R vs. Python: Two Programming Languages Compared
Data scientists often use both R and Python in their work. Both languages are well suited to the tasks that come up in data science, such as data cleaning, and come with extensions that make them even more powerful.
If you are becoming a data scientist, or considering a career in data science, you may be wondering whether you should learn R or Python. In this guide, we are going to address this issue. We will compare R and Python and discuss how both are used in data science.
What Is R?
R is a programming language used for statistical computing. Based on the S language, R provides the features one needs to run a statistical analysis on a dataset. You can also create aesthetically pleasing and functional visualizations to demonstrate the insights you have found in a particular dataset.
While R by itself is powerful, the language comes with a range of packages to help you make the most out of it. For instance, the ggplot2 library lets you create more advanced visualizations. You can use dplyr to create data frames. Or you can work with TensorFlow to build machine learning algorithms.
What Is Python?
Developed by Guido van Rossum, Python is a general-purpose programming language. It is commonly used to create the backend architecture behind websites, analyze data sets, visualize data, and build software applications.
Python has developed a reputation for being easy-to-use, perhaps as a result of its syntax. People describe the Python syntax as being similar to English because it is so readable. This makes Python a great first language to learn.
Like R, Python really shines with the help of libraries. NumPy, for instance, lets you easily create multi-dimensional arrays. Matplotlib lets you create visualizations such as bar charts and scatter plots from a specified data set.
Comparing R and Python
Now that we have covered the basics of R and Python, we are going to compare the languages. To do so, we are going to focus on four areas: what makes each library shine, popularity and job prospects, speed, and the learning curve for each language.
The Benefits of Python and R
The R programming language was built specifically for data analysis and statistical computing. This means that the developers thought through the problems data scientists are likely to encounter and considered them when creating the language.
R has a massive library of data science extensions that help with machine learning, data visualization, retrieving data, and more. Python has a similar library of packages. However, because Python is a general-purpose language, there’s more variety when it comes to the packages and their uses.
Another benefit of R is how easy it is to showcase your findings. You can easily create data visualizations with R. Python data science libraries also allow you to create visualizations easily. There are packages in both R and Python that let you create documents to show your findings. RMarkdown creates a markdown output from your R program. Jupyter Notebook, built on Python, lets you run code and see the output in a “notebook document.”
Because R was built for data science, you will rarely see the language used for other purposes. Python, on the other hand, is not limited to the world of data science. If you learn Python, you can use the language for a range of purposes.
Popularity and Job Prospects
Both R and Python are incredibly popular programming languages. According to the TIOBE Index, which tracks the popularity of various programming languages, Python was the third most popular programming language in February 2021. R was the 11th most popular language.
In terms of job prospects, it is safe to say that both R and Python will help you find a well-paying position. R is more suited toward data science jobs such as data scientist, data analyst, machine learning engineer, and artificial intelligence engineer. Python is also a great skill to know for data science. However, Python can be applied to other careers in technology as well.
Jobs that demand knowledge of either Python or R are in high demand. We looked at Glassdoor to find how many jobs there are in data science fields. These numbers will help you gauge the state of the data science job market:
- Data scientist: 30,355 open jobs
- Data analyst: 7,151 open jobs
- Data engineer: 98,469 open jobs
- Machine learning engineer: 19,488 open jobs
- Artificial intelligence engineer: 4,524 open jobs
Indeed, jobs in data science are in high demand. The above stats do not consider whether R or Python was listed in the job description. However, they do show the general growth of the jobs where R and Python are commonly required as skills.
In everyday coding, you may not think speed matters. But if you are dealing with large data sets and need to conduct complicated analyses, every second you can save will make a difference.
Python is a fast programming language, whereas R is much slower. Python was designed to be intuitive and friendly for users. R, on the other hand, does not focus so much on performance. With that in mind, R may be a bit slower than you would like it to be but it is by no means “too” slow.
Which language is easier to learn: Python or R? That’s a good question. Arguably, Python is the easier language to learn, with a syntax that resembles English and all of the beginner-friendly documentation available on the language. R, on the other hand, is known for having a steeper learning curve.
Learning R will probably take you more time than learning Python, especially if R is the first programming language you have ever used. It may be worth considering whether learning Python first is a better investment of your time.
This course of action will let you grow familiar with programming before you face R. In fact, knowing any programming language before you learn R is a bonus. You can absolutely learn R without any programming experience, but it will arguably be more difficult than if you already know another language.
Knowing another language means you have practiced coding. As a result, you are probably able to translate some concepts from the language you have learned over to R.
Should I Learn R or Python?
Both R and Python are popular skills. They come up often in job adverts for data positions, such as data scientist and machine learning engineer. R was built specifically for data science whereas Python has a more general purpose, meaning you can use Python for different applications (e.g. game development, web development, or scripting).
Whether you learn R, Python, or both is completely up to you. While there are many data science jobs that require knowledge of R, the same can be said for Python. With knowledge of one or the other, you should have no trouble finding jobs that match your skills.
Like with any programming language, learning R or Python does not happen overnight. But if you want to work with data, you will need to put in the time and energy to learn either of these languages. To help you choose a language, consider these questions:
- Do you have any programming experience? (If so, you may feel more comfortable going straight into R).
- Are you ready to commit to learning a programming language and do you have the time required?
- What programming language is likely to help you get to where you want to be? (Python has a more general purpose while R focuses mainly on data science).
Good luck on your learning journey!