One of the most-asked questions from aspiring data scientists is: “What is the best language for data science? R or Python?”
People looking into data science languages are usually confused about which language they should learn first: R or Python. Both are extremely useful for an array of data science applications, including Natural Language Processing (NLP). To understand the strengths and weaknesses of each, let’s explore R vs Python for data science by analyzing which language works best with NLP.
Natural language processing: Teaching computers human words
Natural language processing means, as the name implies, teaching computers to process natural human languages (English, Hindi, etc.) and perform analyses. NLP can be used on written text or speech data.
For our example, we will use written text for our comparison of R vs Python for data science. We are surrounded by written text every day: emails, SMS messages, webpages, books, and much more. Text data plays a vital role in our day-to-day life, which makes NLP a very important area for data scientists to explore.
R vs Python for data science: Digging into the differences
Python and R are two of the top data science languages. Both are open-source and have large user bases. In the real world, it’s often difficult to choose between R and Python for data science and NLP. Here, we’re going to run through some of the must-know info about each of these versatile languages.
R: Analytics powerhouse
R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. It’s quite popular for its visualizations: charts, graphs, pictures, and various plots. These visualizations are useful for helping people visualize and understand trends, outliers, and patterns in data.
Python: Versatile workhorse
Python is a general-purpose, robust, versatile language with readable syntax. Python’s readable syntax makes it easy to learn and understand, since it can be read much like a human language. Python also integrates well in a variety of different project environments.
Libraries for NLP
Libraries are collections of modules and functions that programmers can include in programs and projects to accomplish specific tasks. Programmers choose different libraries because they help do a particular task more efficiently. For example, the wordcloud library is used to create a word cloud displaying the most frequently used words from a text dataset. We’ll actually do this later in this article.
R libraries
R boasts more than 10,000 libraries, such as Caret, Dplyr, tidyr, caTools, ggplot2, and many others. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. Some of the libraries used for NLP are: tm, tidytext, text2vec, and wordcloud. Again, the library you use will be based on your use case. Check out this page from the R Project for a detailed look at the other libraries used for NLP.
Python libraries
Python has 200+ standard libraries and nearly infinite third-party libraries. Some standard Python libraries are Pandas, Numpy, Scikit-Learn, SciPy, and Matplotlib. These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling. Libraries used for NLP are: NLTK, gensim, SpaCy, glove, and Scikit-Learn.Every library has its own purpose and benefits. For instance, NLTK is excellent for learning and exploring NLP concepts, but it is not meant for production. SpaCy, meanwhile, is a new NLP library that’s designed to be fast and production-ready.
Data exploration in R and Python
Data exploration is the initial step in data analysis, yielding visualizations like charts or plots that show human users patterns and trends. For our NLP demo, let’s take a dataset of commonly used words from Kaggle and do some data exploration on it in both data science languages.
Loading data in both R and Python
First, let’s load training data in both Python and R and check how much time it takes each language.
R code:
#Load data
start_time <- Sys.time()
train<- read.csv("train.csv")
timediff<-difftime(Sys.time(),start_time)
cat("Time taken to load csv is: ",timediff, units(timediff))Output:
The time taken to load the csv is 3.038562 minutes.
Python code:
# Load Data
start_time = time.time()
tr_data=pd.read_csv("train.csv")
time_diff=time.time()-start_time
print("Time taken to load csv is: {} seconds ".format(time_diff))Output:
The time taken to load the CSV is 17.733536 seconds.
It takes significantly less time for Python to load the CSV than for R to load the same dataset.