**Econometrics and Free Software**, and kindly contributed to R-bloggers)

This short blog post illustrates how easy it is to use R and Python in the same R Notebook thanks to the

`{reticulate}`

package. For this to work, you might need to upgrade RStudio to the current preview version.

Let’s start by importing `{reticulate}`

:

`library(reticulate)`

`{reticulate}`

is an RStudio package that provides “*a comprehensive set of tools for interoperability
between Python and R*”. With it, it is possible to call Python and use Python libraries within

an R session, or define Python chunks in R markdown. I think that using R Notebooks is the best way

to work with Python and R; when you want to use Python, you simply use a Python chunk:

```
```{python}
your python code here
```
```

There’s even autocompletion for Python object methods:

Fantastic!

However, if you wish to use Python interactively within your R session, you must start the Python

REPL with the `repl_python()`

function, which starts a Python REPL. You can then do whatever you

want, even access objects from your R session, and then when you exit the REPL, any object you

created in Python remains accessible in R. I think that using Python this way is a bit more involved

and would advise using R Notebooks if you need to use both languages.

I installed the Anaconda Python distribution to have Python on my system. To use it with `{reticulate}`

I must first use the `use_python()`

function that allows me to set which version of Python I want

to use:

```
# This is an R chunk
use_python("~/miniconda3/bin/python")
```

I can now load a dataset, still using R:

```
# This is an R chunk
data(mtcars)
head(mtcars)
```

```
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
```

and now, to access the `mtcars`

data frame, I simply use the `r`

object:

```
# This is a Python chunk
print(r.mtcars.describe())
```

```
## mpg cyl disp ... am gear carb
## count 32.000000 32.000000 32.000000 ... 32.000000 32.000000 32.0000
## mean 20.090625 6.187500 230.721875 ... 0.406250 3.687500 2.8125
## std 6.026948 1.785922 123.938694 ... 0.498991 0.737804 1.6152
## min 10.400000 4.000000 71.100000 ... 0.000000 3.000000 1.0000
## 25% 15.425000 4.000000 120.825000 ... 0.000000 3.000000 2.0000
## 50% 19.200000 6.000000 196.300000 ... 0.000000 4.000000 2.0000
## 75% 22.800000 8.000000 326.000000 ... 1.000000 4.000000 4.0000
## max 33.900000 8.000000 472.000000 ... 1.000000 5.000000 8.0000
##
## [8 rows x 11 columns]
```

`.describe()`

is a Python Pandas DataFrame method to get summary statistics of our data. This means that

`mtcars`

was automatically converted from a `tibble`

object to a Pandas DataFrame! Let’s check its type:

```
# This is a Python chunk
print(type(r.mtcars))
```

`## `

Let’s save the summary statistics in a variable:

```
# This is a Python chunk
summary_mtcars = r.mtcars.describe()
```

Let’s access this from R, by using the `py`

object:

```
# This is an R chunk
class(py$summary_mtcars)
```

`## [1] "data.frame"`

Let’s try something more complex. Let’s first fit a linear model in Python, and see how R sees it:

```
# This is a Python chunk
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
model = smf.ols('mpg ~ hp', data = r.mtcars).fit()
print(model.summary())
```

```
## OLS Regression Results
## ==============================================================================
## Dep. Variable: mpg R-squared: 0.602
## Model: OLS Adj. R-squared: 0.589
## Method: Least Squares F-statistic: 45.46
## Date: Sun, 30 Dec 2018 Prob (F-statistic): 1.79e-07
## Time: 00:45:07 Log-Likelihood: -87.619
## No. Observations: 32 AIC: 179.2
## Df Residuals: 30 BIC: 182.2
## Df Model: 1
## Covariance Type: nonrobust
## ==============================================================================
## coef std err t P>|t| [0.025 0.975]
## ------------------------------------------------------------------------------
## Intercept 30.0989 1.634 18.421 0.000 26.762 33.436
## hp -0.0682 0.010 -6.742 0.000 -0.089 -0.048
## ==============================================================================
## Omnibus: 3.692 Durbin-Watson: 1.134
## Prob(Omnibus): 0.158 Jarque-Bera (JB): 2.984
## Skew: 0.747 Prob(JB): 0.225
## Kurtosis: 2.935 Cond. No. 386.
## ==============================================================================
##
## Warnings:
## [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
```

Just for fun, I ran the linear regression with the Scikit-learn library too:

```
# This is a Python chunk
import numpy as np
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
x = r.mtcars[["hp"]]
y = r.mtcars[["mpg"]]
model_scikit = regressor.fit(x, y)
print(model_scikit.intercept_)
```

`## [30.09886054]`

`print(model_scikit.coef_)`

`## [[-0.06822828]]`

Let’s access the `model`

variable in R and see what type of object it is in R:

```
# This is an R chunk
model_r <- py$model
class(model_r)
```

```
## [1] "statsmodels.regression.linear_model.RegressionResultsWrapper"
## [2] "statsmodels.base.wrapper.ResultsWrapper"
## [3] "python.builtin.object"
```

So because this is a custom Python object, it does not get converted into the equivalent R object.

This is described here. However, you can still

use Python methods from within an R chunk!

```
# This is an R chunk
model_r$aic
```

`## [1] 179.2386`

`model_r$params`

```
## Intercept hp
## 30.09886054 -0.06822828
```

I must say that I am very impressed with the `{reticulate}`

package. I think that even if you are

primarily a Python user, this is still very interesting to know in case you need a specific function

from an R package. Just write all your script inside a Python Markdown chunk and then use the R

function you need from an R chunk! Of course there is also a way to use R from Python, a Python library

called `rpy2`

but I am not very familiar with it. From what I read, it seems to be also quite

simple to use.

Hope you enjoyed! If you found this blog post useful, you might want to follow

me on twitter for blog post updates and

buy me an espresso or paypal.me.

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