R with Python and Jupyter Notebook¶

Download and install R¶

First, you'll want to install R – go here to read the installation instructions.

Use R in Jupyter notebooks¶

Run the code below from the command land. It starts up R, installs the adapter between Jupyter and R (a kernel), and then exits.

R -e "install.packages('IRkernel', repos = 'http://cran.us.r-project.org');IRkernel::installspec()"

Now when you open Jupyter you can create new R notebooks!

R and Python in one notebook¶

Install the rpy2 package, which allows Python to talk to R. You can just use pip to install it:

pip install rpy2

Each time you start a new notebook you'll need to load rpy2 as an extension:

In [1]:

%load_ext rpy2.ipython

%load_ext rpy2.ipython

And then you can run all of the R code you want, as long as you put %%R in the top of the cell.

In [2]:

%%R
R.version.string

%%R R.version.string

[1] "R version 4.2.2 (2022-10-31)"

Sharing variables between Python and R¶

You'll start with a pandas dataframe.

In [5]:

import pandas as pd

df = pd.read_csv("countries.csv")
df.head()

import pandas as pd df = pd.read_csv("countries.csv") df.head()

Out[5]:

	Country	Continent	GDP_per_capita	life_expectancy	Population
0	Afghanistan	Asia	663	54.863	22856302
1	Albania	Europe	4195	74.200	3071856
2	Algeria	Africa	5098	68.963	30533827
3	Angola	Africa	2446	45.234	13926373
4	Antigua and Barbuda	N. America	12738	73.544	77656

Then you'll import it into your R cell with -i df at the top of the cell. Now R can use df just like normal!

In [22]:

%%R -i df
library("ggplot2")

ggplot(df, aes(x=GDP_per_capita,
               y=life_expectancy,
               color=Continent)) +
geom_point()

%%R -i df library("ggplot2") ggplot(df, aes(x=GDP_per_capita, y=life_expectancy, color=Continent)) + geom_point()