Python versus R

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life sciences data solutions

Which is better for Life Sciences Data Solutions?

If you’re looking to engineer data solutions for life sciences, you need to know about Python and R. In this blog, we cover the fundamentals of these open-source programming languages, while identifying the key features that set them apart – their strengths and weaknesses – to help you choose the best option for your life sciences data solution project. First, let’s look at the similarities of these languages. Python and R are both free to access for everyone and they’re both advancing life sciences data solutions through advanced such as artificial intelligence (AI) and machine learning (ML). Put simply, they are both very well-suited for providing life sciences data solutions, but in different ways. The key difference is that Python is more of a multi-purpose programming language, whereas R has its foundations in statistical analysis.

A brief introduction to Python

Python Python is an object-oriented language for general programming with an emphasis on code readability – the language uses lots of white space. Python was initially released in 1989 and today it’s just behind Java and C as one of the world’s most popular programming languages. Largely due to the fact it’s easy to learn, Python is championed by many programmers and developers. Python is ideally suited to the deployment of ML for big data. It comes with a suite of specialist deep learning and ML capabilities that allow data scientists to create sophisticated data models that can be plugged directly into a life sciences data analytics production system.

A brief introduction to R

Python versus R Released in 1992, this programming language is ideal for all kinds of statistical analysis and data visualizations. R is part of a rich ecosystem that includes a wealth of complex and powerful data models as well as refined reporting tools. R is the go-to programming language for many data science scholars and researchers. It has a vast array of libraries and tools that effectively enable the cleaning and preparation of data and the creation of rich visualizations. R is also used to train and evaluate ML and deep learning algorithms. R is often used within RStudio – Posit, an integrated development environment (also known as an IDE) that delivers simplified statistical analysis, visualization, and reporting for life sciences organizations. R packages can be used directly or in an interactive way online via Shiny, which provides a powerful framework for building web apps using R.

Primary differences when used for Life Sciences Data Solutions

life sciences data solutions   In terms of life sciences data solutions, the main distinction between the two languages is:

  • R is mostly used for statistical analysis
  • Python offers a more general approach to data wrangling (the process of transforming raw data to make It usable for life sciences business intelligence (BI) and analytics purposes)

Python is a multi-purpose language

data solutions for life sciences   In a similar way to C++ and Java, Python has a readable syntax that is generally considered easier to learn. With Python, programmers can carry out data analytics or use ML in scalable production environments. For instance, you could use Python to build face recognition into your API. You could also use it to develop an ML application.

R focuses on statistical models and specialized analytics

life sciences data solutions   R is built by statisticians and data scientists use it for deep statistical analysis, which is supported by just a couple of lines of code and compelling data visualizations. For instance, you could use R to carry out customer behavior analysis, and research into genomics.

Best of both worlds

data solutions for life sciences   Many life sciences organizations have realized that both R and Python are integral to their data solution. Therefore, the question for your business may not be “Which one should I choose?” Rather, you should be asking “How can I make the most of both programming languages for my specific use cases?” There are now many tools, like Microsoft Machine Learning Server, that support both R and Python. That’s why it makes sense for many organizations to use a combination of both languages. For instance, you may conduct early-stage data analysis and exploration in R and then switch to Python when it’s time to ship your data products.

Speak to our trusted Python and R experts

ProCogia is a data science company with a wealth of Python and R expertise. Find out how our team of programmers can deliver life sciences data solutions to accelerate your data journey.


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