Setup

reticulate has a default approach to instruct R where to find python, which environment and version to use. There are three approaches to manually configure this.

1. virtualenv where you specify the directory of python virtual environment

2. use_python where you specify the path where your ‘python’ resides.

3. use_condaenv where you specify the name of the specific Conda environment to use. You can access the name(s) of the available environments via conda_list()[[1]]

conda_list()[[1]] %>% use_condaenv()

Let’s check which python version, environment and configuration has been bind to this R session.

py_config() #not run to maintain privacy

Running python

Amend {r} in your code chunk to {python} to run python code. For this post, I will add #{python/r} in my code chunks to make it explicit that I ran the code as a python or a r code chunk.

#{python}

Plist= [11,22,33,44,55,66]

print(Plist)
## [11, 22, 33, 44, 55, 66]
#{python}

def Psq_fun (x):
  value= x*x 
  return(value)
  
print(Psq_fun(9))
## 81

Alternatively, you can execute python scripts in your r chunks using the function py_run_string. You will need to wrap your python scripts within the quotation marks “”.

#{r}

py_run_string("def Pten (x):
          value= x*10
          return(value)")

Let’s run the above in a python code chunk.

#{python}

Pten(2)
## 20

Do note that python objects/functions are not explicitly displayed in your global environment (you are after all working in an R global environment by default).

#{r}

ls()
## character(0)

Nevertheless, be assured that you can still access them in future python and r chunks.

#{python}

print(Psq_fun(9))
## 81
#{python}

print(Plist)
## [11, 22, 33, 44, 55, 66]

Accessing python

You can access previously ran python functions/objects in your r chunks using the prefix py combined with the R’s dollar sign syntax $.

#{r}

py$Psq_fun(4)
## [1] 16
#{r}

py$Plist
## [1] 11 22 33 44 55 66

Alternatively, you can directly evaluate previous python objects/functions in r chunks using the py_eval function.

#{r}

py_eval("Plist")
## [1] 11 22 33 44 55 66
#{r}

py_eval("Psq_fun(2)")
## [1] 4

Accessing R

Likewise, you can access R objects/functions in your python chunks using the prefix r combined with a punctuation mark .

#{r}

(Rvec<-c(11,22,33,44,55,66))
## [1] 11 22 33 44 55 66
#{python}

r.Rvec
## [11.0, 22.0, 33.0, 44.0, 55.0, 66.0]
#{r}

Rroot_fun<-function(x){
  value= x^.5
  print(value)
  }
#{python}

r.Rroot_fun(81)
## 9.0

Converting objects between languages

Besides accessing a R object in python via r., you can convert the R object into a python object while still running R with r_to_py.

Rvec %>% r_to_py() %>% class()
## [1] "python.builtin.list"   "python.builtin.object"

Previously, I mentioned that python objects do not exist in your global R environment when you run the python script directly inside {python} code chunks or with python_run_string. However, when you create python objects in {r} code chunks, the python object is saved in the R environment.

#{r}

# convert R object into python 
Python_in_Renv<- Rvec %>% r_to_py()

# check if the converted object lives in the `R` environment. https://stackoverflow.com/questions/1169248/test-if-a-vector-contains-a-given-element
"Python_in_Renv" %in% ls() 
## [1] TRUE

For python objects living in the R global environment, you can convert it back to a R object with py_to_r.

Python_in_Renv %>% py_to_r() %>% class()
## [1] "numeric"

Layout

At times you may decide to display R and python code side by side to compare which language is superior.

#Start of column partition 
<div class = "row">
  
#Left column
<div class = "col-md-6">
Text: `R` code will be on the left
Chunk of R code ``{r}``
</div>

#Right column
<div class = "col-md-6">
Text: `python` code will be on the right
Chunk of Python code ``{python}``
</div>

# End of column partition
</div>

## Level 2 heading {.tabset}

### Level 3 heading (first tab i.e. left tab)
Text: R code will be on the left tab
Chunk of R code ``{r}``

### Level 3 heading (next tab i.e. right tab)
Text: python code will be on the right tab
Chunk of python code ``{python}``

## 

<style>
 .col2 {
    columns: 2;
  }
</style>

  <div class = "col2">
<br>
Text: R code will be on the left 
Code: ``{r}`` 

<br>
Text: python code will be on the left 
Code: ``{python}`` 
</div>

#{r}

Rroot_fun
## function(x){
##   value= x^.5
##   print(value)
##   }

#{python}

print(Pten)
## <function Pten at 0x0000000021361288>

Resources

The adoption of reticulate in data science projects is endless. For example, Manuel Tilgner used R for data wrangling and pre-processing and python via reticulate to do some prediction. Martin Henze used python again via reticulate to do some prediction and used R’s almighty ggplot to visualize the results. Sean Lopp used reticulate to run some python code to create a Shiny app. If you prefer a video presentation of a little bit of all the above, you can watch Dan Chen’s presentation during the late 2019 Rstats DC Conference.