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 to their catalog. The major purpose of using R is for statistical analysis, while Python provides a more general data science approach.
Both languages are state-of-the-art programming languages 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 in the extra effort. On the other hand, R is built by statisticians that are a little bit hard to master.
Check the below mentioned stats of user loyalty for Python and R.
Before moving to the difference, let’s look at some stats that help you understand why both programming languages are popular or best to learn.
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:-
What is R?
R is one of the oldest programming languages 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.
What is 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 data scientists use only five Python libraries, i.e., Numpy, Pandas, Scipy, Scikit-learn, and Seaborn.
- 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, statistics, 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 data mining and time series analysis.
Let’s get a brief detail about the history of both programming languages!
- ABC -> Python was Invented (1989 Guido van Rossum) -> Python 2 (2000) came into existence -> Python 3 (2008) modifies.
- Fortran -> S (at Bell Labs) -> R was Invented (1991 by Ross Ihaka and Robert Gentleman) -> R 1.0.0 (2000) came into existence -> R 3.0.2 (2013).
Why should we not use both of these languages at the same time?
Heaps of people think that they can use both 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 languages 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.
What is the difference between R and Python?
- General Parameters
| Parameter | Python | R |
| Objective | Production and Deployment | Statistics and Data analysis |
| Flexibility | Easy to create new and unique models from scratch. Such as optimization and matrix computation. | Easy to practice because of the availability of libraries. |
| The popularity of Both Programming Languages | 21.69% in 2018 | 4.23% in 2018 |
| Integration | Well-integrated with app | Run locally |
| Database size | Handle huge size | Handle huge size |
| Important Packages & library | caret, scikit-learn, pandas, scipy, TensorFlow, | zoo, ggplot2, tidyverse, caret, |
| Primary Users | Programmers & developers | Scholar and R&D |
| Learning curve | Linear & smooth | Difficult at the beginning |
| Average Salary | $100.000/month | $99.000/month |
| Task | Suitable to deploy the algorithm | Easily get primary results |
| IDE | Ipython Notebook, Spyder | Rstudio |
| Advantages | Jupyter notebook: This help to exchange the data among colleaguesSpeedDeploymentMathematical computationCode ReadabilityFunction in Python | Large table for data analysisR helps in creating excellent graphsRMarkdownGitHub interfaceShiny |
| Disadvantages | Do not have enough libraries as R | Dependencies between librarySlow High Learning curve |
- Technical Parameters
| Parameter | Python | R |
| Graphics and Visualization | The facility of graphics and data visualization is not available in Python. | There is an excellent feature of graphics and data visualization in R. |
| Usability | With the help of Python, it is possible to do data manipulation.Python can pull the data to produce a visualization, such as charts and maps from the given Outputs. | R is being used for exploring datasets and ad-hoc analysis.It is more useful with statistics-heavy projects. |
| Usage in machine learning | It is more flexible for machine learning.It is also flexible for data analysis tasks that require it to integrate with web applications. | R is always preferable for working with datasets and rapid prototyping to make the best machine learning models. |
| Usage in statistics | Libraries, such as Ipython, scipy, numpy, etc, makes Python an excellent choice for structured code and higher performances.The Numpy library of Python allows developers to perform statistical functions and advanced mathematical computations. | R has the capability in terms of data visualization, both interactive as well as static.R packages, such as Highcharter, Ggiraph, Plotly enable the interaction among the users and the data. |
| License | Python libraries offer various business-friendly licenses, like MIT and BSD, by which users can share software.BSD and MIT are permissive and simple licenses. | Libraries such as GPL-3, GPL-2 are “copy-left” licenses.The license distribution is not easy in R. |
| Popularity | Python programming is getting popular day by day, and it is included in the Top 5 programming languages to learn. | R is also a popular programming language, but it is not as popular as Python. |
| Speed | Python programs comply faster, and the speed of Python is much better than any other programming language. | R programming compiles slower than Python. |
| Community Support | Huge community support is available in Python for the users. | Excellent community support is also available for R users. |
Detailed explanation about difference 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 built-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 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 inspired by the Data Frame R Dataframe, the Sundersaute inspired the rvest package). 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
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 functions that are 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 these languages’ learning curve, then R is quite hard to learn for beginners. It requires lots of effort to start with R.
But once you start with it, you can polish your R programming skills with its developer community’s help. Apart from that, if you have a basic knowledge of programming, you may not find it very 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 be read by the data scientist that 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 needs. 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.
| Note: In R, the glm function is used for liner model handling. Moreover, the Logistic, Linear, or Poisson Regression obtained by using this function’s relevant family parameters. On the other hand, the sklearn library function is used for linear model handling. Moreover, the statsmodels library can support the linear regression models. |
R vs Python for data analysis
I have mentioned earlier, R is well suited for statistics analysis; therefore, it is also the best data analysis option. 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 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.
Blue: R programming language
Red: Python programming language
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 then 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 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 this, python is better than the R language.
Pandas vs DataFrames is probably a more significant comparison and one that matters.
In an experiment: The comparison of the execution times on exploratory effort when mirroring each part. The result was as follows:
When one writes a program and has several iterations that are less than 1000, Python would be the best in 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? We can say that if you have a finance team or are working in an accounting firm, a bank, or consulting, you 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 manager can prefer some of R vs Python’s criteria 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 the team not only on the goods but also helps to earn profit for the large companies.
So, whether to choose Python or R?
In general, it is beneficial to use Python if you:
- Look for developing desktop apps, video games, or web apps.
- Requirement to work with computations or deep learning that depend on GPUs.
- Prefer to write clean, short, and legible code.
On the other hand, it is beneficial to use R if you:
- Want to produce excellent data visualizations.
- Work with the exploratory analysis.
- Conduct analyses and inferential statistics, especially for academic settings.
Let’s Sum Up R vs Python
You might still think about should I learn R or Python? Well, it depends on you 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 a student of R programming language, then you can get the best r homework assignments from our experts.
Frequently Asked Questions
Is R easier than Python?
R is somehow harder to select, as it doesn’t support other programming languages’ normal conventions. At the same time, Python is quite simple, making it a good choice as a first programming language to learn for beginners.
Can Python replace R?
The answer to this question is yes—there are different tools (such as the feather package) that allow the user to exchange data among Python and R and combine the codes into a single project.
Is R still used?
This is important not to exceed the decline in the usages of R. Still; it has been seen that R is broadly used for statistical analysis and in data science. In a recent survey, it has been found that almost half of data scientists are still using R frequently.
