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 me on twitter for blog post updates and buy me an espresso or paypal.me.

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