The data science community has come a long way and has matured a lot in the last 5 years. Earlier, the IT sector used to place a lot of emphasis on technologies like Java which includes Spring and Hibernate for writing and testing code. Following the advent of machine learning and data analytics, the focus shifted to technologies such as R, Python, and SAS. These technologies are being constantly deployed for algorithms in machine learning, deep learning, artificial intelligence and much more cutting-edge discoveries that have taken the world and visionaries by surprise.
What is R?
R is an open-source programming language mostly used by statisticians and data engineers who utilize it to build various algorithms and techniques for statistical modeling and data analysis. It first came into the picture in August 1993. R includes a ton of inbuilt libraries that offer a wide variety of statistical and graphical techniques which include regression analysis, statistical tests, classification models, clustering and time-series analysis. Tons of packages are already available and they help in exploratory data analysis, basic data exploration and data representation in the form of graphs. It has the capability of creating some powerful charts and dashboard quality graphs to demonstrate and monitor the monthly sales or profit of a company. R language runs on the R Studio platform which helps in initiating and executing codes and packages in R.
What is Python?
Similar to R, Python also is an open-source programming language deployed for statistical and machine learning models like regression and classification which is employed in many systems. Created in 1991, it was used and implemented in simple systems at the time when machine learning had a niche market. Python has the capability of supporting object-oriented, procedural and functional programming. It comprises of a comprehensive collection of libraries which help in taking care of the minor and major implementations in Python. There are many tools and frameworks used for Python but the most popular one is known as the Jupyter, which takes care of both algorithms and data visualization.
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Why Do We Use Them and Their Applications?
Both R and Python are the current torch-bearers of machine learning brigade. Both these languages can be used to initiate and modify algorithms in machine learning like classification, regression, clustering, neural networks and algorithms in artificial intelligence. Both of these languages have a different approach toward solving a given problem. Whether building a machine learning model or exploring the data to get some useful insights, both play an important role in understanding the data and extract facts from them.
Creating meaningful dashboards
Dashboards have nowadays become the bread and butter for the majority of team meetings. These dashboards can be created either for measuring sales or for tracking the current status of a company. It has proved useful and incremental in enhancing the performance metrics for any brand or company. Tools like Tableau and PowerBI are quite popular and at the same time, R and Python do have the capability of building enterprise-grade dashboards due to the large selection of packages. R has the packages-dashboard, ggplot2 and plotly which facilitates the data to pop-out in the form of graphs and charts. Python includes matplotlib, seaborn and pandas to look after the visualization and data representation part for the data. The last couple of years saw a lot of enhancements and improvements in both the user bases and since then they have continuously supported the languages to get the best of both the worlds.
Making machines smarter and sharper
There was a time when machines had limited capability and were programmed for some predetermined set of activities which it would repeat every time for performing a particular set of tasks. This monotony has been taken care of now and machines have become more intelligent and picky after the advent of machine learning. This has made them more aware and confident of their actions and has enabled them to perform actions that they learn during the hit and trial process. This unlocks new functions and possibilities for companies who can explore various components of their business without hassles.
Shaking the market with artificial intelligence
All the machine learning performed to date culminated into a broader concept which we refer to as artificial intelligence. Artificial intelligence basically involves machines learning and tries to develop algorithms that can enable the machines to think like humans. This by far has been the most dreaded and earth-shattering concept in the industry which has made it quite clear that AI is the definite future. All companies are slowly deploying AI in their systems in order to make them more efficient and dynamic.
R vs Python: Important Differences, Features
Popularity among masses due to higher employment opportunities
The above graph signifies that Python (indicated by the yellow curve) is more popular and widely employed in systems and businesses. The curve in blue belongs to R which definitely looks good and is almost on-par with Python. There are immense opportunities in R in the industry but Python wins the popularity contest.
Library repository
R has an exhaustive repository of libraries that are regularly updated in CRAN which is Comprehensive R Archive Network. It includes dplyr, mice, ggplot2 and many more. Python includes a pip package index that has all the libraries for Python. They include matplotlib, pandas, seaborn and many more. R has a more active user base which contributes to updating its huge database of 10,000 packages. Even Python includes a user community that updates its packages regularly but it is a bit complicated process compared to R.
Data handling
R has the power of doing data analysis without loading any package in its memory. There are many packages that are available in R that can be utilized to study data and get insights from them. In python, we need packages like numpy and pandas for creating a data frame and processing the data. Both languages are strong enough to carry out data computations and tasks which make it easy for users to make the most of data.
Flexibility
The uniqueness of each language makes it unique in some aspects when used in machine learning and deep learning algorithms. Both R and Python have libraries to implement algorithms. But the catch here is even though R is easy to learn and master, Python will always remain one step ahead due to a large number of applications and real-world implementations. Time and again, developers have been reliant on its superpowers and the simplicity it offers for executing any piece of code.
Graphics and Visualization
R has several advanced graphics packages, for example, ggplot2, for customizing graphs. Python also has some amazing visualization libraries, for example, seaborn and bokeh. As compared to R, Python has many more libraries for graphics and visualization, however, they are slightly more complex.
Support for Deep Learning
R has two new deep learning packages -- KerasR and RStudio’s Keras. Both of these packages provide an R interface to the deep learning package of Python.
Python has Tensorflow and Keras, which are amazing deep learning libraries. Learning Keras is among the easiest ways to start with deep learning in Python.
Customer Support
Both R and Python don’t have any customer service support. This means that if you run into any trouble, you’re on your own. However, both R and Python have online communities for help. Since Python is older than R, it has greater community support as compared to R.
Pros and Cons
The demand for both of these languages is increasing and the salaries offered are also pretty good. In this section, we’ll learn about some pros and cons of both languages so as to help you choose between them better!
Python: Pros
- Python is a general-purpose language. It is very easy and intuitive. The learning curve is not very steep and you can write programs quickly.
- IPython Notebook is a very nice interactive computational environment that makes it a lot easier to work with Python. These notebooks can be easily shared and used without the need to install anything.
Python: Cons
- Python has many nice visualization libraries, for example, Pygal, Bokeh, and Seaborn. However, it becomes slightly difficult to choose from the vast range of options. Moreover, as compared to R, these libraries produce complex visualizations which may not be very pleasing to look at.
R: Pros
- R has a rich ecosystem of an active community and cutting-edge packages. You can find all the R packages in the R documentation.
- R has amazing visualization packages that produce wonderful visualizations. Some of these packages are ggplot2, rCharts, ggvis, and googleVis.
R: Cons
- The learning curve for R is very steep. The available documentation is not necessarily user-friendly and things such as finding packages can also be very time-consuming if you’re not familiar with it beforehand.
R vs Python: Head-to-Head Comparison
|
Parameters |
R |
Python |
|
Scope |
Used majorly for statistical computing and graphics |
General-purpose, including task automation, data analysis, and data visualization |
|
Syntax Complexity |
Complex |
Easy |
|
Learning Curve |
High |
Low |
|
Package Distribution |
CRAN (Comprehensive R Archive Network) |
PyPI (Python Package Index) |
|
Deep Learning Libraries |
KerasR and RStudio’s Keras |
Tensorflow and Keras |
|
Statistical Packages |
Powerful |
Less powerful |
|
IDEs |
R Commander, Rkward, and Rstudio |
Spyder, Atom, and Eclipse+Pydev |
|
Graphics and Visualization Package |
Ggplot, plotly, and |
seaborn and bokeh |
|
Data Modeling Libraries |
Tidyverse |
NumPy, SciPy, and Scikit-Learn |
|
Data Handling |
Does not require to load packages |
Requires packages |
Conclusion
Startups and MNCs are reliant on these technologies to expand and leave a mark in the industry. A huge number of coders and developers are actively learning and practising these two trending languages. Anyone looking to enhance their business value surely knows the value of machine learning in the market and is ready to invest a huge capital for the same. It is difficult to make a conclusive statement on which language is "better", as both of these languages are in great demand and can be used to solve many unique problems.
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