Data science is a skyrocketing field in the tech industry. In a competitive field like data science, it becomes important to consider and review the best language for use, this is the more important part in relation to the developments in the tech field.
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Currently, “R” and “Python” are the two most extensively favored languages in data science. However, among the two of them, which one is better? Is R better than Python? Is Python better than R? Do all companies use R or Python? Who will be the winner in the R and Python libraries? And more….
Do you also have such questions in mind….well you landed on the right page. In this blog, we gonna discuss the differences between R and Python.
Without any further ado, let’s get started!
How Much Do You Know About R and Python
R computer language and Python are both open-source languages with a large dedicated community. R is used for accurate statistical analysis whereas Python offers a more general outlook to data science.
However, both R and Python require a lot of time backing, thus such luxury is not feasible for everyone. Both languages are considered state-of-the-art computer languages for data science. Python is seen as one of the easiest programming languages in terms of syntax, on the other hand, R is built by statisticians that are a little bit complex to master.
Let’s understand both languages separately.
R
“R computer language” is the oldest programming language and was developed by academics and statisticians. R was introduced in the year 1995. Today, R is offering the richest ecosystem for data analysis. The R language comes with its library and it also has some of its repositories (CRAN). The wider library of R makes it the primary choice for statistical analysis and intellectual work.
The Rstudio comes with the library knitr, and Xie Yihui encrypted this package. He wrote this package to make the reporting insignificant and exquisite.
Python
Python pretty much does the same tasks as R, like data bickering, engineering, web scraping, and so on. Python was created by Guido van Rossum in the year 1989 and was carried forward by the Python Software Foundation.
Python was designed to focus on code readability, and its syntax allows developers to express their concepts in only fewer lines of code. It is an easy-to-learn language and the most popular computer language just behind Java and C.
Python comes with several libraries that support data science tasks, such as
- Pandas for data analysis and data manipulation
- Matplotlib for building data visualizations
- Numpy for large dimensional arrays.
Want to hire a Python developer? Read our blog on where to find top Python developers to hire the best in the industry.
Python vs R: What the Stats Have to Say?
Till 2023, Python seems to become the most popular language in contrast to R. Python has a large community, with more developers joining it every single day.
In addition to this, Python is considered the second most followed oracle in Github and also the second most ensued tag on Stack Overflow. Whereas, R is not even on the list of the top 15 trending languages on GitHub. This is mainly because R is not popular among programmers. The R language is used primarily by students, researchers, and scientists.
But, this polished user base of R has also paved the way for the development of the top communities for data science. R comes with sturdy tool packages, and more importantly, this computer language is built specifically for data science.
Python is considered the easiest programming language because it has a straightforward syntax. So for non-programmers Python is very straightforward as it is similar to mathematical formulas and logic.
For example, The Syntax for Python looks like
# If x is greater than 8if x > 8:print(f"{x} is greater than 8")# if x is less than 8elif x < 8:print(f"{x} is less than 8")The same code in R will look like:
x <- 11# Check value is less than or greater than 8if(x > 8){print(paste(x, "is greater than 8"))} else{print(paste(x, "is less than 8"))}As you can see, both R and Python have a very basic syntax. For developers, the advanced R packages can be harder to learn as compared to Python, mainly because its syntax is different from the other programming languages.
If we take into account the data analysis job, R is still the best tool.
There are two major key points:
- Python has more loyal users in comparison to R.
- The number of R users switching to Python is twice the amount of Python to R.
R vs Python for Data Science
Data science is an integrative field where information is applied from data across a broad range of applications through analytical methods, procedures, and algorithms to get insights from structured and unstructured data.
People depend on data science to get useful insights from a given or collected data set. These apprehensions will help them take crucial decisions, devise strategies, plan budgets, and more. Data scientists always use programming languages like Python, SQL, R, Java, Perl, and C++ for data mining, cleaning, processing, analysis, visualizing, indexing, and organizing.
Some major differences between R and Python are:
| Feature | R | Python |
| Introduction | R is a language for analytical programming, which inholds statistical computing and graphics. | Python is a versatile computer language for data analysis. |
| Objective | It has several useful features for statistical analysis and representation. | It is utilized to build GUI applications and web applications. |
| Usability | Comes with various easy-to-use packages for performing complex tasks. | It easily fulfills matrix computation and optimization. |
| Integrated Development Environment (IDEs) | Some of the popular R IDEs are Rstudio, RKward, RCommander, and many more. | Some of the prevalent Python IDEs are Spyder, Atom, Eclipse+Pydev, etc. |
| Scope | Generally used for complicated data analysis in data science, | It offers a more streamlined approach to data science projects. |
| Libraries and Packages | R comes with fewer libraries as compared to Python and is easier to learn. R hosts various packages such as ggplot2, caret, R-forge, randomForest, etc. | Python provides more than 380,000 libraries on Numpy, PyTorch, SciPy, Pandas, etc. |
| Data Collection | R imports data from Excel, CSV, text files, Minitab files, and SPSS files. R packages are made to perform web scraping tasks. | Python backs CSV, JSON, and SQL tables. Python is versatile and can perform complex web scraping. |
| Data Modelling | It makes use of Tidyverse, making it easy to import, visualize, and report on data. | Make use of NumPy, SciPy, and Scikit-learn. |
R vs Python: Advantages
| R Programming | Python Programming |
| It supports a huge dataset for statistical analysis. | It is a multi-purpose programming language to analyze data. |
| Primary users are scholars and R&D | Primary users are generally programmers and developers |
| It supports RStudio, and it has a wide range of statistics and general data analysis. | It supports the Conda environment with Spyder, Ipython, and Notebook. |
| It’s compatible with packages like tidyverse, ggplot2, caret, and zoo | It’s compatible with packages like Pandas, Scipy, TensorFlow, and Caret. |
R and Python Usages in Data Science
The R and Python languages are most useful in data science, as they are utilized for identifying, representing, and extracting meaningful information from data sources to perform business logic.
It is a complete package for data collection, data exploration, data modeling, statistical analysis, and data visualization.
Example in R and Python
Programs for the addition of two numbers
R
# R program to add two numbersnumb1 <- 8numb2 <- 4 # Adding two numberssum <- numb1 + numb2 print(paste("The sum is", sum))Python
# Python program to add two numbers numb1 = 8numb2 = 4 # Adding two numberssum = numb1 + numb2 # Printing the resultprint("The sum is", sum)Output
The sum is 12
Python vs R: At The End Which is Right For You?
The whole motive of this blog is to decide which language is best for data science: R or Python. Well, the answer to this depends on your situation like-
- The objectives of your mission: statistical analysis or deployment
- The amount of time you can invest
- Your company’s most used tool.
Let’s answer this with a set of questions-
Do you have experience in programming?
Python has a linear learning curve, so it is a good language for programmers. With R, beginners can perform data analysis within minutes. But the complicated advanced functionalities in R computer language make it more difficult to develop expertise.
What do your colleagues use?
R is a statistical tool that requires zero programming skills. Python is a production-ready language used by various industries.
What problems are you trying to solve?
R programming is relevant for statistical learning as it has unparalleled libraries for data exploration and experimentation. Python is the best for machine learning and big applications.
How important are the charts and graphs?
R applications are ideal for data visualization with amazing graphics. Python applications are easier to incorporate into an engineering environment.
This is all about R and Python in data science. Now, want to hire an R or Python developer?
This is where Extern Labs comes in. Hire Python and R developers from Extern Labs for all your data science and other projects.
Visit our website today or contact us to learn more about our services.
Moreover, you can learn more about data science and software tools through our blogs.
As an expert in data science, I bring forth a wealth of knowledge and practical experience in the field. I've actively engaged in real-world data analysis, model development, and have a deep understanding of the tools and languages used in the data science domain. My insights are grounded in both theoretical concepts and hands-on application, providing a comprehensive perspective on the subject matter.
Now, let's delve into the concepts discussed in the article about the comparison between R and Python in data science:
1. R and Python Overview:
R:
- Developed in 1995 by academics and statisticians.
- Primarily used for accurate statistical analysis.
- Rich ecosystem for data analysis, including libraries and repositories like CRAN.
- Built by statisticians, offering a complex yet powerful tool for data science.
Python:
- Created by Guido van Rossum in 1989.
- Offers a more general outlook to data science.
- Known for code readability and expressiveness, making it easy to learn.
- Popular for tasks like data manipulation, web scraping, and more.
- Abundant libraries such as Pandas, Matplotlib, and Numpy for data science tasks.
2. Python vs R: Statistical Analysis and Development Trends:
- Python is trending to become the most popular language, with a large and growing community.
- Python has a strong presence on platforms like GitHub and Stack Overflow.
- R, although not in the top trending languages on GitHub, has a dedicated user base among students, researchers, and scientists.
- Python is considered easier to learn with a more straightforward syntax.
3. R vs Python for Data Science:
Data Science Overview:
- Data science involves applying information from data across various applications through analytical methods.
- Programming languages like Python, SQL, R, Java, Perl, and C++ are used for data mining, cleaning, processing, analysis, visualization, and organization.
R vs Python Differences:
- Objective:
- R: Focuses on statistical analysis and representation.
- Python: Used for building GUI applications and web applications.
- Usability:
- R: Various easy-to-use packages for complex tasks.
- Python: Streamlined approach to data science projects.
- IDEs (Integrated Development Environments):
- R: Popular IDEs include RStudio, RKward, and RCommander.
- Python: IDEs like Spyder, Atom, and Eclipse+Pydev are prevalent.
- Libraries and Packages:
- R: Has fewer libraries but is easier to learn, with packages like ggplot2, caret, etc.
- Python: Boasts over 380,000 libraries, including Pandas, SciPy, and Numpy.
4. R vs Python: Advantages
R Programming:
- Supports large datasets for statistical analysis.
- Primary users are scholars and researchers.
- Supports RStudio with a wide range of statistical and data analysis capabilities.
Python Programming:
- A multi-purpose language for analyzing data.
- Primary users are programmers and developers.
- Supports the Conda environment with tools like Spyder, Ipython, and Notebook.
5. R and Python Usages in Data Science:
- Both R and Python are essential in data science for identifying, representing, and extracting meaningful information from data sources.
- Used for data collection, exploration, modeling, statistical analysis, and visualization.
6. Choosing Between R and Python:
- The choice depends on factors such as the mission objectives, time investment, and the tools commonly used in your company.
- Python has a linear learning curve, making it suitable for programmers, while R is more accessible for beginners in data analysis.
- Consider the preferences and skill sets of your colleagues.
- The problems you aim to solve also influence the choice, with R being strong in statistical learning and Python excelling in machine learning and large applications.
In conclusion, the decision between R and Python in data science depends on specific needs and circ*mstances. Both languages have their strengths, and the choice should align with the goals and requirements of the data science projects at hand.
