R vs Python is one of the most common but important questions asked by lots of data science students. We know that R and Python both are open source programming languages. Both of these languages are having a large community. Apart from that, these languages are developing continuously.

That’s the reason these languages add new libraries and tools in their catalog. The major purpose of using R is for statistical analysis, while Python provides a more general approach to data science.

Both of the languages are state of the art programming language for data science. Python is one of the simplest programming languages in terms of its syntax.

That’s why any beginner in a programming language can learn Python without putting extra efforts. On the other hand, R is built by statisticians that are a little bit hard to master.

Now we have read some basic differences between R vs Python. But this is not the end of the difference between these two languages. There is a lot more to learn about the comparison between R vs Python. Here we go:-

R

R is one of the oldest programming language developed by academics and statisticians. R comes into existence in the year 1995. Now R is providing the richest ecosystem for data analysis.

The R programming language is full of libraries. There are a couple of repositories also available with R. In fact, CRAN has around 12000 packages. The wide variety of libraries makes it the first choice for statistical analysis and analytical work.

Consists of packages for almost any statistical application one can think of. CRAN currently hosts more than 10k packages.

Equipped with excellent visualization libraries like ggplot2.

Capable of standalone analyses.

Python

On the other hand, Python can do the same tasks as the R programming language does. The major features of Python are data wrangling, engineering, web scraping, and so on. Python also has the tools that help in implementing machine learning on a large scale.

Guido van Rossum developed Python in 1991. Python is the most popular programming language in the world. Python is one of the simplest languages to maintain, and it is more robust than R. Now a day Python has the cutting edge API. This API is quite helpful in machine learning and AI.

Most of the data scientist uses only five Python libraries i.e., Numpy, Pandas, Scipy, Scikit-learn, and Seaborn. It is quite handy to use Python over R.

Object-oriented language

General Purpose

Has a lot of extensions and incredible community support

Simple and easy to understand and learn

Packages like pandas, NumPy, and sci-kit-learn, make Python an excellent choice for machine learning activities.

R or Python Usage

Python has the most potent libraries for math, statistic, artificial intelligence, and machine learning. But still, Python is not useful for econometrics and communication, and also for business analytics.

On the other hand, R is developed by academics and scientists. It is specially designed for machine learning and data science. R has the most potent communication libraries that are quite helpful in data science. In addition, R is equipped with many packages that are used to perform the data mining and time series analysis.

Why should we not use both of these languages at the same time?

Heaps of people think that they can use both the programming languages at the same time. But we should prevent using them at the same time. The majority of people are using only one of these programming languages. But they always want to have access to the capability of the language adversary.

For example, if you use both languages at the same time, that may face some of the problems. If you use R and you want to perform some object-oriented function, then you can’t use it on R.

On the other hand, Python is not suitable for statistical distributions. So that they should not use both the language at the same time, because there is a mismatch of their functions.

But there are some ways that will help you to use both of these languages with one another. We will talk about them in our next blog. Let’s have a look at the comparison between R vs Python.

R vs Python Programming Paradigms

R is more functional. It provides a variety of functions to the data scientist i.e., Im, predicts, and so on. Most of the work done by functions in R. On the other hand, Python uses classes to perform any task within Python.

R vs Python Packages

R provides the build-in data analysis for summary statistics, and it is supported by summary built-in functions in R. But on the other hand, we have to import the stats model packages in Python to use this function. Besides, there is also a built in the constructor in R i.e., is the data frame.

On the other hand, we have to import it in Python. Python also helps to do linear regression, random forests with its sci-kit learn package. As mentioned above, it also offers API for machine learning and AI. On the other hand, R is having an enormous diversity of packages.

R vs Python Ecosystem

R was created as a statistical language, and it shows. statsmodels in Python and other packages provide decent coverage for statistical methods, but the R ecosystem is far more extensive.

It’s usually more straightforward to do non-statistical tasks in Python. With well-placed libraries like beautifulsoup and request, web scraping in Python is much easier than R. This applies to other tasks that we don’t see closely, such as saving the database, deploying the Web server.

R vs Python Data analysis workflow

R and Python are the clearest points of inspiration between the two (pandas were inspired by the Dataframe R Dataframe, the rvest package was inspired by the Sundersaute), and the two ecosystems are getting stronger. It may be noted that the syntax and approach for many common tasks in both languages are the same.

R vs Python for statistics

As I have mentioned earlier that R has been developed and the academic experts and statisticians. Therefore it is the best-suited language for statistics. You can perform almost every function and method of statistics using R. it is the best programming language for statistical analysis.

On the other hand, Python is not that user friendly for statistics. You can’t do statistical analysis with Python. But it is well suitable to perform statistics function that is widely used in data science. In this battle, R has a slight edge over Python.

R or Python easier to learn

When it comes to the learning curve of these languages, then R is quite hard to learn for the beginners. It requires lots of effort to start with R., But once you start with it, then you can polish your R programming skills with the help of its developer community. Apart from that, if you have the basic knowledge of programming, then you may not found it that much difficult.

On the other hand, Python is one of the simplest programming languages with clean syntax. You can start with Python quickly if you have the basic knowledge of programming, then you will find it the most straightforward programming language. And you will have a good command over it in less time. But if you are a beginner in programming, then it takes less time than R to learn Python. It also has a large community that will help you to clear all your doubts.

R vs Python data science

It is the point that is more likely to read by the data scientist that which is better between r vs Python for data science. Both of these programming languages are playing their crucial role in the field of data science. Both of these languages have almost the same impact on data science. You can find nearly all the packages in R that are useful in data science. You can perform various data science tasks seamlessly with R.

On the other hand, Python all has all the modules that make the seamless flow in data science. It also offers lots of packages and libraries that make the data science process quite easier. That is why most of the data scientists are using Python for data science. It also works seamlessly with Hadoop and other data warehouses. But Microsoft shocked the entire world to use R for their Big data need. That’s why there is no clear winner of r vs Python in data science.

R vs Python for machine learning

R is not a well-suited language for machine learning. Machine learning requires lots of packages and modules to work seamlessly. R is a traditional language, and it is not able to fulfill the requirements of machine learning technologies.

On the other hand, Python is well suited for machine learning. It can work seamlessly with machine learning algorithms. You can create, modify, and append machine learning algorithms easily with Python. You love to implement machine learning with Python. It has a well-crafted library for machine learning. So in this battle of r vs python machine learning, Python is the clear winner.

R vs Python syntax

There is a lot of difference between R and Python Syntax. Here we go with R basic Syntax:-

myString <- “variable”

print ( myString)

myString <- “variable”

Python Syntax

print “variable”

R vs Python popularity

R is not a popular language anymore; it is not even in the top 10 list of IEEE Spectrum ranking. The popularity of R is decreasing with every passing year.

On the other hand, in the IEEE Spectrum ranking, Python is the number 1 programming language in the world. The reason is the vast use of Python in data science and big data technologies. And it is also widely used in machine learning and artificial intelligence technologies.

R vs Python performance

R is slightly faster than Python to perform a variety of tasks. That is the reason most of the data science professionals are more likely to use R over Python. You can complete most of the functions almost half the time as compared with Python.

On the other hand, Python is one of the slowest programming languages in the world. It takes plenty of time to perform the same tasks that its competitors do much faster. But if we talk about the overall performance than Python is still the first choice.

R vs Python deep learning

R is not well suited for deep learning technology because deep learning requires lots of modules and packages to work seamlessly.

On the other hand, Python is best for machine learning. Python offers the best programming modules and packages that fulfill all the requirements of advanced technologies i.e., deep learning. Python will never disappoint you with deep learning.

R vs Python salary

R developers earn somewhere between 50k$ to 80k$ per annum. On the other hand, Python developers earn more than 100$ per annum. In this comparison, Python is the clear winner.

R vs Python for data analysis

As I have mentioned earlier, that R is well suited for statistics analysis; therefore, it is also the best option for data analysis. On the other hand, it requires lots of effort to perform data analysis tasks with Python. In this battle R is the winner.

R vs python visualization

Both of these languages are best for data visualization. But it is quite easy to implement data visualization techniques in R with the help of ggplot2.

On the other hand, Python offers Matplotlib to implement data visualization, which is quite slower. But the use of the Seaborn library is trying to overcome this problem in Python.

R vs python speed

Although both these programming languages are used to analyze the large data, if one compares the performance of this, python is better as compared to the R language.

When one writes a program, and it has a number of iterations that are less than 1000, then the python would be the best in terms of speed. For below 100 iterations, python could be 8 times faster than the R, but if you have more than 1000, then R might be better than python.

R vs Python for finance

Might you think that is R or python better for finance? Well, we can say that if you have a finance team or you are working in an accounting firm, a bank, or consulting, then one can easily compare these coding languages. R is better for writing customized functions, statistical applications, and it has standard libraries that can be utilized for statistical work. On the other side, python has its own standard libraries that are built for computations, with some extension of matrix algebra and natural language.

Besides this, natural language processing in R programs is also possible. Overall, a manger can prefer some of the criteria for R vs Python as developmental potential, team familiarities, open-source support, or external communities, the last but not the least technical power for standard libraries. All these points are reasonable to concentrate team not only on the goods but also helps to earn profit for the large companies.

Lets Sum Up R vs Python

You might still think about should I learn R or Python? Well, it depends on you that for which purpose you want to learn a new programming language. R is used for the data science projects, whereas Python has a wide variety of uses, and it has its own libraries for different uses.

R has so kind of complicated syntax that is sometimes not easily understandable, but R has a plotting library that is easy to use. On the other hand, Python has a number of accessible sources and communities that are comparatively larger than that of the R coding language. So, we can say that both have their own utilization, select any of these programming languages as per your requirements.

Other than this, you have got a detailed comparison of R vs Python. Both of these languages are having their strengths and weaknesses. You can use either one for data analysis and data science.

Both of these languages are having a similarity in terms of their syntax and approach. You can pick any one of them, and no one will let you down. Now you may come to know the fundamental strengths of these languages over each other.Now you may be more confident to choose the best one as per your needs. If you are the students of R programming language, then you can get the best R programming assignment help or R programming homework help from our experts.