Combining the power of R and Python with reticulate

August 26, 2019
By

[This article was first published on r-bloggers on Programming with R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

R + Py

In the word of R vs Python fights, This is a simple (could be called, naive as well) attempt to show how we can combine the power of Python with R and create a new superpower.

 jack-jacc_Parr.jpg Like this one, If you have watched The Incredibles before!

About this Dataset

This dataset contains a bunch of tweet that came with this tag #JustDoIt after Nike released the ad campaign with Colin Kaepernick that turned controversial.

Dataset source: https://www.kaggle.com/eliasdabbas/5000-justdoit-tweets-dataset

Superstar – Reticulate

The superstar who’s making this possible is the R package reticulate by RStudio.

Let us start with the code!!

The R Code

#loading required R libraries 
library(tidyverse)
library(ggthemes)
library(knitr)
tweets <- read_csv("https://raw.githubusercontent.com/amrrs/python_plus_r_brug/master/justdoit_tweets_2018_09_07_2.csv")
text <- tweets$tweet_full_text
set.seed(123)
text_10 <- text[sample(1:nrow(tweets),100)]

The Python Code

import spacy
import pandas as pd
nlp = spacy.load('en_core_web_sm')
doc = nlp(str(r.text_10))
pos_df = pd.DataFrame(columns = ["text","pos","lemma"])
for token in doc:
    df1 = pd.DataFrame({"text" : token.text, "pos" : token.pos_, "lemma" : token.lemma_}, index = [0])
    #print(token.text, token.pos_)
    #print(df1)
    pos_df = pd.concat([pos_df,df1])
#print(pos_df) 

Now, Again The R Code

#data.frame(token = as.vector(py$tokens)) %>% count(token) %>% arrange(desc(n))
py$pos_df %>% 
  count(pos) %>% 
  ggplot() + geom_bar(aes(pos,n), stat = "identity") +
  coord_flip() +
  theme_minimal() +
  labs(title = "POS Tagging",
       subtitle = "NLP using Python space - Graphics using R ggplot2")

Now, Again The Python Code

ent_df = pd.DataFrame(columns = ["text","label"])
for ent in doc.ents:
    df1 = pd.DataFrame({"text" : ent.text,   "label" : ent.label_}, index = [0])
    #print(token.text, token.pos_)
    #print(df1)
    ent_df = pd.concat([ent_df,df1])

One Final Time, The R Code

py$ent_df %>% 
  count(label) %>% 
  ggplot() + geom_bar(aes(label,n), stat = "identity") +
  coord_flip() +
  #theme_solarized() +
  theme_fivethirtyeight() +
  labs(title = "Entity Recognition",
       subtitle = "NLP using Python space - Graphics using R ggplot2")

Summary

Thus, In this post we learnt how to combine the best of R and Python – in this case – R for Data Analysis and Data Visualization – Python for Natural Languge Processing with Spacy.

If you liked this, Please subscribe to my Language-agnostic Data Science Newsletter and also share it with your friends!

To leave a comment for the author, please follow the link and comment on their blog: r-bloggers on Programming with R.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)