# R or Python? Why not both? Using Anaconda Python within R with {reticulate}

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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))

## <class 'pandas.core.frame.DataFrame'>

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

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