If you are an individual who is interested in or a working professional in Data Analytics or Data Science, you are probably familiar with the R vs. Python debate. However, these two languages are evolving and bringing the future to life via machine learning, artificial intelligence, and data-driven innovation.
The two open-source languages are
pretty similar in many aspects. Both languages are free to download and use for
data science tasks ranging from data manipulation and automation to business
analysis and ample data research. The critical distinction is that Python is a
general-purpose programming language, whereas R is a statistical analysis
programming language. The challenge is increasingly becoming to create the best
use of both programming languages for your use cases, rather than deciding
which to choose. But, in this blog, let us see the differences between R and
Python.
Python
Python is an interpreted
general-purpose, high-level programming language. Its design philosophy
emphasizes readability with its use of significant indentation. Its language
constructs and object-oriented approach make it easier for programmers to
create clear, logical code for small and large projects alike.
Python is used for machine learning,
data analytics, and web and software development.
Several Python libraries are available to help with data science activities, including the ones listed below:
- NumPy is a popular Python library that deals with big-dimensional arrays.
- Pandas is a library that may be used to manipulate and analyze data.
- Matplotlib is also a library for creating data visualizations.
R
R is a statistical programming
language created by statisticians Ross Ihaka and Robert Gentleman. Data miners
and statisticians use it to analyze data and construct statistical software.
R is a computer language and
environment for statistical computation, data analysis, and scientific
research. It is one of the most widely used languages for retrieving, cleaning,
analyzing, visualizing, and presenting data by statisticians, researchers, data
analysts, and marketers.
You can use R while
- Loading datasets
- Scraping webpages
- Building REST APIs
- Analyzing Data and Showing Statistical Summaries
- Visualizing Data
- Training a Machine Learning Model
- Developing Simple Web Applications
Now let's see the difference between
R and Python.
R Vs Python
Performance and speed: Both
languages are employed in big data analytics. On the other hand, Python is a
superior alternative for developing critical but quick applications in terms of
performance. Many people believe that R
can be a little slower than Python, but it's still fast enough for massive data
sets.
Data modeling: NumPy for
numerical modeling analysis, SciPy for scientific computing and calculations,
and sci-kit-learn for machine learning techniques are all standard Python
libraries for data modeling. You may need to use packages outside of R's core
functionality for specific modeling analysis in R. However, the Tidyverse, a
collection of packages, makes it simple to import, manipulate, analyze, and
report on data.
Unstructured Data: Unstructured
data makes up 80% of the world's data. The majority of the data created by
social media is unstructured. Python has packages such as NLTK, scikit-image, and
PyPI to analyze unstructured data. R has modules for analyzing unstructured
data, although the support isn't quite as good as Python's. Both languages,
however, can be used to analyze unstructured data.
Exploring data: Pandas, the
Python data analysis library, can be used to examine data in Python. In a
couple of seconds, you can filter, sort, and display data. On the other hand, R
is designed for the statistical analysis of massive datasets and provides
various data exploration tools. You can use R to create probability
distributions, perform statistical tests, and do conventional machine learning
and data mining tasks.
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