This script will show some elements of working in Python from RStudio.

version 2, updated. version 1: Jan 19, 2021.

R part

Here is your main project in R:

data <- mtcars
str(data)
## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

Python part

In order to start using Python syntax in RStudio, we must dance around a bit.

Step 1: preparation

Install and load the reticulate package: 

#install.packages("reticulate")
library(reticulate)

Step 1.1: Source a Python script from an {r} chunk:

reticulate::source_python("script.py")

Step 1.2: Insert a few lines of Python with py_ from an {r} chunk:

#library(reticulate)
py_run_string("import numpy as np")
py_run_string("my_python_array = np.array([1,4,6,8])")
py_run_string("print(my_python_array)")

Step 1.3: Install Python modules from an {r} chunk:

py_install("pandas")
py_install("seaborn")

Step 2: Work in Python!

For using whole Python chunks as part of a script from a {python} chunk

import numpy as np
my_python_array = np.array([3,4,6,8])
my_python_array
## array([3, 4, 6, 8])
import pandas as pd
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data', header=None)
df.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'salary']
df.head()
##    age          workclass  fnlwgt  ... hours-per-week  native-country  salary
## 0   39          State-gov   77516  ...             40   United-States   <=50K
## 1   50   Self-emp-not-inc   83311  ...             13   United-States   <=50K
## 2   38            Private  215646  ...             40   United-States   <=50K
## 3   53            Private  234721  ...             40   United-States   <=50K
## 4   28            Private  338409  ...             40            Cuba   <=50K
## 
## [5 rows x 15 columns]

NB: don’t be scared if neither Python objects nor code lines appear in the Environment or console.

import seaborn as sns
## Matplotlib created a temporary config/cache directory at C:\Users\lssi7\AppData\Local\Temp\matplotlib-95okh5lb because the default path (C:\Users\lssi7\OneDrive\?????????\.matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.
sns.countplot(x = "salary",data = df)

Bonus:

  1. You can use Python objects in R:
my_r_array <- py$my_python_array
class(my_r_array) # this is still a Python data class!
## [1] "array"
  1. Or convert them to R data types to perform any operations:
my_r_vector <- as.vector(py$my_python_array)
class(my_r_vector) # it is an R data type now.
## [1] "numeric"
my_r_vector <- my_r_vector * 2
my_r_vector
## [1]  6  8 12 16
  1. You can convert this object back to Python (use {python} chunks):
my_python_array2 = r.my_r_vector
print(my_python_array2)
## [6.0, 8.0, 12.0, 16.0]
  1. You can access any R object from the built-in data or from the Environment by calling it with r. from a {python} chunk. For example,
cars = r.mtcars #our mtcars
cars.head()
##                     mpg  cyl   disp     hp  drat  ...   qsec   vs   am  gear  carb
## Mazda RX4          21.0  6.0  160.0  110.0  3.90  ...  16.46  0.0  1.0   4.0   4.0
## Mazda RX4 Wag      21.0  6.0  160.0  110.0  3.90  ...  17.02  0.0  1.0   4.0   4.0
## Datsun 710         22.8  4.0  108.0   93.0  3.85  ...  18.61  1.0  1.0   4.0   1.0
## Hornet 4 Drive     21.4  6.0  258.0  110.0  3.08  ...  19.44  1.0  0.0   3.0   1.0
## Hornet Sportabout  18.7  8.0  360.0  175.0  3.15  ...  17.02  0.0  0.0   3.0   2.0
## 
## [5 rows x 11 columns]

Another example (start in an {r} chunk):

x = c(1:10) # create a vector in R

Then switch to a {python} chunk:

r.x # access the created vector with Python
## [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

Click here to download a cheatsheet!

Sources: https://www.infoworld.com/article/3340120/how-to-run-python-in-r.html