Python and R are both open-source programming languages in the computer field having a very large community. Both are known to have libraries as well as tools that are present inside their catalog, R being outsourcing some libraries as well. The user mainly uses R for statistical analysis, business analytics, web scraping, etc. whereas python is used to give a wider approach to data science terminologies such as data mining, artificial intelligence, web development, machine learning, etc.
R is not just a computer programming language but also an environment that provides statistical analysis and representation of graphics along with the facility of reporting. The founders of this language are Ross Ihaka and Robert Gentleman and they did their research at The Auckland University in New Zealand. The name of the langue R was also kept after the founder Ross’s initials. Now, this is handed over to the R Development Core Team who takes care of its development as well. R is free as well as open-source and is available under the various versions provided for various operating systems like Mac, MS, Linux, etc, however, it is comfortably used with R studio to work on.
The users mainly use the R language for cleaning and preparing the data. It also comes into use for creating visualizations.
Let us see a basic program in R language:
"Welcome to the Python v/s R tutorial"
10 + 10
The output of the R program:
 Welcome to the Python v/s R tutorial
Python is a computer programming language that was designed by Guido van Rossum. The release date of python is 1991 and is majorly used in web development, scripting systems, mathematics, and the development of software. The programmers use python language along with the software which creates workflows. We also use python for handling work-related big data and for performing complex mathematical problems. Along with this, Python comes into use for the fast prototyping and development of software.
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Python works as both procedural and object-oriented language for users. It is free as well as open-source and works well with various operating systems like Mac, MS, Linux, etc. Python is always chosen because of its feature of readability as it has a lot of similarities with English with a hint of maths.
Let us see a basic program in Python language:
print("Welcome to python v/s R tutorial")
Output of the python program:
Welcome to python v/s R tutorial
When we talk about these two languages, there’s no one language that is right or wrong in learning. Both go hand in hand depending on the requirement of the data purist or the programmer. Both of these languages are competitive and equally lying in demand when we talk about IT companies in affiliation to web development. Let us discuss these languages in depth along with the key points one should focus on before making the choice between R and python. Which one is better for you will ultimately come down to your background, interests, and career goals.
While we talk about data analysis as well as the collection, python is known to be supportive of almost every format of data whether it is a CSV file or a JSON file from the web. A user is able to directly import the SQL tables into python and embed them. Python has in-built libraries which let the user take the data from the internet for the development or building up datasets.
However, when we talk about R language, it is created in such a manner that it needs to import data from Excel or in the form of text files to analyze data. The user is able to turn the files that are present in Minitab and SPSS format into R data frames. While Python is known for web development and retrieving data from the internet, R is more versatile and better designed for web scraping with the use of packages such as Rvest.
The user is able to explore the data in python using a library for data analysis known as ‘Pandas’. With this library, the user can filter, sort as well as display the data within no period of time.
However, while working with the R language, it is well known for optimization of statistical analysis for bigger datasets. It has various options for exploring the data. The user can easily build applications like probability distribution systems and can perform statistical tests with the help of machine learning or techniques related to data mining.
Python has in-built libraries like Numpy, SciPy, sci-kit-learn, etc to perform processes such as data modeling.
However, for data modeling in R, the user cannot fully depend on R libraries. He has to import packages outside the language to perform various functionalities. There are some packages such as Tidyverse which make it really possible to import the packages, manipulate and visualize them and also help in reporting the data.
There is a library called Matplotlib used for generating usual graphs as well as charts in python. This is because visualization is not actually a pro while using python language. Similarly, there is a library called Seaborn which allows the user to draw not just attractive but also creates very informative graphics for Python.
Whereas, in R language, the user can build it to showcase the outputs he gets from the statistical analysis. Here, the graphics module can simply help to create the graphs and charts with the help of the ggplot2 library.
Python is very easy to use and read as compared to the R language. Hence its learning curve is pretty linear as well as smooth. It’s considered one of the most flexible languages for the users who begin to work with python. However, the R language is majorly used for data analysis tasks and web scraping. Since it is a more complex language than python due to its advanced functionalities, R is pretty difficult when it comes to development.
R language has a little steeper learning curve than python majorly at the beginning of the learning however once the user gets the hold of this language and its features, he is good to go.
When we talk about team spirit in a company, one should always focus on the similar language as his team because then the interests are common and can be shared. It is always a good idea to share code with and take an initiative in collaborating on the company projects.
R language has tools that are very statistical and generally used by academic holders, people who work with engineering tasks, or even scientists who do not have any programming skills. Python is always ready for production and is used in industries for research purposes as well as engineering work. There are a lot of tools which support both R language and Python-like (MMLS) Microsoft Machine Learning Server. This is the reason why a lot of organizations require a combination of these 2 languages. Moreover, at an early stage of data analysis, it was concluded that exploring data in both python and R goes hand in hand as data exploration is done in R and then it is later switched to Python when there is a need to process those data products.
As we have also discussed earlier, programming in R is more appropriate for statistical learning by having various libraries for exploring the data and performing experiments on them. Whereas, python is the number one choice when it comes to performing processes via machine learning on various large-scale applications. These applications may be like wide data analysis for web development applications. A user is able to directly import the SQL tables into python and embed them. Python has in-built libraries which let the user grab data from the web for the development or building up datasets Whereas R language needs to source its libraries from outside for development.
Python is considered one of the most flexible languages for the users who begin to work with python. Hence, if one is focusing on tasks such as web scraping, data visualization, mathematical calculations, etc. then he should opt for the R language. But for tasks such as big data analysis, machine learning, artificial intelligence, deep learning techniques, etc. Python is the right fit.
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It is a dilemma to make the right choice between these two languages. Below are the key points that one should take care of before choosing between R and python:
As we talk about the pros and cons of these languages, both are excellent to work with. Both are appropriate for beginners who do not have any experience in coding and can best start their career or study with. There is a wide range of resources available on the internet, a beginner may choose any language.
Let us see a few options here which will give you an idea of how to move on with python or R:
Data analysis works with two main professional courses Google Data Analytics Professional Course which makes the learner understand the basics of R language and the other course is IBM Data Analyst Professional Certificate which focuses on teaching python. Both work in such a manner that it's easy to complete them in under 6 months.
If a learner wants to pursue just one language at the moment, or if he is just focusing on adding another language to his pre-existing skill set, he can take up a course in either python or R depending on the requirements we have discussed in the topic above. There are a lot of courses going on for both from time to time where a learner can enroll himself.
In case the confusion still persists, a learner can opt to learn both Python and R languages. There are online courses that provide a basic introduction to the languages in less than two hours without even buying the course. That way a beginner won't have to spend that extra money on a course he wouldn't choose in the future and will also be able to finally make up his mind.
Top 30 frequently asked Python Interview Questions!
Python is a way better choice than R as it is an all-rounder language. However, if a person wants to focus on just statistical methods or techniques related to maths, he should opt for R language.
Python is known to have a smooth learning curve as compared to R as R has a steeper learning curve when compared python. Python is easier to read and understand. This is one of the reasons Python is a more popular language than R.
Both these languages come into play when we talk about machine learning, data analytics or data exploration. However, both have their own perks on topic specifications when we know these languages in depth.
Yes, python is a more popular language than R due to its ability to be easy as well as multi-structured.
Yes. it is absolutely possible to do anything in python that R can perform. It is also stated that python code would be shorter and easier to understand than R.
Yes. one should learn R even if they know python as the R language comes with additional benefits when one wants to work with statistical analysis. This idea will bring more versatility when it comes to data analysis.
Python could be the best tool for machine learning, data mining, or artificial intelligence, however, it is definitely not the best when it comes to business analytics. R focuses on academic people, data purists, data scientists, etc., and is focused to solve data-related issues. Hence, R is a better option here.
When we talk about data science and computers, Python is the language that stands out as the appropriate coding language for performing data analysis and web development processes on computers. However, R is on its own when it comes to pros. R language masters the programming when it comes to tasks like web scraping. In this article, we have discussed a lot of points where the pros and cons of both languages are discussed in detail. To elaborate, we have discussed how python and R vary in terms of company, their learning curve, and differences in their approach to exploring data, analyzing data, modeling data, and reporting it as well as visualizing data.
2. Python Ogre
3. Python IDEs
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