A tutorial on tidy cross-validation with R
Analyzing NetHack data, part 1: What kills the players
Analyzing NetHack data, part 2: What players kill the most
Building a shiny app to explore historical newspapers: a step-by-step guide
Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 1
Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 2
Curly-Curly, the successor of Bang-Bang
Dealing with heteroskedasticity; regression with robust standard errors using R
Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport
Exporting editable plots from R to Powerpoint: making ggplot2 purrr with officer
Fast food, causality and R packages, part 1
Fast food, causality and R packages, part 2
For posterity: install {xml2} on GNU/Linux distros
Forecasting my weight with R
From webscraping data to releasing it as an R package to share with the world: a full tutorial with data from NetHack
Get text from pdfs or images using OCR: a tutorial with {tesseract} and {magick}
Getting data from pdfs using the pdftools package
Getting the data from the Luxembourguish elections out of Excel
Going from a human readable Excel file to a machine-readable csv with {tidyxl}
Historical newspaper scraping with {tesseract} and R
How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data
Imputing missing values in parallel using {furrr}
Intermittent demand, Croston and Die Hard
Looking into 19th century ads from a Luxembourguish newspaper with R
Making sense of the METS and ALTO XML standards
Manipulate dates easily with {lubridate}
Manipulating strings with the {stringr} package
Maps with pie charts on top of each administrative division: an example with Luxembourg's elections data
Missing data imputation and instrumental variables regression: the tidy approach
Modern R with the tidyverse is available on Leanpub
Objects types and some useful R functions for beginners
Pivoting data frames just got easier thanks to `pivot_wide()` and `pivot_long()`
R or Python? Why not both? Using Anaconda Python within R with {reticulate}
Searching for the optimal hyper-parameters of an ARIMA model in parallel: the tidy gridsearch approach
Some fun with {gganimate}
Split-apply-combine for Maximum Likelihood Estimation of a linear model
Statistical matching, or when one single data source is not enough
The best way to visit Luxembourguish castles is doing data science + combinatorial optimization
The never-ending editor war (?)
The year of the GNU+Linux desktop is upon us: using user ratings of Steam Play compatibility to play around with regex and the tidyverse
Using Data Science to read 10 years of Luxembourguish newspapers from the 19th century
Using a genetic algorithm for the hyperparameter optimization of a SARIMA model
Using cosine similarity to find matching documents: a tutorial using Seneca's letters to his friend Lucilius
Using linear models with binary dependent variables, a simulation study
Using the tidyverse for more than data manipulation: estimating pi with Monte Carlo methods
What hyper-parameters are, and what to do with them; an illustration with ridge regression
{disk.frame} is epic
{pmice}, an experimental package for missing data imputation in parallel using {mice} and {furrr}
Building formulae
Functional peace of mind
Get basic summary statistics for all the variables in a data frame
Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash
Importing 30GB of data into R with sparklyr
Introducing brotools
It's lists all the way down
It's lists all the way down, part 2: We need to go deeper
Keep trying that api call with purrr::possibly()
Lesser known dplyr 0.7* tricks
Lesser known dplyr tricks
Lesser known purrr tricks
Make ggplot2 purrr
Mapping a list of functions to a list of datasets with a list of columns as arguments
Predicting job search by training a random forest on an unbalanced dataset
Teaching the tidyverse to beginners
Why I find tidyeval useful
tidyr::spread() and dplyr::rename_at() in action
Easy peasy STATA-like marginal effects with R
Functional programming and unit testing for data munging with R available on Leanpub
How to use jailbreakr
My free book has a cover!
Work on lists of datasets instead of individual datasets by using functional programming
Method of Simulated Moments with R
New website!
Nonlinear Gmm with R - Example with a logistic regression
Simulated Maximum Likelihood with R
Bootstrapping standard errors for difference-in-differences estimation with R
Careful with tryCatch
Data frame columns as arguments to dplyr functions
Export R output to a file
I've started writing a 'book': Functional programming and unit testing for data munging with R
Introduction to programming econometrics with R
Merge a list of datasets together
Object Oriented Programming with R: An example with a Cournot duopoly
R, R with Atlas, R with OpenBLAS and Revolution R Open: which is fastest?
Read a lot of datasets at once with R
Unit testing with R
Update to Introduction to programming econometrics with R
Using R as a Computer Algebra System with Ryacas

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, 10 Feb 2019 Prob (F-statistic): 1.79e-07
## Time: 00:25:51 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.