Python and R are two popular programming languages for data analysis. R is an open-source programming language used for statistical computing and data visualization.
Python is another open-source programming language with data analysis capabilities like data manipulation, visualization, and statistical analysis.
Choosing the right data science tools is vital and a programming language is no exception. It’s the core of data analysis and manipulation, machine learning, and data visualization.
Making a good choice for a programming language ensures more efficiency and enjoyability whereas the wrong choice increases the risk of inefficiency and frustration.
If you want to maximize the outcome of your data science project, keep reading to learn all about Python vs. R in our Python and R comparison.
R is a programming language used by data analysts and research scientists because it enables the importation, processing, and analysis of data. Those interested in statistical calculation and data visualization benefit the most from R.
Here are some of the main reasons why a data analyst may decide to choose R as their go-to programming language:
R was designed with statistics in mind. It performs optimally in the analysis of statistics and offers an array of packages to perform statistical modeling.
With R, you get awesome data visualization abilities including libraries like ggplot2 that ensure quality customization and graphs.
R provides specialized packages for different domains. So, it’s highly recommendable for data science for specific industries like finance or healthcare.
With R’s variety of packages, data analysts can transform messy data into structured formats.
R offers useful machine-learning operations like classification and regression. It also provides packages and features for the development of artificial neural networks.
Every programming language has its downsides. Here are some of the potential reasons why a data analyst may not choose R for their project:
Certain more advanced R libraries have a steep learning curve so it may not be as beginner-friendly as some other options.
The lack of basic security measures may not be suitable if you’re looking for premium options. You can’t embed it into web browsers and use it for web-safe apps.
R doesn’t have strong memory management. The data is usually stored in the physical memory instead of in the cloud.
R is a slower programming language than Python.
Many data analysts prefer Python because it has a straightforward syntax and the ability to perform complex tasks easily and handle large batches of data.
It’s the most beneficial option for data scientists. In addition to using it for data science, you can also use it for AI and deep learning algorithms.
Here are some of the major advantages of Python for data science that make it an indispensable tool:
Python offers web scraping libraries for different scenarios. Requests is an elegant and simple library used for sending HTTP requests and accessing the data in different formats, which makes it great for basic web scraping.
Scrapy is a scalable framework for crawling and scraping websites and it’s suitable for complex and large-scale data projects.
The Python community is very active and you can find an abundance of online tutorials, forums, and courses. The strong support system eases the learning and troubleshooting processes.
The syntax of Python is not complex and its readability makes it a great choice for beginners.
Python is easily combined with different databases, big data tools like Spark, and development frameworks.
Although Python is the second most in-demand programming language, it’s in no way ideal for every data science project. Here are some reasons why a data analyst may opt out of Python:
As an interpreted programming language, Python isn’t as fast as some compiled languages. Therefore, it may not be suitable for certain tasks.
Despite being considered as some of the programming languages that are relatively easy to learn, some of its advanced libraries, for example, PyTorch for machine learning, have a steeper learning curve.
Python may not be a suitable choice for memory-intensive tasks. Its memory consumption is also high because of the flexibility of the data types.
Despite its strength for desktop and server platforms, Python doesn’t excel in mobile development so it’s rarely used for it. You can find only a few mobile apps built into it like Carbonelle.
Before you make the final call on a programming language for your data science project, weigh the pros and cons of each data science tool and consider the important factors and the goals of your project.
Python is a flexible choice for data analysts and useful for the integration and analysis of data. It offers excellent libraries for machine learning and AI like PyTorch and TensorFlow.
R is beneficial for researchers and statisticians who need to perform statistical analysis and data visualization using ggplot2 or dplvr.
If you’re still having doubts about the ultimate programming language for your data science project, consult our experts at ArtHaus to find out more. We offer within-budget effective IT solutions to clients and assist them in gaining a significant advantage over their competitors.
