Python is available on your system


It returns TRUE/FALSE. If it is TRUE, it means python is installed on your system.

Import a python module within R

You can use the function import( ) to import a particular package or module.

os <- import(“os”)

The above program returns working directory.
[1] "C:\\Users\\DELL\\Documents"

You can use listdir( ) function from os package to see all the files in working directory


 [1] ".conda"                       ".gitignore"                   ".httr-oauth"                 
[4] ".matplotlib" ".RData" ".RDataTmp"
[7] ".Rhistory" "1.pdf" "12.pdf"
[10] "122.pdf" "124.pdf" "13.pdf"
[13] "1403.2805.pdf" "2.pdf" "3.pdf"
[16] "AIR.xlsx" "app.r" "Apps"
[19] "articles.csv" "Attrition_Telecom.xlsx" "AUC.R"

Install Python Package

Step 1 : Create a new environment 


Step 2 : Install a package within a conda environment

conda_install(“r-reticulate”, “numpy”)

Since numpy is already installed, you don’t need to install it again. The above example is just for demonstration.

Step 3 : Load the package

numpy <- import(“numpy”)

Working with numpy array

Let’s create a sample numpy array

y <- array(1:4, c(2, 2))
x <- numpy$array(y)

     [,1] [,2]
[1,] 1 3
[2,] 2 4

Transpose the above array


    [,1] [,2]
[1,] 1 2
[2,] 3 4

Eigenvalues and eigen vectors


[1] -0.3722813 5.3722813

[,1] [,2]
[1,] -0.9093767 -0.5657675
[2,] 0.4159736 -0.8245648

Mathematical Functions


Working with Python interactively

You can create an interactive Python console within R session. Objects you create within Python are available to your R session (and vice-versa).
By using repl_python() function, you can make it interactive. Download the dataset used in the program below.
# Load Pandas package
import pandas as pd
# Importing Dataset
travel = pd.read_excel(“AIR.xlsx”)
# Number of rows and columns

# Select random no. of rows
travel.sample(n = 10)

# Group By

# Filter
t = travel.loc[(travel.Month >= 6) & (travel.Year >= 1955),:]

# Return to R

Note : You need to enter exit to return to the R environment.

call python from R
Run Python from R

How to access objects created in python from R

You can use the py object to access objects created within python.


In this case, I am using R’s summary( ) function and accessing dataframe t which was created in python. Similarly, you can create line plot using ggplot2 package.

# Line chart using ggplot2
ggplot(py$t, aes(AIR, Year)) + geom_line()

How to access objects created in R from Python

You can use the r object to accomplish this task. 
1. Let’s create a object in R

mydata = head(cars, n=15)

2. Use the R created object within Python REPL

import pandas as pd

Building Logistic Regression Model using sklearn package
The sklearn package is one of the most popular package for machine learning in python. It supports various statistical and machine learning algorithms.
# Load libraries
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
# load the iris datasets
iris = datasets.load_iris()
# Developing logit model
model = LogisticRegression(),
# Scoring
actual =
predicted = model.predict(
# Performance Metrics
print(metrics.classification_report(actual, predicted))
print(metrics.confusion_matrix(actual, predicted))
Other Useful Functions

To see configuration of python

Run the py_config( ) command to find the version of R installed on your system.It also shows details about anaconda and numpy.


python:         C:\Users\DELL\ANACON~1\python.exe
libpython: C:/Users/DELL/ANACON~1/python36.dll
pythonhome: C:\Users\DELL\ANACON~1
version: 3.6.1 |Anaconda 4.4.0 (64-bit)| (default, May 11 2017, 13:25:24) [MSC v.1900 64 bit (AMD64)]
Architecture: 64bit
numpy: C:\Users\DELL\ANACON~1\lib\site-packages\numpy
numpy_version: 1.14.2

To check whether a particular package is installed

In the following program, we are checking whether pandas package is installed or not.


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